Generation of a genetically double-attenuated Plasmodium berghei parasite that fully arrests growth during late liver stage development.

Malaria caused by Plasmodium parasites remains a large health burden. One approach to combat this disease involves vaccinating individuals with whole sporozoites that have been genetically modified to arrest their development at a specific stage in the liver by targeted gene deletion, resulting in a genetically attenuated parasite (GAP). Through a comprehensive phenotyping screen, we identified the hscb gene, encoding a putative iron-sulfur protein assembly chaperone, as crucial for liver stage development, making it a suitable candidate gene for GAP generation.

Probing novel epitopes on the Plasmodium falciparum circumsporozoite protein for vaccine development.

RTS,S and R21 are the only vaccines recommended by the WHO to protect children from Plasmodium falciparum (Pf) clinical malaria. Both vaccines target the Pf sporozoite surface protein circumsporozoite protein (CSP). Recent studies showed that human antibodies neutralize Pf sporozoites most efficiently when simultaneously binding to the PfCSP NANP repeat and the NPDP junction domain.

Host cell CRISPR genomics and modelling reveal shared metabolic vulnerabilities in the intracellular development of Plasmodium falciparum and related hemoparasites.

Parasitic diseases, particularly malaria (caused by Plasmodium falciparum) and theileriosis (caused by Theileria spp.), profoundly impact global health and the socioeconomic well-being of lower-income countries. Despite recent advances, identifying host metabolic proteins essential for these auxotrophic pathogens remains challenging.

Machine learning for predicting Plasmodium liver stage development in vitro using microscopy imaging.

Malaria, a significant global health challenge, is caused by parasites. The liver stage plays a pivotal role in the establishment of the infection. This study focuses on the liver stage development of the model organism Plasmodium berghei, employing fluorescent microscopy imaging and convolutional neural networks (CNNs) for analysis.