Genome-wide identification of late embryogenesis abundant (LEA) protein family and their key regulatory network in Pinus tabuliformis cold acclimation.
Chengcheng ZhouShihui NiuYousry A El-KassabyWei LiPublished in: Tree physiology (2023)
Cold acclimation is a crucial biological process that enables conifers to safely overwinter. The late embryogenesis abundant (LEA) protein family plays a pivotal role in enhancing freezing tolerance during this process. Despite its importance, the identification, molecular functions, and regulatory networks of the LEA protein family have not been extensively studied in conifers or gymnosperms. Pinus tabuliformis, a conifer with high ecological and economic value with high-quality genome sequence, is an ideal candidate for such studies. Here, a total of 104 LEA genes were identified from P. tabuliformis and renamed them according to their subfamily group: PtLEA1-PtLEA92 (group LEA1-LEA6), PtSMP1-PtSMP6 (group SMP) and PtDHN1-PtDHN6 (group Dehydrin). While the sequence structure of P. tabuliformis LEA genes are conserved, their physicochemical properties exhibit unique characteristics within different subfamily groupings. Notably, the abundance of low-temperature responsive elements in PtLEA genes was observed. Using annual rhythm and temperature gradient transcriptome data, PtLEA22 was identified as a key gene that responds to low-temperature induction while conforming to the annual cycle of cold acclimation. Overexpression of PtLEA22 enhanced Arabidopsis freezing tolerance. Furthermore, several transcription factors potentially co-expressed with PtLEA22 were validated using Y1H and LUC assays, revealing that PtDREB1 could directly bind PtLEA22 promoter to positively regulate its expression. These findings reveal the genome-wide characterization of P. tabuliformis LEA genes and their importance in the cold acclimation, while providing a theoretical basis for studying the molecular mechanisms of cold acclimation in conifers.
Keyphrases
- electronic health record
- genome wide identification
- transcription factor
- genome wide
- dna binding
- dna methylation
- amino acid
- binding protein
- protein protein
- single cell
- atrial fibrillation
- cell proliferation
- high throughput
- climate change
- drug delivery
- machine learning
- risk assessment
- bioinformatics analysis
- deep learning
- big data