Comparison of commercial DNA extraction kits for isolation and purification of bacterial and eukaryotic DNA from PAH-contaminated soils
Bibliographic record
Abstract
Molecular characterization of the microbial populations of soils and sediments contaminated with polycyclic aromatic hydrocarbons (PAHs) is often a first step in assessing intrinsic biodegradation potential. However, soils are problematic for molecular analysis owing to the presence of organic matter, such as humic acids. Furthermore, the presence of contaminants, such as PAHs, can cause further challenges to DNA extraction, quantification, and amplification. The goal of our study was to compare the effectiveness of four commercial soil DNA extraction kits (UltraClean Soil DNA Isolation kit, PowerSoil DNA Isolation kit, PowerMax Soil DNA Isolation kit, and FastDNA SPIN kit) to extract pure, high-quality bacterial and eukaryotic DNA from PAH-contaminated soils. Six different contaminated soils were used to determine if there were any biases among the kits due to soil properties or level of contamination. Extracted DNA was used as a template for bacterial 16S rDNA and eukaryotic 18S rDNA amplifications, and PCR products were subsequently analyzed using denaturing gel gradient electrophoresis (DGGE). We found that the FastDNA SPIN kit provided significantly higher DNA yields for all soils; however, it also resulted in the highest levels of humic acid contamination. Soil texture and organic carbon content of the soil did not affect the DNA yield of any kit. Moreover, a liquid-liquid extraction of the DNA extracts found no residual PAHs, indicating that all kits were effective at removing contaminants in the extraction process. Although the PowerSoil DNA Isolation kit gave relatively low DNA yields, it provided the highest quality DNA based on successful amplification of both bacterial and eukaryotic DNA for all six soils. DGGE fingerprints among the kits were dramatically different for both bacterial and eukaryotic DNA. The PowerSoil DNA Isolation kit revealed multiple bands for each soil and provided the most consistent DGGE profiles among replicates for both bacterial and eukaryotic DNA.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".