An improved method with a wider applicability to isolate plant mitochondria for mtDNA extraction
Bibliographic record
Abstract
BACKGROUND: Mitochondria perform a principal role in eukaryotic cells. Mutations in mtDNA can cause mitochondrial dysfunction and are frequently associated with various abnormalities during plant development. Extraction of plant mitochondria and mtDNA is the basic requirement for the characterization of mtDNA mutations and other molecular studies. However, currently available methods for mitochondria isolation are either tissue specific or species specific. Extracted mtDNA may contain substantial chloroplast DNA (cpDNA) and nuclear DNA (nDNA) and its end use efficiency can be reduced. Clearly, an effective mitochondria isolation method is warranted with wider applicability and with minimum contamination from cpDNA and nDNA. RESULTS: Here we reported an improved method for isolating mitochondria from dry wheat seeds and its extension to dead seeds, viable seeds, etiolated leaf tissue and several other plant species: oat, Arabidopsis, flax, and yellow mustard. The isolated mitochondria were successfully used to extract mtDNA with QIAamp DNA mini kit (Qiagen). The extracted mtDNA from the assayed samples of these species was intact in large quantity and showed little contamination from nDNA, cpDNA, RNA, and proteins. The mtDNA extracted from dead wheat seeds was also substantial, but more degraded and less intact when compared to those from viable seeds and other tissues. CONCLUSION: The improved method was successfully applied to isolate mitochondria and extract mtDNA from several different tissues and plant species. The major advance in the improvement lies in its wider application with the same mitochondria extraction medium to different tissues and species. The improvement is significant, as it helps to widen the scope of future plant mitochondria research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".