A method for assessing efficiency of bacterial cell disruption and DNA release
Bibliographic record
Abstract
BACKGROUND: DNA-based testing is becoming the preferred method both for identifying microorganisms and for characterizing microbial communities. However, no single DNA extraction method exists that is suitable for all types of microorganisms because bacteria are variable in their susceptibility to lysis by available extraction procedures. To develop and test new DNA extraction procedures, it would be helpful to determine their efficiencies. While the amount of extracted DNA can readily be measured by different methods, calculation of true efficiency requires knowledge of the initial amount of DNA in the starting bacterial sample, which cannot be done with precision by any existing method. In the process of developing a new extraction procedure, we developed a method that can be used to determine the total amount of both DNA and RNA in bacteria. The amount of DNA can be calculated from the amount of purines released after mild acid and alkali treatment. The amount of RNA in the same extract can also be calculated from the amount of ribonucleoside monophosphates. The released purines and ribonucleoside monophosphates can be quantified by absorbance using HPLC, with reference to appropriate standards. RESULTS: The acid/HPLC method was used to measure the efficiency of commonly used bead-beating and chemical protocols for releasing DNA from a particularly hardy organism, Mycobacterium smegmatis as well as several other species (Bacillus subtilis vegetative cells and spores; Francisella philomiragia; Pseudomonas aeruginosa; Moraxella catarrhalis; Bacillus thuringiensis; Staphylococcus aureus). Surprisingly large differences in efficiency between methods were found. CONCLUSIONS: The acid/HPLC method is a new tool to determine DNA extraction efficiencies and should aid in the development of improved protocols for releasing DNA from a broad range of microorganisms.
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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 | 0.000 |
| 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) | 0.000 | 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".