Antibiotic resistome and microbial community structure during anaerobic co-digestion of food waste, paper and cardboard
Bibliographic record
Abstract
Solid organic waste is a significant source of antibiotic resistance genes (ARGs) and effective treatment strategies are urgently required to limit the spread of antimicrobial resistance. Here, we studied ARG diversity and abundance as well as the relationship between antibiotic resistome and microbial community structure within a lab-scale solid-state anaerobic digester treating a mixture of food waste, paper and cardboard. A total of 10 samples from digester feed and digestion products were collected for microbial community analysis including small subunit rRNA gene sequencing, total community metagenome sequencing and high-throughput quantitative PCR. We observed a significant shift in microbial community composition and a reduction in ARG diversity and abundance after 6 weeks of digestion. ARGs were identified in all samples with multidrug resistance being the most abundant ARG type. Thirty-two per cent of ARGs detected in digester feed were located on plasmids indicating potential for horizontal gene transfer. Using metagenomic assembly and binning, we detected potential bacterial hosts of ARGs in digester feed, which included Erwinia, Bifidobacteriaceae, Lactococcus lactis and Lactobacillus. Our results indicate that the process of sequential solid-state anaerobic digestion of food waste, paper and cardboard tested herein provides a significant reduction in the relative abundance of ARGs per 16S rRNA gene.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".