Expression of Genes Associated with Nickel Resistance in White Spruce (<i>Picea glauca</i>) under Nickel Stress: Analysis of AT2G16800 and <i>NRAMP</i> Genes
Bibliographic record
Abstract
Heavy metals such nickel (Ni) can cause toxicity by 1) displacing essential components in the biomolecules, 2) blocking the functional group of molecules, or 3) modifying enzymes, proteins, the plasma membrane, and membrane transporters. The main objective of the present study was to investigate the effect of nickel (Ni) on gene expression of nitrate on gene expression with a focus on the genes coding for the high affinity Ni transporter family protein AT2G16800, and natural resistance-associated macrophage protein (NRAMP). Ni toxicity was assessed by treating seedlings with an aqueous solution of nickel nitrate salt [Ni(NO3)2] at the concentrations of 150 mg, 800 mg, and 1600 mg of nickel per 1 kg of dry soil. RT-qPCR was used to measure the expression of AT2G16800, and NRAMP genes in samples treated with nickel nitrates and controls. The results revealed that P. glauca is resistant to Ni based on lack of plant damage at all nickel concentrations. Ni has no effect on the expression of the AT2G16800 gene in needles or roots. However, it induced an upregulation of the NRAMP genes in roots at all the doses tested (150 mg/kg, 800 mg/kg, and 1600 mg/kg). On the other hand, Ni has no effect on the expression of the NRAMP gene in needle but the lowest dose of potassium (150 mg/kg) upregulated this gene in needle tissues.
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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".