Metal and non-metal nanoparticles differentially regulate flavonoid accumulation in Cyclocarya paliurus: A multi-omics analysis
Bibliographic record
Abstract
Nanotechnology offers emerging tools for elevating metabolites levels in agricultural and medicinal crops, yet its regulatory effects on woody species remains underexplored. Cyclocarya paliurus , a precious tree species with flavonoid-linked antioxidant and hypoglycemic properties, was foliar-applied with nanoparticles (NPs) of ZnO, MnO 2 , and SiO 2 to investigate their elicitation potential under field-simulated conditions. Compared with the control, applications of MnO 2 - and SiO 2 -NPs markedly increased total flavonoid content (TFC) in C. paliurus leaves by 21.26–49.99 %, and enhanced per-plant flavonoid accumulation by 40.70–50.06 %. Conversely, applications of high-dose ZnO-NPs induced oxidative imbalance, resulting in a 30.25 % and 43.84 % decline in TFC and flavonoid accumulation respectively. Multi-omics profiling revealed that both MnO 2 - NPs and SiO 2 -NPs activated ROS-dependent pathways involving MAPK and glutathione signaling, while SiO₂-NPs may also promote flavonoid biosynthesis via enhancing carbon fixation and primary metabolism. The MYB-bHLH-WD40 complex (including MYB113 , bHLH42 , and TTG1 ) was identified as a central regulatory node, ranking among the top 10 contributors in O2PLS analysis and exhibiting strong correlations (cor > 0.95) with flavonoid metabolites. Collectively, we propose a model wherein ROS signaling induced by NPs interacts with transcriptional networks to modulate flavonoid accumulation. This work provides a nano-enabled strategy to boost health-promoting compounds in the woody plants. • Nanoparticles (NPs) act as elicitors to enhance flavonoid accumulation in C. paliurus. • Both metal and non-metal NPs activate ROS cascades to promote flavonoid biosynthesis. • MnO 2 -NPs induce strong redox activity, while SiO 2 -NPs enhance carbon fixation pathways. • The MYB-bHLH-WD40 complex likely regulates NP-induced flavonoid biosynthesis.
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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 | 0.001 | 0.001 |
| 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".