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

PLoS Biology
Michael J BoucherEllen Yeh

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...Continue Reading

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Oct 4, 2019·The New Phytologist·Anna M G Novák VanclováVladimír Hampl
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Sep 30, 2021·Molecular Microbiology·Jessica KimmelTobias Spielmann

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Methods Mentioned

BETA
proteomic profiling
RNA-Seq
fluorescence microscopy
PCR
transfection

Software Mentioned

Fiji
PlasmoGEM
PlastNN
caret
Tensorflow
PlasmoDB
OrthoMCL
SignalP
Perl
DB

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