Identification of critical transition signal (CTS) to characterize regulated stochasticity during ABA-induced growth-to-defense transition
Bibliographic record
Abstract
BACKGROUND: Abscisic acid (ABA) plays a central role in regulating plant responses to abiotic stress. It orchestrates a complex regulatory network that facilitates the transition from growth to defense. Understanding the molecular mechanisms underlying this ABA-induced transition from growth to defense is essential for elucidating plant adaptive strategies under environmental stress conditions. RESULTS: In this study, we used a refined dynamic network biomarker (DNB) approach to quantitatively identify the critical transition signal (CTS) and characterize the regulated stochasticity during the ABA-induced transition from growth to defense in Arabidopsis thaliana. By integrating high-resolution time-series RNA-seq data with dynamic network analysis, we identified a set of DNB genes that serve as key molecular regulators of this transition. The critical transition phase was identified precisely at the ninth time point (6 h after treatment), which marks the crucial switch from a growth-dominated to a defense -oriented state. Gene Ontology (GO) enrichment analysis revealed a significant overrepresentation of defense-related biological processes, while STRING network analysis revealed strong functional interactions between DNB genes and differentially expressed genes (DEGs) and highlighted key regulatory hubs. In particular, key hub genes such as PIF4, TPS8, NIA1, and HSP90-5 were identified as potential master regulators of ABA-mediated defense activation, highlighting their importance for plant stress adaptation. CONCLUSIONS: By integrating a network-driven transcriptomic analysis, this study provides new insights into the molecular basis of ABA-induced transitions from growth to defense. The identification of CTS provides a new perspective on regulated stochasticity in plant stress responses and provides a conceptual framework for improving crop stress resistance. In addition, the establishment of a comprehensive database of ABA-responsive defense genes represents a valuable resource for future research on plant adaptation and resilience.
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 | 0.000 |
| 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 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".