Comparison of Ileum Microflora of Pigs Fed Corn-, Wheat-, or Barley-Based Diets by Chaperonin-60 Sequencing and Quantitative PCR
Bibliographic record
Abstract
We have combined the culture-independent methods of high-throughput sequencing of chaperonin-60 PCR product libraries and quantitative PCR to profile and quantify the small-intestinal microflora of pigs fed diets based on corn, wheat, or barley. A total of 2,751 chaperonin-60 PCR product clones produced from samples of ileum digesta were examined. The majority (81%) of these clones contained sequences independently recovered from all three libraries; 372 different nucleotide sequences were identified, but only 14% of the 372 different sequences were recovered from all three libraries. Taxonomic assignments of the library sequences were made by comparison to a reference database of chaperonin-60 sequences combined with phylogenetic analysis. The taxa identified are consistent with previous reports of pig ileum microflora. Frequencies of each sequence in each library were calculated to identify taxa that varied in frequency between the corn, barley, and wheat libraries. The chaperonin-60 sequence inventory was used as a basis for designing PCR primer sets for taxon-specific quantitative PCR. Results of quantitative PCR analysis of ileum digesta confirmed the relative abundances of targeted taxa identified with the library sequencing approach. The results of this study indicate that chaperonin-60 clone libraries can be valid profiles of complex microbial communities and can be used as the basis for producing quantitative PCR assays to measure the abundance of taxa of interest during experimentally induced or natural changes in a community.
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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 | 0.000 |
| 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 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".