Integrative proteomics and bioinformatic prediction enable a high-confidence apicoplast proteome in malaria parasites

by Michael J. Boucher, Sreejoyee Ghosh, Lichao Zhang, Avantika Lal, Se Won Jang, An Ju, Shuying Zhang, Xinzi Wang, Stuart A. Ralph, James Zou, Joshua E. Elias, Ellen Yeh Malaria parasites (Plasmodium spp.) and related apicomplexan pathogens contain a nonphotosynthetic plastid called the apicoplast. Derived from an unusual secondary eukaryote –eukaryote endosymbiosis, the apicoplast is a fascinating organelle whose function and biogenesis rely on a complex amalgamation of bacterial and algal pathways. Because these pathways are distinct from the human host, the apicoplast is an excellent source of novel antimalarial targets. Despite it s biomedical importance and evolutionary significance, the absence of a reliable apicoplast proteome has limited most studies to the handful of pathways identified by homology to bacteria or primary chloroplasts, precluding our ability to study the most novel apicoplast pathways. Here, we combine pr oximity biotinylation-based proteomics (BioID) and a new machine learning algorithm to generate a high-confidence apicoplast proteome consisting of 346 proteins. Critically, the high accuracy of this proteome significantly outperforms previous prediction-based methods and extends beyond other BioID studies of unique parasite compartments. Half of identified proteins have unknown function, and 77% are predicted to be important for normal blood-stage growth. We validate the apicoplast localization of a subset of novel proteins and show that an ATP-b...
Source: PLoS Biology: Archived Table of Contents - Category: Biology Authors: Source Type: research