Phytohormones enhance heavy metal responses in <i>Euglena gracilis</i> : evidence from uptake of Ni, Pb and Cd and linkages to hormonomic and metabolomic dynamics
Bibliographic record
Abstract
Abstract Over the last decade, significant effort has been made to understand phytohormonal functions (e.g. cytokinins (CKs) and abscisic acid (ABA)) in metal stress responses of higher plants and algae. Despite the potential for these phytohormones to improve industrial remediation by Euglena gracilis (Euglenophyceae), no such roles have been elucidated for this highly adaptive species and its response to heavy metals. This study demonstrates that toxic metals (nickel, lead, cadmium) modify hormonal activity profiles (i.e., CK forms and their concentrations) in E. gracilis . Furthermore, exogenous ABA or CK ( t Z) enabled higher metal uptake efficiency and alleviated metal toxicity through the regulation of endogenous CKs and gibberellins (GAs) levels. These responses suggest that E. gracilis regulates multiple phytohormone signals during metal stress acclimation. A deeper approach, using untargeted metabolomic analyses, gave more detailed insight into phytohormone-controlled pathways and associated modified metabolites, which were frequently related to metal accumulation and the physiological acclimation to metal presence. Significant changes in the levels of cellular metabolites, especially those involved in acclimation to metal stress, were under the influence of phytohormones in algal cells. When grown under metal stress conditions, the presence of exogenous ABA or CKs, caused changes in cellular metabolites which included those from: lipid pathways, riboflavin metabolism, the biosynthesis of cofactors/vitamins, and carbohydrate metabolism. Also, bioactive secondary metabolites (e.g., terpenoids, alkaloids, flavonoids, carotenoids) were modified in algal cells treated with phytohormones. Thus, the study gives a detailed view on the regulatory functions of ABA and CKs in algal metal bioremediation strategies, which are attributed to enhanced metal uptake and in the fine-tuning of plant hormone levels during metal stress response. The results can guide efforts to develop efficient, low-cost and environmentally friendly methods for bioremediation. Highlights Metal stress produces phytohormone-specific responses in Euglena gracilis . Phytohormones (ABA and CK) enhance metal accumulation rates. Phytohormone-controlled metal uptake reflects enhanced CK activity profiles. Modified hormonal crosstalk is involved in phytohormone-induced metal uptake. Metabolomic responses to phytohormones-involve metal stress mitigation compounds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".