Effect of sub-inhibitory antibiotic exposure on antimicrobial resistance of river biofilm microbial communities
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».