Genome-wide identification of late embryogenesis abundant protein family and their key regulatory network in <i>Pinus tabuliformis</i> cold acclimation
Bibliographic record
Abstract
Cold acclimation is a crucial biological process that enables conifers to overwinter safely. 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 values and 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 we renamed them according to their subfamily group: PtLEA1-PtLEA92 (group LEA1-LEA6), PtSMP1-PtSMP6 (group seed maturation protein) 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 yeast one-hybrid and dual-luciferase 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".