Identification and Application of Phocaeicola-Specific Conserved Signature DNA Markers for Human Fecal Source Tracking
Bibliographic record
Abstract
A major goal of fecal pollution monitoring in the environment is to identify point sources of fecal contamination that may pose potential health risks due to animal- and human-specific pathogens. Ideal source tracking markers should have high host specificity and can be employed for the unambiguous identification of the host/fecal point sources. Conserved signature proteins (CSPs) are a class of unique, phylogenetically coherent indicators that are specific to a given taxon (e.g., genus or species). In this study, we report the identification and characterization of a new CSP, whose gene (designated as CSP-DV) is present in a single copy, and for whom homologs showing a high degree of sequence similarity are found only in genomes of Phocaeicola dorei and Phocaeicola vulgatus, two commensal species commonly found in the human gut and feces. We developed a qPCR method targeting this CSP gene to explore its usefulness as a human source tracking marker. We confirmed that the CSP-DV marker showed an absolute human sensitivity (100%) but some cross-reactivities in chicken, cats, dogs, rabbits, and rodents. In recreational water, the CSP-DV marker gene levels were well correlated with those of HF183, a well-validated human marker that predominantly targets the 16S rRNA gene of P. dorei, suggesting that it can be a new potential source tracking tool for human fecal contamination in specific environmental waters. In summary, our CSP-DV marker targets Phocaeicola clade-specific microbes and can provide an additional approach independent of the 16S rRNA gene to detect human sources of fecal pollution.
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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".