GUS Reporter-Aided Promoter Deletion Analysis of A. thaliana POLYAMINE OXIDASE 3 .
Varvara PodiaDimitris ChatzopoulosDimitra MilioniDimitrios J StravopodisChrysanthi ValassakisAndreas RoussisKalliopi A Roubelakis-AngelakisKosmas HaralampidisPublished in: International journal of molecular sciences (2023)
Polyamine oxidases (PAOs) have been correlated with numerous physiological and developmental processes, as well as responses to biotic and abiotic stress conditions. Their transcriptional regulation is driven by signals generated by various developmental and environmental cues, including phytohormones. However, the inductive mechanism(s) of the corresponding genes remains elusive. Out of the five previously characterized Arabidopsis PAO genes, none of their regulatory sequences have been analyzed to date. In this study, a GUS reporter-aided promoter deletion approach was used to investigate the transcriptional regulation of AtPAO3 during normal growth and development as well as under various inductive environments. AtPAO3 contains an upstream open reading frame (uORF) and a short inter-cistronic sequence, while the integrity of both appears to be crucial for the proper regulation of gene expression. The full-length promoter contains several cis -acting elements that regulate the tissue-specific expression of AtPAO3 during normal growth and development. Furthermore, a number of TFBS that are involved in gene induction under various abiotic stress conditions display an additive effect on gene expression. Taken together, our data indicate that the transcription of AtPAO3 is regulated by multiple environmental factors, which probably work alongside hormonal signals and shed light on the fine-tuning mechanisms of PAO regulation.
Keyphrases
- genome wide identification
- transcription factor
- gene expression
- dna methylation
- genome wide
- crispr cas
- poor prognosis
- genome wide analysis
- minimally invasive
- working memory
- electronic health record
- binding protein
- big data
- metabolic syndrome
- human health
- deep learning
- risk assessment
- long non coding rna
- data analysis