The malarial blood transcriptome: translational applications.

The blood transcriptome of malaria patients has been used extensively to elucidate the pathophysiological mechanisms and host immune responses to disease, identify candidate diagnostic and prognostic biomarkers, and reveal new therapeutic targets for drug discovery. This review gives a high-level overview of the three main translational applications of these studies (diagnostics, prognostics, and therapeutics) by summarising recent literature and outlining the main limitations and future directions of each application. It highlights the need for consistent and accurate definitions of disease states and subject groups and discusses how prognostic studies must distinguish clearly between analyses that attempt to predict future disease states and those which attempt to discriminate between current disease states (classification). Lastly it examines how many promising therapeutics fail due to the choice of imperfect animal models for pre-clinical testing and lack of appropriate validation studies in humans, and how future transcriptional studies may be utilised to overcome some of these limitations.

Comparative transcriptomic analysis reveals translationally relevant processes in mouse models of malaria.

Recent initiatives to improve translation of findings from animal models to human disease have focussed on reproducibility but quantifying the relevance of animal models remains a challenge. Here, we use comparative transcriptomics of blood to evaluate the systemic host response and its concordance between humans with different clinical manifestations of malaria and five commonly used mouse models. Plasmodium yoelii 17XL infection of mice most closely reproduces the profile of gene expression changes seen in the major human severe malaria syndromes, accompanied by high parasite biomass, severe anemia, hyperlactatemia, and cerebral microvascular pathology. However, there is also considerable discordance of changes in gene expression between the different host species and across all models, indicating that the relevance of biological mechanisms of interest in each model should be assessed before conducting experiments. These data will aid the selection of appropriate models for translational malaria research, and the approach is generalizable to other disease models.

Predictors of outcome in childhood Plasmodium falciparum malaria.

Plasmodium falciparum malaria is classified as either uncomplicated or severe, determining clinical management and providing a framework for understanding pathogenesis. Severe malaria in children is defined by the presence of one or more features associated with adverse outcome, but there is wide variation in the predictive value of these features. Here we review the evidence for the usefulness of these features, alone and in combination, to predict death and other adverse outcomes, and we consider the role that molecular biomarkers may play in augmenting this prediction. We also examine whether a more personalized approach to predicting outcome for specific presenting syndromes of severe malaria, particularly cerebral malaria, has the potential to be more accurate. We note a general need for better external validation in studies of outcome predictors and for the demonstration that predictors can be used to guide clinical management in a way that improves survival and long-term health.