Immunogenomic profile at baseline predicts host susceptibility to clinical malaria

Clinical immunity to malaria involves epigenetic reprogramming of innate immune cells

The regulation of inflammation is a critical aspect of disease tolerance and naturally acquired clinical immunity to malaria. Here, we demonstrate using RNA sequencing and epigenetic landscape profiling by cytometry by time-of-flight, that the regulation of inflammatory pathways during asymptomatic parasitemia occurs downstream of pathogen sensing-at the epigenetic level. The abundance of certain epigenetic markers (methylation of H3K27 and dimethylation of arginine residues) and decreased prevalence of histone variant H3.3 correlated with suppressed cytokine responses among monocytes of Ugandan children. Such an epigenetic signature was observed across diverse immune cell populations and not only characterized active asymptomatic parasitemia but also correlated with future long-term disease tolerance and clinical immunity when observed in uninfected children. Pseudotime analyses revealed a potential trajectory of epigenetic change that correlated with a child's age and recent parasite exposure and paralleled the acquisition of clinical immunity. Thus, our data support a model whereby exposure to Plasmodium falciparum induces epigenetic changes that regulate excessive inflammation and contribute to naturally acquire clinical immunity to malaria.

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.