Temporal Dynamics and Impact of Manure Storage on Antibiotic Resistance Patterns and Population Structure of <i>Escherichia coli</i> Isolates from a Commercial Swine Farm
Bibliographic record
Abstract
Many confined-livestock farms store their wastes for several months prior to use as a fertilizer. Storing manure for extended periods could significantly bias the composition of enteric bacterial populations subsequently released into the environment. Here, we compared populations of Escherichia coli isolated from fresh feces and from the manure-holding tank (stored manure) of a commercial swine farm, each sampled monthly for 6 months. The 4,668 confirmed E. coli isolates were evaluated for resistance to amikacin, ampicillin, cephalothin, chloramphenicol, kanamycin, nalidixic acid, streptomycin, sulfamethoxazole, tetracycline, trimethoprim, and trimethoprim plus sulfamethoxazole. A subset of 1,687 isolates was fingerprinted by repetitive extragenic palindromic PCR (rep-PCR) with the BOXA1R primer to evaluate the diversity and the population structure of the collection. The population in the stored manure was generally more diverse than that in the fresh feces. Half of the genotypes detected in the stored manure were never detected in the fresh fecal material, and only 16% were detected only in the fresh feces. But the majority of the isolates (84%) were assigned to the 34% of genotypes shared between the two environments. The structure of the E. coli population showed important monthly variations both in the extent and distribution of the diversity of the observed genotypes. The frequency of detection of resistance to specific antibiotics was not significantly different between the two collections and varied importantly between monthly samples. Resistance to multiple antibiotics was much more temporally dynamic in the fresh feces than in the stored manure. There was no relationship between the distribution of rep-PCR fingerprints and the distribution of antibiotic resistance profiles, suggesting that specific antibiotic resistance determinants were dynamically distributed within the population.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".