Metabolomics-Driven Investigation of Harpin αβ and Laminarin Effects on Cannabis sativa L. Employing GC/EI/MS and 1H NMR Metabolomics
Bibliographic record
Abstract
Hemp (Cannabis sativa L.) is a polymorphic species that synthesizes an array of bioactive metabolites, with cannabinoids and terpenoids constituting the major chemical classes. Until recently, the lack of legislative framework led to limited research on hemp’s plant protection and nutrition. Biostimulants have recently attracted scientific attention as sustainable alternatives to plant protection products (PPPs). Herein, we investigated the effects of biostimulant harpin (αβ) proteins and the PPP polysaccharide laminarin on hemp (cv. Futura 75), employing GC/EI/MS and 1H NMR metabolomics. Analyses demonstrated that treatments induced distinct shifts in the metabolism of the plants, thus, enabling the discovery of metabolite-biomarkers of physiological adaptation, defense mechanisms (α-linolenic acid), and bioactivity (cannabinoids). Harpin and laminarin altered the concentration of bioactive compounds such as cannabidiol, essential amino acids including L-phenylalanine and GABA, salicylate, and caffeate. Pathway analysis revealed treatment-specific modulation of key metabolic networks, with harpin triggering early, yet transient activation of phenylpropanoid- and amino acid-related pathways before broad repression, whereas laminarin maintained a more balanced regulation, sustaining defense-related biosynthesis while preserving core primary metabolism. Results advance the understanding of molecular mechanisms underlying biostimulants’ action in hemp and support their potential for improving plant health and attributes of cannabis-derived products, providing insights for its sustainable cultivation.
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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 | 0.001 |
| 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".