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ARS Home » Midwest Area » Ames, Iowa » Corn Insects and Crop Genetics Research » Research » Publications at this Location » Publication #377728

Research Project: Host and Pathogen Signaling in Cereal-Fungal Interactions

Location: Corn Insects and Crop Genetics Research

Title: Next-generation yeast-two-hybrid analysis with Y2H-SCORES identifies novel interactors of the MLA immune receptor

item VELASQUEZ-ZAPATA, VALERIA - Iowa State University
item ELMORE, MITCH - Iowa State University
item BANERJEE, SAGNIK - Iowa State University
item DORMAN, KARIN - Iowa State University
item Wise, Roger

Submitted to: PLoS Computational Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/17/2021
Publication Date: 4/2/2021
Citation: Velasquez-Zapata, V., Elmore, M.J., Banerjee, S., Dorman, K., Wise, R.P. 2021. Next-generation yeast-two-hybrid analysis with Y2H-SCORES identifies novel interactors of the MLA immune receptor. PLoS Computational Biology. 17(4). Article e1008890.

Interpretive Summary: Organisms respond to their environment through networks of interacting proteins and other biomolecules. In order to investigate these interacting proteins, many in vitro and in vivo techniques have been used. Among these, yeast two-hybrid (Y2H) has been adapted for use in combination with next generation sequencing (NGS) to approach protein-protein interactions on a genome-wide scale. The fusion of these two methods has been termed next-generation-interaction screening, abbreviated as Y2H-NGIS. However, the massive amounts of data resulting from this technology have presented challenges in data analysis, resulting in delayed implementation. Features such as proper selection of controls and data normalization have received little attention. To address these challenges, we optimized the computational and statistical analysis of Y2H-NGIS to provide metrics to identify high-confidence interacting proteins under a variety of dataset scenarios. Our proposed framework can be extended to different yeast-based interaction settings, utilizing the general principles of enrichment, specificity, and in-frame prey selection to accurately assemble protein-protein interaction networks. Lastly, we showed how the pipeline works experimentally, by validating the interaction between the powdery mildew effector AVRA13 and the barley vesicle-mediated thylakoid membrane biogenesis protein, HvTHF1. Impact: Challenges in data analysis of genome-scale protein-protein interaction data have inhibited broad use and implementation. Application of Y2H SCORES will enable bench scientists to quickly put together statistically relevant biological networks to model cellular behavior. This will promote new investigations from lab to fields, critical to breeders and growers that use abiotic and biotic stress resistance to produce better crops.

Technical Abstract: Interactomes embody one of the most efficient models of cellular behavior by representing function through protein associations. Building such models requires reproducible and efficient high-throughput implementation of the prevailing molecular techniques to ascertain interacting partners. Among them, the yeast two-hybrid (Y2H) screen, and its high-throughput versions, termed next-generation interaction screening (NGIS), comprise a promising approach to generate extensive protein-protein interaction networks. However, challenges that remain to mining reliable information from these screens limit its broader implementation. Here, we describe a statistical framework, designated Y2H-SCORES, for analyzing high-throughput Y2H screens that considers key aspects of experimental design, normalization, and controls. Three quantitative ranking scores were implemented to identify interacting partners, comprising: 1) significant enrichment under selection for positive interactions, 2) degree of interaction specificity among multi-bait comparisons, and 3) selection of in-frame interactors. Using simulation and an empirical dataset, we show that outputs from this pipeline efficiently predicted interacting partners and facilitated validation by one to one bait-prey tests. Y2H-SCORES identified bonafide interactors under different scenarios, providing a quantitative measurement to predict Y2H interacting partners. Simulation of Y2H-NGIS identified conditions that maximize the interactor mining efficiency, which can be achieved with protocols such as prey library normalization, appropriate experiment volumes and replication of experimental treatments. Hence, the Y2H-SCORES framework makes possible computational implementation in different yeast-based interaction screenings and biological interpretation of the resulting networks. Proof-of-concept was demonstrated by identification and validation of a novel interaction between the barley powdery mildew effector, AVRA13, with the vesicle-mediated thylakoid membrane biogenesis protein, HvTHF1.