DNA extraction procedure affects organic-aggregate-attached bacterial community profiles from a shallow eutrophic lake
Bibliographic record
Abstract
Organic aggregates (OA) in aquatic ecosystems harbour diverse microbial communities. The colonization and growth of OA-attached bacteria are important processes in the degradation and transformation of the particles. The development of efficient and comparative DNA extraction methods is one of the most critical steps in the study of the composition and diversity of OA-attached bacterial communities. To evaluate whether different DNA extraction procedures affect the measurement of bacterial community composition, we compared four in situ lysis procedures using OA from three locations in a shallow eutrophic lake (Lake Taihu, China). The extracted DNA was analyzed using denaturing gradient gel electrophoresis profiles. We found that the choice of DNA extraction protocol had a significant influence on the fingerprints of the OA-attached bacterial community. This was shown not only in the number of bands but also in their relative representation of certain DNA bands. Using the bead-beating DNA extraction method in the presence of hexadecyltrimethylammonium bromide, we found that crude microbial DNA could be extracted efficiently from different OA types. This protocol is reproducible and gives very pure DNA of relatively high molecular mass. More importantly, the protocol provided more representative and informative data on the diversity of OA-attached bacterial communities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".