Transgenerational Plasticity Enhances the Tolerance of Duckweed (Lemna minor) to Stress from Exudates of Microcystis aeruginosa
Bibliographic record
Abstract
Transgenerational plasticity (TGP) refers to the influence of ancestral environmental signals on offspring’s traits across generations. While evidence of TGP in plants is growing, its role in plant adaptation over successive generations remains unclear, particularly in floating plants facing fluctuating environments. Duckweed (Lemna minor), a common ecological remediation material, often coexists with the harmful bloom-forming cyanobacterium Microcystis aeruginosa, which releases a highly toxic exudate mixture (MaE) during its growth. In this study, we investigate the TGP of duckweed and its adaptive role under stress from MaE during the bloom-forming process. We found that exposure to MaE induces significant phenotypic plasticity in duckweed, manifested by alterations in morphological, physiological, and transcriptomic profiles. Specifically, MaE exposure significantly affected duckweed, promoting growth at low concentrations but inhibiting it at high concentrations, affecting traits like biomass, frond number, total frond area, and photosynthetic efficiency. Additionally, the activities of antioxidant enzymes, together with the levels of proline, soluble sugars, and proteins, are elevated with increasing MaE concentrations. These plastic changes are largely retained through asexual reproductive cycles, persisting for several generations even under MaE-free conditions. We identified 619 genes that maintain a ‘transcriptional memory’, some of which correlate with the TGP-linked alterations in morphological and physiological traits in response to MaE stress. Notably, progeny from MaE-exposed lineages demonstrate enhanced fitness when re-exposed to MaE. These results enhance our comprehension of the adaptive significance of TGP in plants and suggest feasible approaches for utilizing duckweed’s TGP in the bioremediation of detrimental algal blooms.
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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".