Exploring physiological and molecular dynamics of drought stress responses in plants: challenges and future directions
Bibliographic record
Abstract
Plants face multifactorial environmental stressors mainly due to global warming and climate change which affect their growth, metabolism, and productivity. Among them, is drought stress which alters intracellular water relations, photosynthesis, ion homeostasis and elevates reactive oxygen species which eventually reduce their growth and yields. In addition, drought alters soil physicochemical properties and beneficial microbiota which are critical for plant survival. Recent reports have shown that climate change is increasing the occurrence and intensity of drought in many regions of the world, which has become a primary concern in crop productivity, ecophysiology and food security. To develop ideas and strategies for protecting plants against the harmful effects of drought stress and meeting the future food demand under climatic calamities an in-depth understanding of molecular regulatory pathways governing plant stress responses is imperative. In parallel, more research is needed to understand how drought changes the features of soil, particularly microbiomes, as microorganisms can withstand drought stress faster than plants, which could assist them to recover. In this review we first discuss the effect of drought stress on plants, soil physicochemical properties and microbiomes. How drought stress affects plant microbe interactions and other microbe-driven beneficial traits was also highlighted. Next, we focused on how plants sense drought and undergo biochemical reprogramming from root to shoot to regulate diverse adaptive traits. For instance, the role of calcium (Ca2+), reactive oxygen species (ROS) and abscisic acid (ABA) in modulating different cellular responses like stomata functioning, osmotic adjustment, and other adaptive traits. We also provide an update on the role of different hormones in drought signaling and their crosstalk which allows plants to fine tune their responses during drought stress. Further, we discussed how recurrent drought exposure leads to the development of short-term memory in plants that allows them to survive future drought stresses. Lastly, we discussed the application of omics and biotechnological-based mitigating approaches to combat drought stress in sustainable agriculture. This review offers a deeper understanding of multiple factors that are related to drought stress in plants which can be useful for drought improvement programs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".