Background: Transcriptomics has been used to evaluate immune responses during malaria in diverse cohorts worldwide. However, the high heterogeneity of cohorts and poor generalization of transcriptional signatures reported in each study limit their potential clinical applications.
Methods: We compiled 28 public data sets containing 1556 whole-blood or peripheral blood mononuclear cell transcriptome samples. We estimated effect sizes with Hedge's g value and the DerSimonian-Laird random-effects model for meta-analyses of uncomplicated malaria. Random forest models identified gene signatures that discriminate malaria from bacterial infections or malaria severity. Parasitological, hematological, immunological, and metabolomics data were used for validation.
Results: We identified 3 gene signatures: the uncomplicated Malaria Meta-Signature, which discriminates Plasmodium falciparum malaria from uninfected controls; the Malaria or Bacteria Signature, which distinguishes malaria from sepsis and enteric fever; and the cerebral Malaria Meta-Signature, which characterizes individuals with cerebral malaria. These signatures correlate with clinical hallmark features of malaria. Blood transcription modules indicate immune regulation by glucocorticoids, whereas cell development and adhesion are associated with cerebral malaria.
Conclusions: Transcriptional meta-signatures reflecting immune cell responses provide potential biomarkers for translational innovation and suggest critical roles for metabolic regulators of inflammation during malaria.
Keywords: bacterial infection; biomarker; cerebral malaria; gene expression; immune response.
© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.