Optimizing a PCR protocol for cpn60-based microbiome profiling of samples variously contaminated with host genomic DNA
Bibliographic record
Abstract
BACKGROUND: The current recommended protocol for chaperonin-60 (cpn60) universal target based microbiome profiling includes universal PCR of microbiome samples across an annealing temperature gradient to maximize the diversity of sequences amplified. However, the value of including this gradient approach has not been formally evaluated since the optimization of a modified universal PCR primer cocktail for cpn60 PCR. PCR conditions that maximize representation of the microbiome while minimizing PCR-associated distortion of the community structure, especially in samples containing large amounts of host genomic DNA are critical. The goal of this study was to measure the effects of PCR annealing temperature and the ratio of host to bacterial DNA on the outcome of microbiota analysis, using pig microbiota as a model environment. FINDINGS: Six samples were chosen with an anticipated range of ratios of pig to bacterial genomic DNA, and universal cpn60 PCR amplification with an annealing temperature gradient was used to create libraries for pyrosequencing, resulting in 426,477 sequences from the six samples. The sequences obtained were classified as target (cpn60) or non-target based on the percent identity of their closest match to the cpnDB reference database, and target sequences were further processed to create microbiome profiles for each sample at each annealing temperature. Annealing temperature affected the amount of PCR product generated, with more product generated at higher temperatures. Samples containing proportionally more host genomic DNA yielded more non-target reads, especially at lower annealing temperatures. However, microbiome composition for each sample across the annealing temperature gradient remained consistent at both the phylum and operational taxonomic unit levels. Although some microbial sequences were detected at only one annealing temperature, these sequences accounted for a minority of the total microbiome. CONCLUSIONS: These results indicate that PCR annealing temperature does have an affect on cpn60 based microbiome profiles, but that most of the differences are due to differences in detection of low abundance sequences. Higher annealing temperatures resulted in larger amounts of PCR product and lower amounts of non-target sequence amplification, especially in samples containing proportionally large amounts of host DNA. Taken together these results provide important information to guide decisions about experimental design for cpn60 based microbiome studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".