Immune signature against Plasmodium falciparum antigens predicts clinical immunity in distinct malaria endemic communities.

In this study, using samples collected from young children in Ghana at multiple time points during a longitudinal study, we adapted a predictive modelling framework which combines feature selection and machine learning techniques to identify an antigen signature of clinical immunity to malaria. Our results show that an individual's immune status can be accurately predicted by measuring antibody responses to a small defined set of 15 target antigens. We further demonstrate that the identified immune signature is highly versatile and capable of providing precise and accurate estimates of clinical protection from malaria in an independent geographic community. Our findings pave the way for the development of a robust point-of-care test to identify individuals at high risk of disease and which could be applied to monitor the impact of vaccinations and other interventions. This approach could be also translated to biomarker discovery for other infectious diseases. PMID: 31658979 [PubMed - as supplied by publisher]
Source: Molecular and Cellular Proteomics : MCP - Category: Molecular Biology Authors: Tags: Mol Cell Proteomics Source Type: research