Humic acid improves wheat growth by modulating auxin and cytokinin biosynthesis pathways
Bibliographic record
Abstract
Abstract Humic acids have been widely used for centuries to enhance plant growth and productivity. The beneficial effects of humic acids have been attributed to different functional groups and phytohormone-like compounds enclosed in macrostructure. However, the mechanisms underlying the plant growth-promoting effects of humic acids are only partially understood. We hypothesize that the bio-stimulatory effect of humic acids is mainly due to the modulation of innate pathways of auxin and cytokinin biosynthesis in treated plants. A physiological investigation along with molecular characterization was carried out to understand the mechanism of bio-stimulatory effects of humic acid. A gene expression analysis was performed for the genes involved in auxin and cytokinin biosynthesis pathways in wheat seedlings. Furthermore, Arabidopsis thaliana transgenic lines generated by fusing the auxin-responsive DR5 and cytokinin-responsive ARR5 promoter to ß-glucuronidase (GUS) reporter were used to study the GUS expression analysis in humic acid treated seedlings. This study demonstrates that humic acid treatment improved the shoot and root growth of wheat seedlings. The expression of several genes involved in auxin (Tryptophan Aminotransferase of Arabidopsis and Gretchen Hagen 3.2) and cytokinin (Lonely Guy3) biosynthesis pathways were up-regulated in humic acid-treated seedlings compared to the control. Furthermore, GUS expression analysis showed that bioactive compounds of humic acid stimulate endogenous auxin and cytokinin-like activities. This study is the first report in which using ARR5:GUS lines we demonstrate the biostimulants activity of humic acid.
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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".