An Optimized P. berghei Liver Stage-HepG2 Infection Model for Simultaneous Quantitative Bioimaging of Host and Parasite Nascent Proteomes

The Plasmodium parasites that cause malaria undergo an obligate, asymptomatic developmental stage in the host liver before initiating the symptomatic blood-stage infection. The parasite liver stage is a key intervention point for antimalarial chemoprophylaxis: successful targeting of liver-stage parasites prevents disease development in individuals and can help to reduce parasite transmission in populations, as the gametocyte forms that transmit infection to mosquitos are exclusively found in the blood stage. Antimalarial drugs that can target multiple parasite stages are thus highly desirable, and one emerging cellular target for such multistage active compounds is the process of protein synthesis or translation. Quantitative study of liver stage translation, and thus mechanistic evaluation of translation inhibitors against liver stage parasites, is not amenable to the methods allowing quantification of asexual blood stage translation, such as radiolabeled amino acid incorporation or lysate-based translation of reporter transcripts. Here, we present a method using o-propargyl puromycin (OPP) labeling of host and parasite nascent proteomes in the P. berghei-HepG2 infection model, followed by automated confocal image acquisition and computational separation of P. berghei vs. H. sapiens nascent proteome signals to allow simultaneous readout of the effects of translation inhibitors on both host and parasite. This protocol details our HepG2 cell culture and infected monolayer handling optimized for microscopy, our OPP labeling workflow, and our approach to automated confocal imaging, image processing, and data analysis. Key features • Uses the o-propargyl puromycin labeling technique developed by Liu et al. to quantitatively analyze protein synthesis in Plasmodium berghei liver-stage parasites in actively translating hepatoma cells. • This quantitative approach should be adaptable for other puromycin-sensitive intracellular pathogens residing in actively translating host cells. • The P. berghei-infected HepG2 recovery and reseeding protocol presented here is of use in applications beyond nascent proteome labeling and quantification.

MAIP: a web service for predicting blood-stage malaria inhibitors

Malaria is a disease affecting hundreds of millions of people across the world, mainly in developing countries and especially in sub-Saharan Africa. It is the cause of hundreds of thousands of deaths each year and there is an ever-present need to identify and develop effective new therapies to tackle the disease and overcome increasing drug resistance. Here, we extend a previous study in which a number of partners collaborated to develop a consensus in silico model that can be used to identify novel molecules that may have antimalarial properties. The performance of machine learning methods generally improves with the number of data points available for training. One practical challenge in building large training sets is that the data are often proprietary and cannot be straightforwardly integrated. Here, this was addressed by sharing QSAR models, each built on a private data set. We describe the development of an open-source software platform for creating such models, a comprehensive evaluation of methods to create a single consensus model and a web platform called MAIP available at https://www.ebi.ac.uk/chembl/maip/ . MAIP is freely available for the wider community to make large-scale predictions of potential malaria inhibiting compounds. This project also highlights some of the practical challenges in reproducing published computational methods and the opportunities that open-source software can offer to the community.