Naturalized Escherichia coli in Wastewater and the Co-evolution of Bacterial Resistance to Water Treatment and Antibiotics
Bibliographic record
Abstract
Antibiotic resistance represents one of the most pressing concerns facing public health today. While the current antibiotic resistance crisis has been driven primarily by the anthropogenic overuse of antibiotics in human and animal health, recent efforts have revealed several important environmental dimensions underlying this public health issue. Antibiotic resistant (AR) microbes, AR genes, and antibiotics have all been found widespread in natural environments, reflecting the ancient origins of this phenomenon. In addition, modern societal advancements in sanitation engineering (i.e., sewage treatment) have also contributed to the dissemination of resistance, and concerningly, may also be promoting the evolution of resistance to water treatment. This is reflected in the recent characterization of naturalized wastewater strains of Escherichia coli —strains that appear to be adapted to live in wastewater (and meat packing plants). These strains carry a plethora of stress-resistance genes against common treatment processes, such as chlorination, heat, UV light, and advanced oxidation, mechanisms which potentially facilitate their survival during sewage treatment. These strains also carry an abundance of common antibiotic resistance genes, and evidence suggests that resistance to some antibiotics is linked to resistance to treatment (e.g., tetracycline resistance and chlorine resistance). As such, these naturalized E. coli populations may be co-evolving resistance against both antibiotics and water treatment. Recently, extraintestinal pathogenic strains of E. coli (ExPEC) have also been shown to exhibit phenotypic resistance to water treatment, seemingly associated with the presence of various shared genetic elements with naturalized wastewater E. coli . Consequently, some pathogenic microbes may also be evolving resistance to the two most important public health interventions for controlling infectious disease in modern society—antibiotic therapy and water treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".