The Volatile Pentadecane From <i>Bacillus</i> Alleviates Plant Iron Deficiency Through Activating the Reduction‐Based Fe Uptake System
Bibliographic record
Abstract
ABSTRACT Iron (Fe) is an essential micronutrient for plant growth and development. Rhizosphere microorganisms play crucial roles in plant Fe nutrition, yet the underlying mechanisms remain largely unknown. Here, we assessed the effect of the volatile organic compounds (VOCs) produced by Bacillus velezensis SQR9 on Fe uptake in Arabidopsis, elucidated the underlying mechanism using genetic and biochemical approaches, and identified the active components of SQR9 VOCs. Our results showed that plants exhibited enhanced Fe uptake, chlorophyll content, fresh weight, and root development when exposed to SQR9 VOCs under Fe‐limited conditions. The reduction‐based Fe uptake genes in Arabidopsis, encompassing the Fe‐deficiency‐induced transcription factor FIT , the ferric‐chelate reductase FRO2 , and the ferrous Fe transporter IRT1 , were significantly upregulated by SQR9 VOCs. The deletion of these genes diminished the enhancing effect of SQR9 VOCs on plant Fe uptake. Meanwhile, this enhancement was found to be dependent on the accumulation of nitric oxide (NO) in the roots. Among the 23 VOCs in SQR9, we identified pentadecane as a key active compound that mediates NO signalling and promotes plant Fe uptake under Fe‐limited conditions. The effective working concentration range for gaseous pentadecane was determined to be approximately 128.3 to 513.2 ng L −1 . In conclusion, our findings illuminate the mechanism by which SQR9 VOCs promote plant Fe uptake and highlight the application potential of SQR9 as a plant growth‐promoting rhizobacterium (PGPR) for Fe biofortification of crops.
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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".