A comparative study of Eimeria organelle proteins using spatial proteomics and machine learning

Abstract

Coccidiosis, an enteric disease caused by Eimeria parasites, is a major threat to food security in poultry production, where it costs ~3 billion USD per year. The three most economically significant species are Eimeria tenella, Eimeria acervulina and Eimeria maxima. A group of secretory organelles including the micronemes and rhoptries drive the invasion and intracellular survival of these parasites. However, annotation and functional understanding of the proteins within these organelles is limited. This project aims to comparatively characterise these organelle proteins using machine learning assisted quantitative spatial proteomic approaches, followed by wet-lab validations to identify new targets for disease intervention.

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