Invasion dynamics of antimicrobial-resistant E. coli in river biofilms: impacts on the resistome, microbiomes, and horizontal gene transfer
Bibliographic record
Abstract
Abstract River biofilms are exposed to invasion by antibiotic resistant bacteria (ARB) due to episodic or chronic exposure to wastewater, yet the ecological processes determining the fate of invaders and their resistance plasmids remain poorly understood. We experimentally exposed river-grown biofilms, originating from sites with contrasting microbial diversity and wastewater influence, to invasion by a genetically tagged ARB- E. coli carrying a transferable IncPα plasmid with the nptII resistance gene. Over two weeks, we quantified the dynamics of the invader and its plasmid, using qPCR and the plasmid-to-strain genome ratio in the biofilm as an indicator of horizontal gene transfer (HGT). We further characterized microbiomes and resistomes via 16S rRNA gene sequencing and metagenomics. Independent quantification methods provided highly consistent estimates of invasion dynamics: the invader established transiently across all biofilms, with ARB- E. coli abundance peaking within 48h and subsequently declining to near-background levels within 14 days. Plasmid-to-strain genome ratios decreased, indicating limited HGT and progressive plasmid loss. Wastewater-impacted biofilms showed slower declines, suggesting higher plasmid persistence potential in disturbed environments. The total community resistome exhibited pronounced but short-lived shifts whereas indigenous resistomes and microbiome composition remained stable. Yet, in one replicate from the wastewater-impacted site, specific indigenous ARGs of public health relevance increased, indicating that disturbance can promote localized ARG proliferation even without sustained invader establishment. Our results show that interactions between invaders and indigenous biofilm are dynamic and strongly shaped by community composition. This supports the One Health concept, highlighting how environmental context modulates AMR propagation risks in freshwater ecosystems.
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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 | 0.001 |
| 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".