A fast, highly sensitive double‐nested PCR‐based method to screen fish immunobiomes
Bibliographic record
Abstract
Efficient methods for constructing 16S tag amplicon libraries for pyrosequencing are needed for the rapid and thorough screening of infectious bacterial diversity from host tissue samples. Here we have developed a double-nested PCR methodology that generates 16S tag amplicon libraries from very small amounts of bacteria/host samples. This methodology was tested for 133 kidney samples from the lake whitefish Coregonus clupeaformis (Salmonidae) sampled in five different lake populations. The double-nested PCR efficiency was compared with two other PCR strategies: single primer pair amplification and simple nested PCR. The double-nested PCR was the only amplification strategy to provide highly specific amplification of bacterial DNA. The resulting 16S amplicon libraries were synthesized and pyrosequenced using 454 FLX technology to analyse the variation of pathogenic bacteria abundance. The proportion of the community sequenced was very high (Good's coverage estimator; mean = 95.4%). Furthermore, there were no significant differences of sequence coverage among samples. Finally, the occurrence of chimeric amplicons was very low. Therefore, the double-nested PCR approach provides a rapid, informative and cost-effective method for screening fish immunobiomes and most likely applicable to other low-density microbiomes as well.
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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 | 0.001 | 0.000 |
| 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 | 0.002 |
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".