Dataset Information

Immunogenomic profile at baseline predicts host susceptibility to clinical malaria

ABSTRACT: Host gene and protein expression impact susceptibility to clinical malaria, but the balance of immune cell populations, cytokines and genes that contributes to protection, remains incompletely understood. To identify determinants of host susceptibility to clinical malaria at a time when acquired immunity is developing, we analyzed peripheral blood mononuclear cells (PBMCs) collected from children who differed in susceptibility to clinical malaria, all from a small town in Mali. PBMCs were collected from children aged 4-6 years at the start, peak and end of the malaria season. We characterized the immune cell composition and cytokine secretion for a subset of 20 children per timepoint (10 children with no symptomatic malaria age-matched to 10 children with >2 symptomatic malarial illnesses), and gene expression patterns for six children (three per cohort) per timepoint. We noted higher frequency of HLA-DR+ CD4 T cells in protected children during the peak of the malaria season and comparable levels cytokine secretion after stimulation with malaria schizonts across all three time points. We also observed differences between the two groups of children in the expression of genes related to cell death and inflammation; in particular, inflammatory genes such as CXCL10 and STAT1 and apoptotic genes such as XAF1 were upregulated in susceptible children before the transmission season began. This suggests that differences in apoptotic and inflammatory gene expression patterns can predict susceptibility to clinical malaria.

ORGANISM(S): Homo sapiens

PROVIDER: GSE234970 | GEO | 2023/06/29

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

Project description:We aimed at finding differently expressed genes in whole blood cells of African children with asymptomatic Plasmodium falciparum infection (A), uncomplicated malaria (U), severe malarial anemia (A) and cerebral malaria (Ce) compared one to another and to healthy children (Co). Understanding malarial immunopathology in the human host represents and enormous challenge for transcriptomic research. In this work, we used microarray and real-time RT-PCR technology to pursue deeper knowledge about the mechanisms underlying this disease in African children. To this end, we investigated the genomic transcriptional profiles in whole blood of healthy children and children with asymptomatic infection, uncomplicated malaria, malaria associated with severe anemia and cerebral malaria and compared them with previously published microarray results. We were able to discriminate between the different presentations of P. falciparum infection using supervised and unsupervised clustering of microarray data and unsupervised double-hierarchical clustering of real-time RT-PCR results of a set of 22 genes known to be expressed in at least one of the principal blood cell lineages. We further found considerable overlap between genes regulated in Kenyan and Gabonese children with symptomatic malaria, in contrast to adults with acute malaria from Cameroon. Different signatures for transcription factor binding sites in promoters of genes either up-regulated in symptomatic disease, specifically up-regulated in uncomplicated malaria or specifically down-regulated in cerebral malaria point out that similar gene expression in each of these clinical presentations is probably a result of common regulation at the transcriptional level. Immunoglobulin production, complement regulation and IFN beta signalling emerged as most discrepant features between uncomplicated malaria and all other investigated presentations, correlating with IRF7 and ISRE binding signatures in the corresponding genes. Down-regulation of several genes in cerebral malaria seems instead to be a response to hypoxia orchestrated by AhRF, GABP and HIF1 transcription factors. ARG1, BPI, CD163, IFI27, HP and TNFAIP6 transcript levels correlated positively with lactatemia and inversely with hemoglobin concentration and should be evaluated as prognostic markers to direct early therapeutic measures and prevent malarial disease evolution and death.

Dataset Information

Immunogenomic profile at baseline predicts host susceptibility to clinical malaria

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure