Ethylene Networks – Muday Lab

Elevated levels of the plant hormone ethylene modulate root development, leading to shorter primary roots, fewer lateral roots, and greater numbers of root hairs (Negi et al. 2008). Genetic approaches have identified the ethylene signaling machinery, which includes a family of 5 receptors that modulate distinct developmental processes (Harkey et al. 2018). We have identified the central roles of one of these receptors, ETR1, in mediating ethylene-regulated root architecture (Shakeel et al. 2013) in collaboration with the laboratory of Brad Binder. The early ethylene signaling pathway has been studied in detail, but downstream transcriptional effects have not been as well-studied, especially in roots and in light-grown seedlings. To identify the root transcriptional responses to ethylene we examined the kinetics of changes in transcript abundance and overlaid these on the kinetics of developmental changes (Harkey et al. 2018).

We have recently completed a meta-analysis that reveals both conversed ethylene response genes in roots that are regulated across multiple data sets and ethylene-regulated transcripts that depend upon the light environment (Harkey et al. 2019). Current work is using this time course transcriptomic data to uncover the transcription factor networks controlling ethylene-dependent developmental changes. We are using computational approaches to identify transcription factor targets and applying Bayesian modeling of time-course transcriptomics to reveal relationships in this dataset, in collaboration with David John and William Turkett from Computer Science and James Norris from Mathematics and Statistics Departments at Wake Forest. They have developed Bayesian modeling approaches that reveal cotemporal and next-state relationships between transcripts within our time course ethylene transcriptomics. We are testing those predicted networks (LaPointe et al. 2018, Allen et al. 2020). We have also developed a method to predict transcription factor regulators from lists of coexpressed genes, which is available as a web app, and which we used to predict transcription factors involved in root ethylene response (Harkey et al. 2020).

This work is supported by the National Science Foundation (MCB-1716279).

Publications