Direct Quantification of <i>Campylobacter jejuni</i> and <i>Campylobacter lanienae</i> in Feces of Cattle by Real-Time Quantitative PCR
Bibliographic record
Abstract
Campylobacter species are fastidious to culture, and the ability to directly quantify biomass in microbiologically complex substrates using real-time quantitative (RTQ) PCR may enhance our understanding of their biology and facilitate the development of efficacious mitigation strategies. This study reports the use of nested RTQ-PCR to directly quantify Campylobacter jejuni and Campylobacter lanienae in cattle feces. For C. jejuni, the single-copy mapA gene was selected. For C. lanienae, the three-copy 16S rRNA gene was targeted. RTQ-PCR primers were tested alone or they were nested with species-specific primers, and amplification products were detected using the intercalating dye SYBR Green. Nesting did not increase the specificity or sensitivity of C. jejuni quantification, and the limit of quantification was 19 to 25 genome copies ( approximately 3 x 10(3) CFU/g of feces). In contrast, nested RTQ-PCR was necessary to confer specificity on C. lanienae by targeting the 16S rRNA gene. The limit of quantification was 1.8 genome copies ( approximately 250 CFU/g of feces), and there was no discernible difference between the two C. lanienae secondary primer sets evaluated. Detection and quantification of C. jejuni in naturally infested cattle feces by RTQ-PCR were comparable to the results of culture-based methods. In contrast, culturing did not detect C. lanienae in 6 of 10 fecal samples positive for the bacterium and substantially underestimated cell densities relative to nested RTQ-PCR. The results of this study illustrate that RTQ-PCR can be used to directly quantify campylobacters, including very fastidious species, in a microbiologically and chemically complex substrate. Furthermore, targeting of a multicopy universal gene provided highly sensitive quantification of C. lanienae, but nested RTQ-PCR was necessary to confer specificity. This method will facilitate subsequent studies to elucidate the impact of this group of bacteria within the gastrointestinal tracts of livestock and studies of the factors that influence colonization success and shedding.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".