Implications of <i>miR166</i> and <i>miR159</i> induction to the basal response mechanisms of an andigena potato (<i>Solanum tuberosum</i> subsp. <i>andigena</i>) to salinity stress, predicted from network models in Arabidopsis
Bibliographic record
Abstract
MicroRNA (miRNA) mediated changes in gene expression by post-transcriptional modulation of major regulatory transcription factors is a potent mechanism for integrating growth and stress-related responses. Exotic plants including many traditional varieties of Andean potatoes (Solanum tuberosum subsp. andigena) are known for better adaptation to marginal environments. Stress physiological studies confirmed earlier reports on the salinity tolerance potentials of certain andigena cultivars. Guided by the hypothesis that certain miRNAs play important roles in growth modulation under suboptimal conditions, we identified and characterized salinity stress-responsive miRNA-target gene pairs in the andigena cultivar Sullu by parallel analysis of noncoding and coding RNA transcriptomes. Inverse relationships were established by the reverse co-expression between two salinity stress-regulated miRNAs (miR166, miR159) and their target transcriptional regulators HD-ZIP-Phabulosa/Phavulota and Myb101, respectively. Based on heterologous models in Arabidopsis, the miR166-HD-ZIP-Phabulosa/Phavulota network appears to be involved in modulating growth perhaps by mediating vegetative dormancy, with linkages to defense-related pathways. The miR159-Myb101 network may be important for the modulation of vegetative growth while also controlling stress-induced premature transition to reproductive phase. We postulate that the induction of miR166 and miR159 under salinity stress represents important network hubs for balancing gene expression required for basal growth adjustments.
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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 | 0.000 | 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) | 0.001 | 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".