Nickel Toxicity Targets Cell Wall-Related Processes and PIN2-Mediated Auxin Transport to Inhibit Root Elongation and Gravitropic Responses in Arabidopsis
Bibliographic record
Abstract
Contamination of soils with heavy metals, such as nickel (Ni), is a major environmental concern due to increasing pollution from industrial activities, burning of fossil fuels, incorrect disposal of sewage sludge, excessive manure application and the use of fertilizers and pesticides in agriculture. Excess Ni induces leaf chlorosis and inhibits plant growth, but the mechanisms underlying growth inhibition remain largely unknown. A detailed analysis of root development in Arabidopsis thaliana in the presence of Ni revealed that this heavy metal induces gravitropic defects and locally inhibits root growth by suppressing cell elongation without significantly disrupting the integrity of the stem cell niche. The analysis of auxin-responsive reporters revealed that excess Ni inhibits shootward auxin distribution. Furthermore, we found that PIN2 is very sensitive to Ni, as the presence of this heavy metal rapidly reduced PIN2 levels in roots. A transcriptome analysis also showed that Ni affects the expression of many genes associated with plant cell walls and that Ni-induced transcriptional changes are largely independent of iron (Fe). In addition, we raised evidence that excess Ni increases the accumulation of reactive oxygen species and disturbs the integrity and orientation of microtubules. Together, our results highlight which processes are primarily targeted by Ni to alter root growth and development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".