Location: Plant Gene Expression CenterTitle: A practical guide to Quantitative Interactor Screening with Next Generation Sequencing (QIS-Seq)
|GONG, YUCHEN - University Of Toronto|
|DESVEAUX, DARRELL - University Of Toronto|
|GUTTMAN, DAVID - University Of Toronto|
Submitted to: Methods in Molecular Biology
Publication Type: Book / Chapter
Publication Acceptance Date: 3/24/2016
Publication Date: 10/31/2017
Citation: Gong Y., Desveaux D., Guttman D.S., Lewis J.D. 2017. A practical guide to quantitative interactor screening with next-generation sequencing (QIS-Seq). In: Tatarinova, T., Nikolsky, Y. editors. Biological Networks and Pathway Analysis. Volume 1613. New York, NY: Humana Press. p. 1-20
Interpretive Summary: A major challenge in biology is to understand the protein networks that control the biology of an organism. We developed a novel method to identify protein-protein interactions in a high-throughput quantitative manner, and provide a detailed protocol that can be applied to a multitude of biological questions. This protocol will allow many laboratories to apply this technique to their experimental systems. For instance, this method can be used to identify plant proteins that are targeted by bacterial pathogens during infection, or to identify protein-protein interactions that contribute to cancer or auditory function.
Technical Abstract: Yeast two-hybrid screens are a powerful approach to identify protein-protein interactions; however, they are typically limited in the number of interactions identified, and lack quantitative values to ascribe confidence scores to the interactions that are obtained. We have developed a high-throughput, quantitative, yeast two-hybrid screening approach coupled with next-generation sequencing. This strategy allows the identification of interacting proteins that are preferentially associated with a bait of interest, and helps eliminate non-specific interacting proteins. The method is high-throughput, allowing many more baits to be tested and many more candidate interacting proteins to be identified. Quantitative data allows the interactors to be ascribed confidence scores based on their enrichment with particular baits, and can identify both common and rare interacting proteins.