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PLoS Biol. 2018 Sep 13;16(9):e2005895. doi: 10.1371/journal.pbio.2005895. eCollection 2018 Sep.

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

Author information

1
Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, United States of America.
2
Department of Biochemistry, Stanford University School of Medicine, Stanford, California, United States of America.
3
Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California, United States of America.
4
Department of Pathology, Stanford University School of Medicine, Stanford, California, United States of America.
5
Department of Computer Science, Stanford University, Stanford, California, United States of America.
6
Department of Bioengineering, Stanford University, Stanford, California, United States of America.
7
Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, Vic, Australia.
8
Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, United States of America.
9
Chan Zuckerberg Biohub, San Francisco, California, United States of America.

Abstract

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 its 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 proximity 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-binding cassette protein ABCF1 is essential for blood-stage survival and plays a previously unknown role in apicoplast biogenesis. These findings indicate critical organellar functions for newly discovered apicoplast proteins. The apicoplast proteome will be an important resource for elucidating unique pathways derived from secondary endosymbiosis and prioritizing antimalarial drug targets.

PMID:
30212465
PMCID:
PMC6155542
DOI:
10.1371/journal.pbio.2005895
[Indexed for MEDLINE]
Free PMC Article

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