Development of two microbial source tracking markers for detection of wastewater-associated <i>Escherichia coli</i> isolates
Bibliographic record
Abstract
ABSTRACT Escherichia coli has been used as an indicator of fecal pollution in environmental waters. However, its presence in environmental waters does not provide information on the source of water pollution. Identifying the source of water pollution is paramount to be able to effectively reduce contamination. The present study aimed to identify E. coli microbial source tracking (MST) markers that can be used to identify domestic wastewater contamination in environmental waters. We first analyzed wastewater E. coli genomes sequenced by us (n = 50) and RefSeq animal E. coli genomes of fecal origin (n = 82), and identified 144 candidate wastewater-associated marker genes. The sensitivity and specificity of the candidate marker genes were then assessed by screening the genes in 335 RefSeq wastewater E. coli genomes and 3,318 RefSeq animal E. coli genomes. We finally identified two MST markers, namely W_nqrC and W_clsA_2, which could be used for detection of wastewater-associated E. coli isolates. These two markers showed higher performance than the previously developed human wastewater-associated E. coli markers H8 and H12. When used in combination, W_nqrC and W_clsA_2 showed specificity of 98.9% and sensitivity of 25.7%. PCR assays to detect W_nqrC and W_clsA_2 were also developed and validated. The developed PCR assays are potentially useful for detecting E. coli isolates of wastewater origin in environmental waters, though users should keep in mind that the sensitivity of these markers is not high. Further studies are needed to assess the applicability of the developed markers to a culture-independent approach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".