Metagenomic approach revealed the mobility and co-occurrence of antibiotic resistomes between non-intensive aquaculture environment and human

Abstract

Background: Aquaculture is an important food source worldwide. The extensive use of antibiotics in intensive large-scale farms has resulted in resistance development. Non-intensive aquaculture is another aquatic feeding model that is conducive to ecological protection and closely related to the natural environment. However, the transmission of resistomes in non-intensive aquaculture has not been well characterized. Moreover, the influence of aquaculture resistomes on human health needs to be further understood. Here, metagenomic approach was employed to identify the mobility of aquaculture resistomes and estimate the potential risks to human health.

Results: The results demonstrated that antibiotic resistance genes (ARGs) were widely present in non-intensive aquaculture systems and the multidrug type was most abundant accounting for 34%. ARGs of non-intensive aquaculture environments were mainly shaped by microbial communities accounting for 51%. Seventy-seven genera and 36 mobile genetic elements (MGEs) were significantly associated with 23 ARG types (p < 0.05) according to network analysis. Six ARGs were defined as core ARGs (top 3% most abundant with occurrence frequency > 80%) which occupied 40% of ARG abundance in fish gut samples. Seventy-one ARG-carrying contigs were identified and 75% of them carried MGEs simultaneously. The qacEdelta1 and sul1 formed a stable combination and were detected simultaneously in aquaculture environments and humans. Additionally, 475 high-quality metagenomic-assembled genomes (MAGs) were recovered and 81 MAGs carried ARGs. The multidrug and bacitracin resistance genes were the most abundant ARG types carried by MAGs. Strikingly, Fusobacterium_A (opportunistic human pathogen) carrying ARGs and MGEs were identified in both the aquaculture system and human guts, which indicated the potential risks of ARG transfer.

Conclusions: The mobility and pathogenicity of aquaculture resistomes were explored by a metagenomic approach. Given the observed co-occurrence of resistomes between the aquaculture environment and human, more stringent regulation of resistomes in non-intensive aquaculture systems may be required. Video Abstract.

Keywords: ARG mobility; Aquaculture; Metagenome; One Health; Resistome.

Fig. 1

ARG composition and abundance profile across samples. a Heatmap of ARG relative abundance (log-transformed). Different color blocks at the top represent different sample groups. b Box plot of ARG abundance in different sample groups. Significant difference between groups is indicated by asterisk with t test (***p < 0.001). c PCoA shows the sample clustering based on ARG abundance profile (95% confidence interval)

Fig. 2

The core ARGs in aquaculture system. a UpSet diagram shows the number of ARGs shared and unique between different sample groups. Solid black points indicate ARG occurrence in the sample group, and points linked by lines indicate ARGs shared by different sample groups. The bar chart on the top shows the number of ARG unique or shared by sample groups, and the bar chart on the left shows the total number of detected ARG subtypes. b The Venn diagram shows the number of ARGs shared by the human gut and the aquaculture system. c The percent stacked histogram shows the composition of typical and core ARGs (indicated by different colors). Human gut and environmental typical ARGs defined as ARGs detected in all human gut and environment samples (fishpond sediment and water samples), respectively. Fish gut typical ARGs defined as ARGs detected in 24% fish gut samples, and chicken gut typical ARGs defined as ARGs detected in 50% chicken gut samples

Fig. 3

Influencing factors of ARGs in aquaculture system. a The network analysis shows the relationship between ARGs, MGEs, and bacteria (genus level) (p < 0.05). Larger nodes indicate more connections. The red lines indicate positive correlations, and the green lines indicate negative correlations. b VPA shows the interpretation rate of ARGs by MGEs and bacteria. c Linear relationship between ARG abundance and the number of MGEs (p < 0.001). d Linear relationship between ARG abundance and MGE abundance (p < 0.001) (discrete sample points f3, f4, and f15 were excluded)

Fig. 4

The co-occurrence pattern of ARGs and MGEs based on contigs. a The co-occurrence incidence of ARGs and MGEs. The x-axis means the distance of ARG-MGE, and the y-axis means the co-occurrence incidence. With the increase of distance, the co-occurrence rate also increases and gradually reaches a peak. b The shortest distance of ARG-MGE in the human gut and aquaculture system (**p < 0.01 indicates significant difference). c Gene arrangement patterns of sul1-qacEdelta1 and neighborhood genes in different sample groups. Different colored contigs belong to different sample groups, and the taxonomy names colored in red indicate opportunistic pathogenic bacteria

Fig. 5

Circos diagram shows the ARGs carried by different MAGs. The outermost circle represents the MAG phylum level and ARG types, the gray rectangle represents the number of MAGs and ARG subtypes, and the innermost connecting lines are colored according to the ARG types

Fig. 6

The heatmap shows the ACG abundance, mobility, and carried ARG types. a The color bar A indicates the ACG carries one or more ARGs. The color bar B indicates the ACG does not carry MGE, carries one MGE, or carries multiple MGEs. The color bar C indicates the sample group from which the ACG was recovered. The heatmap shows the average abundance of ACGs in different sample groups (data was log transformed). ACG name labels in red indicate opportunistic pathogenic bacteria. b The number of ARG types carried by ACGs. The color indicated the carried ARG number