Systems immunology approach to mapping malaria disease trajectory identifies conserved gene modules associated with severity 2255965

Abstract Introduction Malaria manifests in a spectrum of clinical outcomes shaped by the complex heterogeneity of both the human host and the Plasmodium parasite, its causative agent. This dynamic and multifactorial host—parasite interaction poses major challenges for effective diagnostics, leading to fragmented observations that hinder the generalizability of findings, and consequently, clinical translation. We hypothesized that a conserved immune response underlies malaria disease trajectories and diverges across patient outcomes. Methods We conducted the largest multicohort analysis of the host transcriptional response to malaria, integrating >3,600 blood transcriptomes from 42 cohorts across 20 countries on six continents spanning children and adults with diverse clinical presentations. Using TIDEPOOL (Trajectory Inference for Disease Endotyping from Pooled data), we applied trajectory modeling to resolve gene modules associated with protective versus detrimental responses, delineating the transcriptional continuum from asymptomatic to severe malaria. Results We identified a robust 208-gene blood signature that distinguishes malaria-infected individuals from uninfected controls (AUROC = 0.875, validation cohort), providing a generalizable framework for molecular diagnosis. TIDEPOOL found that severe malaria was defined by a blood transcriptional program marked by expansion of granulocytes, neutrophils, and monocytes, coupled with depletion of eosinophils, NK cells, B cells, and T cells–reflecting broad immune dysregulation. Within severe cases, we resolved a novel conserved gene signature diagnostic of cerebral malaria. These signatures were consistent across age groups, geographic regions, and parasite strains, underscoring universal host responses to Plasmodium infection. Conclusion Together, our findings reveal a unified framework linking immune dynamics to clinical severity and offer actionable biomarkers to guide next-generation diagnostic and host-targeted therapeutic strategies for malaria elimination. Funding Source Gate Foundation, NIAID Topic Categories Computational and Systems Immunology (COMP)