Epigenomics and stress resilience
Our research focuses on the interface between epigenomics and plant memory. This research area addresses key challenges identified in the decadal vision for Plant Sciences.
The possible roles of epigenetics in environmental responses are hotly debated. While plants clearly retain some memories of past experiences, the extent to which the environment leads to heritable changes in the epigenome is unresolved (Mirouze and Paszkowski, 2011). Despite expectations, the epigenome, in particular DNA methylation, is particularly resistant to many environmental perturbations (Crisp et al., 2016). We have demonstrated that DNA methylation is unresponsive to multiple stresses (Ganguly et al., 2017, 2018). One possibility is that rapid recovery mechanisms preclude lasting imprints of stress.
Cryptic stress resilience epialleles
While DNA methylation may be relatively impervious to stress, beneficial but non-essential stress resilience alleles may have become silenced by DNA methylation over time. We hypothesise that stress resilience traits may be enriched among the cryptic epiallele variants. We aim to identify and characterize cryptic stress resilience traits using comparative genomics tools and epiRIL populations is crops such as barley and sorghum.
Stress recovery in crops
As a second novel avenue towards enhancing stress resilience, we are investigating the tension between plant stress memory and recovery. This is an ongoing collaboration with the Pogson Lab, ANU. We have found that the transcriptome recovers from light-stress with incredible speed (Crisp et al., 2017). Our future interest is in examining whether various crops, including C3 (barley) and C4 (maize), also have mechanisms for rapid transcriptome recovery from light-stress.
Extending prior work, we are constructing gene regulatory networks (GRNs) using RNA-seq from light-stress recovery time courses to identify factors important for both rapid deactivation of stress responses and for re-activation of growth and productivity. These factors; for instance, key transcription factors or signaling pathway genes, will be targeted for detailed reverse genetic characterization.
