DNA methylation analysis of Jack pine (Pinus banksiana) under nickel stress reveals a highly targeted epigenetic response
Bibliographic record
Abstract
Plants use DNA methylation to quickly adjust gene activity in response to environmental stressors helping them adapt and survive. Conifers have large genomes and unique metal responses, making conifer DNA methylation research highly compelling. This study evaluated how excess nickel exposure affects methylation patterns and identified differentially methylated regions (DMRs) in Pinus banksiana . Reduced representation bisulfite sequencing (RRBS) was used to assess global methylation changes in nickel-treated seedlings. Nickel sulfate minimally affected overall global methylation; however, it induced significant levels of methylation within specific, localized regions of the genome. The increase in global CG methylation was largely due to hypermethylated DMRs that had methylation levels exceeding 80 %. The observed global hypermethylation was localized predominantly to constitutively hypermethylated regions. A total of 1173 DMRs were found to be hypermethylated, while 239 were hypomethylated. Notably, 97 % of these DMRs were intergenic and displayed high levels of CG methylation, leading to the suggestion that they may be located within transposable elements (TEs). All gene-annotated Differentially Methylated Regions (DMRs) spanned two distinct areas of the gene body, potentially suggesting a strong bias toward gene upregulation. DMR-covered genes, associated with stress mitigation, encode proteins including DEAD-box ATP-dependent RNA helicase, calcium/calmodulin-dependent protein kinase, and UDP-glycosyltransferase. Methylation analyses reveal epigenetic changes that can help discover how P. banksiana resist nickel and adapt long-term to contamination. For this study, RRBS was a suitable compromise because it allowed for precise analysis of specific, relevant regions.
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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".