Biosynthesis and multifaceted roles of reactive species in plant defense mechanisms during environmental cues
Bibliographic record
Abstract
• Elucidates ROS and RNS biosynthesis and their spatiotemporal signaling dynamics. • Reveals ROS-RNS crosstalk modulating plant immunity and abiotic stress adaptation. • Highlights advanced imaging for in vivo visualization of reactive species in plants. • Identifies gaps in molecular specificity of ROS/RNS signaling and gene regulation. • Integrates ROS/RNS roles with hormonal and epigenetic networks in stress resilience. Reactive oxygen species (ROS) and reactive nitrogen species (RNS) are key components of plant metabolism, acting as cellular damage agents and essential signalling molecules. They play crucial roles in plant growth, development, and responses to environmental cues. This review clarifies their distinct and overlapping functions within plant signalling pathways. ROS and RNS are produced in organelles such as chloroplasts, mitochondria, and peroxisomes, where their concentrations are tightly regulated to balance signalling functions and prevent cellular damage. We explore their signalling roles, mechanisms underlying signal transduction, interactions across plant systems, and influence on plant stress responses. Evidence shows that ROS and RNS regulate adaptation to drought, salinity, pathogens, and herbivores. Advanced imaging techniques have enhanced our understanding of their cellular dynamics in redox signalling pathways, highlighting the need to balance their production and scavenging for optimal plant health. Understanding their biosynthesis, signalling, and interactions is crucial for unravelling their contributions to plant growth and resilience.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".