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. 2024 Jan 22;13(1):107.
doi: 10.3390/antibiotics13010107.

Source Attribution of Antibiotic Resistance Genes in Estuarine Aquaculture: A Machine Learning Approach

Helena Sofia Salgueiro  1 , Ana Cristina Ferreira  2   3 , Ana Sofia Ribeiro Duarte  4 , Ana Botelho  2
Affiliations

Source Attribution of Antibiotic Resistance Genes in Estuarine Aquaculture: A Machine Learning Approach

Helena Sofia Salgueiro et al. Antibiotics (Basel). .

Abstract

Aquaculture located in urban river estuaries, where other anthropogenic activities may occur, has an impact on and may be affected by the environment where they are inserted, namely by the exchange of antimicrobial resistance genes. The latter may ultimately, through the food chain, represent a source of resistance genes to the human resistome. In an exploratory study of the presence of resistance genes in aquaculture sediments located in urban river estuaries, two machine learning models were applied to predict the source of 34 resistome observations in the aquaculture sediments of oysters and gilt-head sea bream, located in the estuaries of the Sado and Lima Rivers and in the Aveiro Lagoon, as well as in the sediments of the Tejo River estuary, where Japanese clams and mussels are collected. The first model included all 34 resistomes, amounting to 53 different antimicrobial resistance genes used as source predictors. The most important antimicrobial genes for source attribution were tetracycline resistance genes tet(51) and tet(L); aminoglycoside resistance gene aadA6; beta-lactam resistance gene blaBRO-2; and amphenicol resistance gene cmx_1. The second model included only oyster sediment resistomes, amounting to 30 antimicrobial resistance genes as predictors. The most important antimicrobial genes for source attribution were the aminoglycoside resistance gene aadA6, followed by the tetracycline genes tet(L) and tet(33). This exploratory study provides the first information about antimicrobial resistance genes in intensive and semi-intensive aquaculture in Portugal, helping to recognize the importance of environmental control to maintain the integrity and the sustainability of aquaculture farms.

Keywords: antimicrobial resistance; aquaculture sediments; machine learning; resistomes; source attribution.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The 20 most important AMR determinants for classification in RF1.
Figure 2
Figure 2
Distribution of abundance in FPKM values of the 20 most important AMR determinants for classification of aquatic environment in RF1.
Figure 3
Figure 3
The 20 most important AMR determinants for classification in RF2.
Figure 4
Figure 4
Distribution of abundance in FPKM values of the 20 most important genes for classification of aquatic environment in RF2.
Figure 5
Figure 5
Study design.
See this image and copyright information in PMC

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