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Metagenomic investigation of the seasonal distribution of bacterial community and antibiotic-resistant genes in Day River Downstream, Ninh Binh, Vietnam

Abstract

In this study, we use high-throughput sequencing-based metagenomic methods to investigate the differences in seasonal structures of the bacterial community and the abundance and diversity of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) in both shrimp ponds and river water samples downstream of the Day River, Ninh Binh, Vietnam. The structure of the central bacterial community, ARGs, and MGEs was found to be regardless of the seasons and locations. The predominant phyla found in all samples was Proteobacteria, Bacteroidetes, and Actinobacteria. Multi-drug resistance (MDR) genes and transposases are the most dominant ARG types and MGEs, respectively. Our data showed a higher abundance of bacterial communities, ARGs, and MGEs in the river water during the rainy season. There is a significant correlation between the abundance of ARGs, MGEs, and environmental factors. Our results indicate that water environments containing ARGs/MGEs carrying bacteria pose a risk to shrimp and human health, especially during the rainfall-polluted water season.

Introduction

Antibiotics are used to treat bacterial infectious diseases in human and veterinary medical practices. Farmers also administer antibiotics to food-production animals, plants, aquaculture, etc., mainly for promoting growth and preventing infections [1,2,3]. However, the misuse of antibiotics in clinical situations and in agriculture leads to antibiotic residues in animal-derived products and environments [4, 5]. Additionally, overusing antibiotics provides a selective pressure which facilitates the acquisition of antibiotic resistance genes (ARGs) through mutations or horizontal gene transfer—requiring mobile genetic elements (MGEs) as important carriers [6,7,8,9,10]. Antibiotic resistance poses a significant concern for public health because the antibiotic-resistant bacteria associated with the animals are often pathogenic to humans, cause complicated, untreatable, and prolonged infections in humans, and subsequently may lead to higher healthcare costs and even death [5, 11]. Water environments like aquaculture farms and rivers in urban areas are potential reservoirs for ARG pollution and hot spots of horizontal gene transfer [12,13,14,15].

Vietnam is a top producers and exporters of aquaculture products [16] and its farmers use antibiotics for disease treatment and prevention, and to stimulate shrimp/fish growth [17,18,19]. Although the Vietnamese government authority has issued strict regulations for the use of antibiotics in aquaculture [20], some antibiotics are still being reported even after they have been banned [21,22,23]. As reviewed by Lulijwa et al. [24], Vietnam is one of the leading users of antibiotics. The inappropriate use of antibiotics in agriculture is attributed to an emergence of drug-resistant pathogens [23, 25]. These factors contribute to the emergence of multi-drug resistant (MDR) bacteria in aquaculture and the surrounding environments. Therefore, research on the bacterial structure and diversity, and ARG abundance in water environments is urgently needed. Although several studies focused on antibiotics and ARG issues in aquaculture environments in Southern Vietnam have been done [19, 23, 26,27,28], the status of ARGs in aquaculture and the surrounding environment in Northern Vietnam has still not been assessed.

Metagenomic research using next-generation sequencing techniques is a powerful tool that can be utilized to investigate the microbial community and functional genomes in various environments [29,30,31,32,33,34]. To our knowledge, application of metagenomics to study antibiotic resistance in Vietnam aquaculture environments are in their infancy. Determining the structure and composition of bacterial communities regarding antibiotic resistance in different water systems may aid in controlling the spread of antibiotic-resistant elements. The Day River basin—one tributary of the Red River system branching 35 km upstream of Hanoi, is an essential component of the flood control system in the Red River, Hanoi, Vietnam [35]. Water pollution and degradation are concentrated in the middle and downstream areas of these river basins [36]. This study used a high-throughput sequencing-based metagenomic approach to investigate the seasonal distribution of microbial communities and antibiotic resistance genes in downstream Day River, Ninh Binh, Vietnam. The present study provides information both on the structure of bacterial communities, ARGs, and MGEs in water bodies and the correlation among ARGs, MGEs, microbial communities, and environmental factors. This study can offer insight for further controlling the prevalence of ARGs in the aquatic environments of Northern Vietnam. By the lack of studies that survey the microbial communities and antibiotic resistance genes in Northern Vietnam, our result could be the unique data that explored the correlation between the abundance of ARGs, MGEs, and water environmental parameters regarding the seasonal variations.

Material and methods

Sample collection

Sampling sites: water samples at two different locations—the Day River (including four sites—denoted as R1, R2, R3, R4) and three shrimp ponds (outside the Day River dike, namely, P1, P2, P3) (Fig. 1, Additional file 1: Table S1) downstream of the Day River in Ninh Binh, Vietnam. Sites were sampled in rainy (June—denoted as RS) and dry (December—denoted as DS) seansons in 2018. Most shrimp ponds outside the Day River dike operate a flow-through system that pumps in the cleaned river for shrimp farming and discharges wastewater back to Day River through a discharge canal, namely DCOD (which is directly connected to the river). The wastewater from aquaculture ponds inside the Day River dike is collected into a discharge canal (called DCID) connected to the river via a trench drain with a concrete sluice gate to control water flow. DCOD and DCID samples served as reference samples.

