Identification and validation of a novel panel of Plasmodium knowlesi biomarkers of serological exposure
journal contributionposted on 12.08.2020 by Lou S Herman, Kimberly Fornace, Jody Phelan, Matthew J Grigg, Nicholas M Anstey, Timothy William, Robert W Moon, Michael J Blackman, Chris J Drakeley, Kevin KA Tetteh
Any type of content formally published in an academic journal, usually following a peer-review process.
BACKGROUND: Plasmodium knowlesi is the most common cause of malaria in Malaysian Borneo, with reporting limited to clinical cases presenting to health facilities and scarce data on the true extent of transmission. Serological estimations of transmission have been used with other malaria species to garner information about epidemiological patterns. However, there are a distinct lack of suitable serosurveillance tools for this neglected disease. METHODOLOGY/PRINCIPAL FINDINGS: Using in silico tools, we designed and expressed four novel P. knowlesi protein products to address the distinct lack of suitable serosurveillance tools: PkSERA3 antigens 1 and 2, PkSSP2/TRAP and PkTSERA2 antigen 1. Antibody prevalence to these antigens was determined by ELISA for three time-points post-treatment from a hospital-based clinical treatment trial in Sabah, East Malaysia (n = 97 individuals; 241 total samples for all time points). Higher responses were observed for the PkSERA3 antigen 2 (67%, 65/97) across all time-points (day 0: 36.9% 34/92; day 7: 63.8% 46/72; day 28: 58.4% 45/77) with significant differences between the clinical cases and controls (n = 55, mean plus 3 SD) (day 0 p<0.0001; day 7 p<0.0001; day 28 p<0.0001). Using boosted regression trees, we developed models to classify P. knowlesi exposure (cross-validated AUC 88.9%; IQR 86.1-91.3%) and identified the most predictive antibody responses. CONCLUSIONS/SIGNIFICANCE: The PkSERA3 antigen 2 had the highest relative variable importance in all models. Further validation of these antigens is underway to determine the specificity of these tools in the context of multi-species infections at the population level.