Review of Antibiotic Resistance Genes (ARGs) in Salmon Aquaculture and Empirical Data on Spatial and Seasonal Trends in the Bay of Fundy
Notice bibliographique
Résumé
The purpose of this paper is to review the background information available for antibiotic microbial resistance (AMR) in the aquaculture sector and to provide empirical data on the presence of antibiotic resistant genes (ARGs) associated with Atlantic salmon farms in the Bay of Fundy. The continual decline of wild fish stocks worldwide and increasing demand for fish from increasing human populations and rising per capita consumption has resulted in the increasing production of the aquaculture industry to meet the global demand. However, the higher culture densities of intensive fish farms in comparison to wild conspecifics provides a prime environment for the spread of bacterial diseases, which pose a significant fish health and financial threat to the aquaculture industry. These diseases are generally controlled with antibiotics, but rising resistance to these drugs by bacteria that could be transferred to human pathogens in some parts of the world is becoming more common, raising concerns about the cost to human health, national economies and highlighting the need for research into alternative treatments. Resistance develops through the propagation of ARGs by natural selection of certain pathogens, and the sharing of ARGs between bacteria by horizontal transfer. Along with pathogens in the fish gut, environmental bacteria are also exposed to antibiotics in the water and sediments in the vicinity of fish farms, creating a further risk of the spread of ARGs to human pathogens from this vector. There have been cases of antibiotic resistance in human pathogens linked to the use of antibiotics in aquaculture and agriculture, and so many countries have imposed legislation over the use of antibiotics, with various levels of success. Finally, there have been attempts into using alternative treatments to control the spread of infectious disease in aquaculture, including bacteriophage therapy, quorum sensing inhibitors, vaccines, probiotics, immunostimulants, and herbal therapy. Empirical data from the Bay of Fundy show that Atlantic salmon aquaculture farms are hotspots for microbial activity and that bacterial populations differ in their community structure depending on their proximity to the fish farms. The classes of bacterial populations closest to the farm generally tend to be anaerobic and sulphur reducers. We sampled for the ARGs related to the drugs florfenicol, tetracycline and sulfonamide. There was a general trend in increasing relative ARG abundance close to the farm, but the patterns were not the same for all ARGs. There also seemed to be a decrease in the concentration of some ARGs over time ranging from 3 to 12 months. The information gathered from the literature review and the conclusions of the empirical study looking for AMR in relation to salmon farming showed that the process of modifying/enhancing ARGs at salmon aquaculture farms is present. This is consistent with other studies, both terrestrial and aquatic, that show AMR in bacterial populations respond to anthropogenic activities that involve the use of antibiotics. These data are some of the first in Canada to look at AMR in relation to aquaculture. While they give some insight into levels of ARGs and the environment, the scale (spatial and temporal) at which this is happening and the implications for the probable transmission to humans through the food supply is yet unknown. Further research is required on this topic in order to better define scales of AMR in comparison with other known reservoirs (e.g., wastewater treatment plants, agriculture activities), the degree of spatial dispersion involved of ARGs, linkages with wild populations of organisms that are part of the human food chain and the probability of transmission of ARGs to pathogens affecting human or animal health. Once a better understanding is gained on these aspects, a proper risk assessment can be done on aquaculture activities to answer questions such as: appropriate treatment regimes, probable impacts on the environment, implications of site selection and overall risk to human health.
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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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,007 | 0,010 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».