Molecular analysis of Betula papyrifera populations from a mining reclaimed region: genetic and transcriptome characterization of metal resistant and susceptible genotypes
Bibliographic record
Abstract
The objectives of the present study were to; 1) determine if there’s an association between plant \npopulation diversity and genetic variation in white birch (Betula papyrifera) populations with soil \nmetal contamination in the Greater Sudbury region (GSR), 2) assess if metal contamination and \nsoil liming has an effect on global DNA methylation, 3) develop and characterize the \ntranscriptome of B. papyrifera under nickel stress and, 4) assess gene expression dynamics in \nwhite birch in response to nickel stress. No association between plant population diversity and \ngenetic variation with metal contamination was found. Liming increases plant population \ndiversity but has no effect on genetic variation in the studied white birch populations. There was \na decrease in root cytosine methylation in metal-contaminated sites compared to references. \nTreatment with the dose corresponding to total level of Ni or Cu (1,600 mg/kg Ni, 1,312 mg/kg \nCu or combined) in sites of the GSR generated different responses within segregating populations \nanalyzed. The main Ni resistance mechanism of white birch was associated with the prevention \nof translocation of Ni from root to shoot. We also observed lower ZAT11 and glutathione \nreductase expression in resistant genotypes compared to susceptible. The transcriptome of B. \npapyrifera was developed for the first time using Next Generation Sequencing. RNA from Ni \nresistant, moderately-susceptible, susceptible and water controls treatment was sequenced. A total \nof 209,802 trinity genes were identified and were assembled to 278,264 total trinity transcripts. In \ntotal, 215,700 transcripts were annotated and compared to the published B. nana genome. \nOverall, a genomic match for 61% transcripts with the reference genome was found. Expression \nprofiles were generated and 62,587 genes were found to be significantly differentially expressed \namong the nickel resistant, susceptible, and untreated libraries. The main nickel resistance \nmechanism in B. papyrifera is a downregulation of genes associated with translation and cell growth, and upregulation in genes involved in the plasma membrane. Seven candidate genes \nassociated to nickel resistance were identified. They include Glutathione S-transferase, \nThioredoxin, Putative transmembrane protein, Nramp transporter, TonB-like family protein and \nTonB-like dependent receptor. This TonB receptor was found to be exclusive to the Betula genus.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".