High-throughput phenotyping using parallel sequencing of RNA interference targets in the African trypanosome

    • 1London School of Hygiene & Tropical Medicine, London WC1E 7HT, United Kingdom;
    • 2The Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, United Kingdom
    • 3 Present addresses: Oxford Nanopore Technologies, Oxford Science Park, Oxford OX4 4GA, UK;
    • 4 The Rockefeller University, 1230 York Avenue, New York, NY 10065, USA;
    • 5 University of Liverpool, Liverpool L69 3BX, UK.
    • 6 These authors contributed equally to this work.
    • 7 Corresponding author. E-mail [email protected].
Published March 1, 2011. https://doi.org/10.1101/gr.115089.110
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cover of Genome Research Vol 36 Issue 5
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Abstract

African trypanosomes are major pathogens of humans and livestock and represent a model for studies of unusual protozoal biology. We describe a high-throughput phenotyping approach termed RNA interference (RNAi) target sequencing, or RIT-seq that, using Illumina sequencing, maps fitness-costs associated with RNAi. We scored the abundance of >90,000 integrated RNAi targets recovered from trypanosome libraries before and after induction of RNAi. Data are presented for 7435 protein coding sequences, >99% of a non-redundant set in the Trypanosoma brucei genome. Analysis of bloodstream and insect life-cycle stages and differentiated libraries revealed genome-scale knockdown profiles of growth and development, linking thousands of previously uncharacterized and “hypothetical” genes to essential functions. Genes underlying prominent features of trypanosome biology are highlighted, including the constitutive emphasis on post-transcriptional gene expression control, the importance of flagellar motility and glycolysis in the bloodstream, and of carboxylic acid metabolism and phosphorylation during differentiation from the bloodstream to the insect stage. The current data set also provides much needed genetic validation to identify new drug targets. RIT-seq represents a versatile new tool for genome-scale functional analyses and for the exploitation of genome sequence data.

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