Gene Expression Profiling of Transcription Factors and Acclimation-Related Genes in Ribes spp.
Bibliographic record
Abstract
The ability of Ribes species to survive the fluctuating winter and early spring conditions, relies on the regulation of transcription factors (TFs) and other key genes involved in the abiotic stress response. In this study, we developed specific primers for 33 stress-responsive genes, which may facilitate future functional studies in Ribes and other less-characterized lineages within the Saxifragales order. These genes were selected based on a comparative transcriptomic analysis of R. nigrum cv. Aldoniai and are known to function in cold acclimation and stress signaling pathways. We analyzed expression profiles of these 33 genes in R. aureum, R. hudsonianum, and R. nigrum microshoot cultures exposed to controlled cold stress, deacclimation and reacclimation treatments. Our results revealed species-specific genetic responses across acclimation cycles of varying durations (24–96 h). Cold stress induces molecular changes in three Ribes spp.; however, deacclimation triggered by transient warming significantly reduced freezing tolerance in R. nigrum, had a moderate effect on R. hudsonianum, and minor impact on R. aureum. Gene expression profiling revealed distinct, species-specific regulatory patterns among species during different stress cycles, highlighting conserved and specific genes in acclimation mechanisms within the Ribes spp. These findings contribute to a deeper understanding of transcriptional regulation under acclimation cycles in currants and provide molecular tools that may support breeding strategies aimed at enhancing cold tolerance in Ribes crops amid increasing climate variability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".