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Updated: Jul 4, 2026

QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii
Published on: June 22, 2017
Expression quantitative trait locus mapping of toxoplasma genes reveals multiple mechanisms for strain-specific
Jon P Boyle1, Jeroen P J Saeij, Scott Y Harada
1Department of Microbiology and Immunology, Stanford University School of Medicine, Fairchild Building, Room D305, 299 Campus Drive, Stanford, CA 94305, USA. john.boothroyd@stanford.edu
Abstract:
Toxoplasma gondii is an intracellular parasite with a significant impact on human health, especially in cases where individuals are immunocompromised (e.g., due to human immunodeficiency virus/AIDS). In Europe and North America, only a few clonal genotypes appear to be responsible for the vast majority of Toxoplasma infections, and these clonotypes have been intensely studied to identify strain-specific phenotypes that may play a role in the manifestation of more-severe disease. To identify and genetically map strain-specific differences in gene expression, we have carried out expression quantitative trait locus analysis on Toxoplasma gene expression phenotypes by using spotted cDNA microarrays. This led to the identification of 16 Toxoplasma genes that had significant and mappable strain-specific variation in hybridization intensity. While the analysis should identify both cis- and trans-mapping hybridization profiles, we identified only loci with strain-specific hybridization differences that are most likely due to differences in the locus itself (i.e., cis mapping). Interestingly, a larger number of these cis-mapping genes than would be expected by chance encode either confirmed or predicted secreted proteins, many of which are known to localize to the specialized secretory organelles characteristic of members of the phylum Apicomplexa. For six of the cis-mapping loci, we determined if the strain-specific hybridization differences were due to true transcriptional differences or rather to strain-specific differences in hybridization efficiency because of extreme polymorphism and/or deletion, and we found examples of both scenarios.
