A WRKY transcription factor, TaWRKY40-D, promotes leaf senescence associated with jasmonic acid and abscisic acid pathways in wheat.
L ZhaoW ZhangQ SongY XuanK LiL ChengH QiaoG WangChunjiang ZhouPublished in: Plant biology (Stuttgart, Germany) (2020)
Leaf senescence is a complex and precise regulatory process that is correlated with numerous internal and environmental factors. Leaf senescence is tightly related to the redistribution of nutrients, which significantly affects productivity and quality, especially in crops. Evidence shows that the mediation of transcriptional regulation by WRKY transcription factors is vital for the fine-tuning of leaf senescence. However, the underlying mechanisms of the involvement of WRKY in leaf senescence are still unclear in wheat. Using RNA sequencing data, we isolated a novel WRKY transcription factor, TaWRKY40-D, which localizes in the nucleus and is basically induced by the progression of leaf senescence. TaWRKY40-D is a promoter of natural and dark-induced leaf senescence in transgenic Arabidopsis thaliana and wheat. We also demonstrated a positive response of TaWRKY40-D in wheat upon jasmonic acid (JA) and abscisic acid (ABA) treatment. Consistent with this, the detached leaves of TaWRKY40-D VIGS (virus-induced gene silencing) wheat plants showed a stay-green phenotype, while TaWRKY40-D overexpressing Arabidopsis plants showed premature leaf senescence after JA and ABA treatment. Moreover, our results revealed that TaWRKY40-D positively regulates leaf senescence, possibly by altering the biosynthesis and signalling of JA and ABA pathway genes. Together, our results suggest a new regulator of JA- and ABA-related leaf senescence, as well as a new candidate gene that can be used for molecular breeding in wheat.
Keyphrases
- transcription factor
- dna damage
- endothelial cells
- genome wide identification
- dna binding
- stress induced
- arabidopsis thaliana
- high glucose
- gene expression
- oxidative stress
- genome wide
- dna methylation
- climate change
- single cell
- heavy metals
- electronic health record
- quality improvement
- deep learning
- single molecule
- big data
- replacement therapy