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Nankabirwa, J. I.

Publications and source records attributed to Nankabirwa, J. I..

2 recordsLinked to original sources

CXCR6⁺ CD127- Tr1 Cells Balance Immunity and Persistence in Plasmodium falciparum Infection

Plasmodium falciparum (Pf) induces the clonal expansion of antigen-specific type 1 regulatory T (Tr1) cells with a capacity for long-term memory. Tr1 cells comprise nearly 90% of the Pf blood stage antigen-specific CD4+ T cell pool in children. Though, whether Tr1 cells contribute to protection from malaria remains undetermined. To address this critical gap in knowledge, we first performed scRNAseq on gated cell populations to validate CXCR6+ CD127- as new phenotypic markers to enrich for bona-fide Tr1 cells. Importantly, these Tr1 cells potently suppressed the proliferation of other CD4+ T cells in vitro via IL-10 secretion. Among children living in malaria-endemic Uganda, CXCR6+ CD127- Tr1 cells were the dominant responding subset to Pf-infected red blood cell stimulation in vitro. They also rapidly expanded following malaria and expressed IL-10 and IFNg during infection in vivo. Tr1 abundance correlated with plasma concentrations of granzyme A, IFNg, IL-10, and LAG3, suggesting that these cells act systemically. Higher CXCR6+ CD127- Tr1 cell frequencies correlated with a lower probability of symptoms given parasitemia but were also associated with delayed parasite clearance among untreated, asymptomatic children. These data suggest that Tr1 cells help mediate clinical immunity to malaria but may also facilitate parasite persistence through mechanisms of immune regulation.

immunology↗

Evaluating the performance of malaria genomics for inferring changes in transmission intensity using transmission modelling

Advances in genetic sequencing and accompanying methodological approaches have resulted in pathogen genetics being used in the control of infectious diseases. To utilise these methodologies for malaria we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment. Here we develop an individual-based transmission model to simulate malaria parasite genetics parameterised using estimated relationships between complexity of infection and age from 5 regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterise the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The best performing model successfully predicted malaria prevalence with mean absolute error of 0.055, suggesting genetic tools could be used for monitoring the impact of malaria interventions.

genetics↗