Hydroxymethylbutenyl diphosphate accumulation reveals MEP pathway regulation for high CO <sub>2</sub> -induced suppression of isoprene emission
Bibliographic record
Abstract
Isoprene is emitted by some plants and is the most abundant biogenic hydrocarbon entering the atmosphere. Multiple studies have elucidated protective roles of isoprene against several environmental stresses, including high temperature, excessive ozone, and herbivory attack. However, isoprene emission adversely affects atmospheric chemistry by contributing to ozone production and aerosol formation. Thus, understanding the regulation of isoprene emission in response to varying environmental conditions, for example, elevated CO 2 , is critical to comprehend how plants will respond to climate change. Isoprene emission decreases with increasing CO 2 concentration; however, the underlying mechanism of this response is currently unknown. We demonstrated that high-CO 2 -mediated suppression of isoprene emission is independent of photosynthesis and light intensity, but it is reduced with increasing temperature. Furthermore, we measured methylerythritol 4-phosphate (MEP) pathway metabolites in poplar leaves harvested at ambient and high CO 2 to identify why isoprene emission is reduced under high CO 2 . We found that hydroxymethylbutenyl diphosphate (HMBDP) was increased and dimethylallyl diphosphate (DMADP) decreased at high CO 2. This implies that high CO 2 impeded the conversion of HMBDP to DMADP, possibly through the inhibition of HMBDP reductase activity, resulting in reduced isoprene emission. We further demonstrated that although this phenomenon appears similar to abscisic acid (ABA)-dependent stomatal regulation, it is unrelated as ABA treatment did not alter the effect of elevated CO 2 on the suppression of isoprene emission. Thus, this study provides a comprehensive understanding of the regulation of the MEP pathway and isoprene emission in the face of increasing CO 2 .
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 | 0.000 |
| 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 | 0.001 |
| 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".