The Critical Role of DNA Extraction for Detection of Mycobacteria in Tissues
Bibliographic record
Abstract
BACKGROUND: Nucleic acid-based methods offer promise for both targeted and exploratory investigations of microbes in tissue samples. As the starting material for such studies is a mixture of host and microbial DNA, we have critically evaluated the DNA extraction step to determine the quantitative and qualitative parameters that permit faithful molecular detection of mycobacteria in infected tissue. Specifically, we assessed: 1) tissue disruption procedures; 2) DNA extraction protocols; and 3) inhibition of bacterial PCR by host DNA. PRINCIPAL FINDINGS: Regarding DNA extraction, we found that 1) grinding was not necessary if bead-beating is done, 2) the reference mycobacterial DNA extraction method recovered more pure DNA than commercial spin column kits, 3) lysozyme digestion of 1 hour was sufficient, and 4) repeated steps of phenol:chloroform:isoamyl alcohol offered minimal gain in DNA quality. By artificially mixing mycobacterial DNA with DNA extracted from uninfected mice, we found that bacterial real-time quantitative PCR was only reliable when the quantity of host DNA was < 3 µg in a final volume of 25 µl and the quality was high (260/280 nm ratio = 1.89 ± 0.08). Findings from spiked DNA studies were confirmed using DNA extracted from mice infected with different intracellular pathogens (M. tuberculosis, M. avium subsp. paratuberculosis). CONCLUSIONS: Our findings point to the most appropriate methods for extracting DNA from tissue samples for the purpose of detecting and quantifying mycobacteria. These data also inform on the limits of detection for two mycobacterial species and indicate that increasing the sample mass to improve analytic sensitivity comes at the cost of inhibition of PCR by host 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".