Identification of 14-3-3 Family in Common Bean and Their Response to Abiotic Stress
Bibliographic record
Abstract
14-3-3s are a class of conserved regulatory proteins ubiquitously found in eukaryotes, which play important roles in a variety of cellular processes including response to diverse stresses. Although much has been learned about 14-3-3s in several plant species, it remains unknown in common bean. In this study, 9 common bean 14-3-3s (PvGF14s) were identified by exhaustive data mining against the publicly available common bean genomic database. A phylogenetic analysis revealed that each predicted PvGF14 was clustered with two GmSGF14 paralogs from soybean. Both epsilon-like and non-epsilon classes of PvGF14s were found in common bean, and the PvGF14s belonging to each class exhibited similar gene structure. Among 9 PvGF14s, only 8 are transcribed in common bean. Expression patterns of PvGF14s varied depending on tissue type, developmental stage and exposure of plants to stress. A protein-protein interaction study revealed that PvGF14a forms dimer with itself and with other PvGF14 isoforms. This study provides a first comprehensive look at common bean 14-3-3 proteins, a family of proteins with diverse functions in many cellular processes, especially in response to stresses.
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 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 | 0.001 |
| Bibliometrics | 0.001 | 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 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".