Effect of sub-inhibitory antibiotic exposure on antimicrobial resistance of river biofilm microbial communities
Bibliographic record
Abstract
Biofilms are ubiquitous throughout aquatic environments and can be influenced by myriad\nfactors including antimicrobial run-off from anthropogenic sources. The South Saskatchewan\nRiver is an oligotrophic system that receives discharge from urban wastewater treatment plants\n(WWTP) and agricultural effluents which can carry antimicrobial residues. Antibiotic\nconcentrations in environmental systems generally occur at low or sub-minimum inhibitory\nconcentrations (sub-MICs). Most of our current understanding of antibiotic resistance comes from\nclinically relevant monoculture studies. Thus, there is a need for experimental data that explores\nthe response of naturally occurring multispecies microbial communities.\nThis thesis employs a “structure-function” approach by using microscopic and metagenomic\nmethods to characterize the effects of sub-MIC antibiotics in riverine biofilm communities. I aimed\nto determine whether exposure induced a selective pressure for antibiotic resistance resulting in\nvariations of overall community composition. For this purpose, riverine biofilm communities were\ndeveloped in a microcosm system under various sub-MIC exposure treatments including constant\nsub-MIC exposure (1/10, 1/50 and 1/100 MIC) to a mix of common antibiotics (ciprofloxacin,\nstreptomycin, and oxytetracycline), and residual antibiotic concentrations present in WWTP\neffluent and swine-manure (SM). This research was divided into two microcosm experiments: a\npilot experiment and a full-scale experiment. Microscopic methods were used to characterize the\nstructural composition of biofilms, and different metagenomic tools were evaluated and compared\nseeking to elucidate comprehensive microbiome and resistome profiles in biofilm communities.\nResults of this thesis research demonstrated shifts in biofilm architecture, microbiome and\nresistome composition. Biofilm formation and accumulation of extracellular polymeric substances\n(EPS) were inversely proportional to the concentration of antibiotics. Microbial diversity wasiv\nreduced after sub-MIC antibiotic exposure, selecting for Pseudomonadota (synonym\nProteobacteria) species, particularly at the sub-MIC 1/10 condition. The biofilm resistome\nconsisted of antibiotic resistance genes (ARGs) that conferred resistance to aminoglycosides,\ntetracyclines, β-lactams, macrolides, phenicols and sulfonamides and trimethoprim. Resistome\nrelative abundance and diversity was consistently higher in biofilms grown under sub-MIC\nantibiotic exposure. Nonetheless, ARGs and virulence genes were observed across all samples\nincluding biofilms grown under non-antibiotic conditions. Correlation between the microbiome\nand resistome showed that aminoglycoside ARGs were associated with several bacterial genera,\nand co-occurrence between virulence factors and ARGs was also significant.\nFunctional prediction analysis indicated that abundance of metabolic pathways involved in\ncell-wall metabolism increased under the presence of sub-MIC antibiotics, thus supporting the\nnotion that low concentrations of antimicrobials exert selective pressure. Overall, results from our\nwhole-community approach demonstrated that the presence of sub-MICs antibiotics increased the\nabundance of ARGs and resistome-related functions. These responses indicate that riverine biofilm\ncommunities promote the prevalence and facilitate the transmission of antimicrobial resistance.
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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 | 0.001 |
| 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".