Application of the <scp>LU</scp>minometric <scp>M</scp>ethylation <scp>A</scp>ssay to ecological species: tissue quality requirements and a survey of <scp>DNA</scp> methylation levels in animals
Bibliographic record
Abstract
The LUminometric Methylation Assay (LUMA) measures global DNA methylation. LUMA depends on digestion of DNA with methyl-sensitive and methyl-insensitive restriction enzymes, followed by pyrosequencing. Until recently, LUMA has been principally used for biomedical research. Here, we use chickens as a model to investigate sample quality issues relating to LUMA and then apply the method to ecological species. First, we assessed the effect of tissue storage conditions on DNA methylation values. This is an important consideration for ecological species because samples are not always ideally preserved and LUMA is sensitive to poor DNA quality. We found that good quality LUMA data could be obtained from chicken liver and brain tissues stored at 21 °C for at least 2 and 12 h, respectively. Longer storage times introduced nonspecific peaks to pyrograms which were associated with reduced DNA methylation. Repeatedly, freezing and thawing the tissues did not affect LUMA data. Second, we measured DNA methylation in 12 species representing five animal classes: amphibians (African and Western clawed frog), reptiles (green anole lizard), fish (yellow perch, goldfish, lake trout), mammals (American mink, polar bear, short-beaked common dolphin, Atlantic white-sided dolphin) and birds (chicken, Japanese quail). We saw a pattern of high DNA methylation in fish (84-87%), and intermediate levels in mammals (68-72%) and birds (52-71%). This pattern corresponds well with previous measures of DNA methylation generated by HPLC. Our data represent the first CpG methylation values to be reported in several species and provide a basis for studying patterns of epigenetic inheritance in an ecological context.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".