Fig. 1
figure 1

Sampling locations. Schematic diagram of sampling locations in Day River Downstream, Ninh Binh, Vietnam. P1, P2, P3: three different shrimp pond sites; R1, R2, R3, R4: four different river sites. DCOD, DCID: discharged canals. P1, P2, P3, R1, R2, R3, R4 and DCOD are located outside the Day River dike. DCID is located inside the Day River dike

Using sterile bottles, water samples were collected from each site in triplicates (3 × 500 mL) at a depth of 0.5 m from the water surface. All the samples were stored in a dark portable icebox and transferred to the laboratory within four hours. Physicochemical parameters of the water samples were measured by the Institute of Chemistry, Vietnam Academy of Science and Technology, and by using the Horiba Multiparameter water quality checker U-50 (Additional file 1: Table S1).

DNA extraction and sequencing

DNA extraction: Water samples were prefiltered through a 15 µm pore size filter then filtered again through the mixed cellulose ester gridded membrane filter (Membrane Solutions—USA, pore size 0.22 μm). Total DNA was extracted from filtered membranes using the Power Soil DNA isolation kit (Qiagen GmbH, Germany).

DNA sequencing: Total DNA extraction of all triplicate samples (collected from each sampling site) were pooled together to minimize the potential variations during DNA extraction and sent to Macrogen Inc. (Seoul, Republic of Korea) for Illumina Novaseq 6000 sequencing (150 bp × 2). Our DNA sequences data was submitted to Genbank with project accession number PRJNA770010.

Sequence analysis

Raw sequences were screened and trimmed using Pearf software (https://microbiology.se/software/petkit/) with the following parameters: pearf -q 28 -f 0.25 -t 0.05 -l 30 to obtain the filtered sequences. The filtered raw sequences were mapped against small subunit ribosomal RNA (SSU rRNA) sequences using the Metaxa2 software [37] (https://microbiology.se/software/metaxa2/) to access the microbial community structure.

The filtered raw sequences were mapped against the Comprehensive Antibiotic Resistance Database (CARD) version 3.0.7 [38] using EDGE script [39] (https://edge.readthedocs.io/en/latest/index.html) with BWA tool [40] (http://bio-bwa.sourceforge.net/) for ARG diversity and abundance elucidation.

The filtered raw sequences were also mapped against the Mobile Genetic Elements database (MGE) [41] for determining MGE composition.

The filtered raw sequences were used as input for de novo assembly in the Megahit software [39] (megahit–min-contig-len 300–presets meta-sensitive -m 0.9) to produce contigs. From all assembled contigs, the ORFs were predicted by using the Prodigal software with option -p meta for metagenomic samples and -c option for closed ends of ORFs [40] (prodigal -c -f gbk -g 11 -m -n -p meta) and aligned them to CARD (amino acid sequences) database using “diamond blastp” [42] (https://www.wsi.uni-tuebingen.de/lehrstuehle/algorithms-in-bioinformatics/software/diamond) (https://github.com/bbuchfink/diamond) with an E value threshold of 1e−10, a bit score of 50, and a sequence similarity cut-off > 70% to access detail ARGs diversity and abundance. In addition, PlasFlow tool (version 1.1) was applied to classify the genome location (chromosome- and plasmid-like contigs) of all the assembled long contigs (> 1000 bp) [43]. The ORFs were normalized by the number of copies of the 16S rRNA gene—estimated using the barrnap 0.9 tool (https://hpc.ilri.cgiar.org/barrnap-software) (https://vicbioinformatics.com/software.barrnap.shtml). Our workflow is summarized in Fig. 2.

Fig. 2
figure 2

Workflow diagram

Statistical analysis

We used Microsoft Excel tools F.test and T.test for statistical analyses. The Pearson correlation was used to assess correlations among the abundance of ARGs, MGEs, microbial communities, and environmental factors.

Results

Microbial community composition and abundance

At the phylum level, we detected a total of 37 bacterial phyla in all samples; with Proteobacteria, Bacteroidetes, and Actinobacteria are the most dominant phyla (Fig. 3). The bacterial community structure shows a similar pattern between all samples at the phylum level. The river water samples collected during the rainy season (include R1-RS, R2-RS, R3-RS, and R4-RS, namely, R-RS) show a significantly higher relative abundance of Proteobacteria and Bacteroidetes than the other samples (including P1-RS, P2-RS, and P3-RS, namely, P-RS; R1-DS, R2-DS, R3-DS, and R4-DS, namely, R-DS; P1-DS, P2-DS, and P3-DS, namely, P-DS). The DCOD-RS displays a similar pattern with R-RS, while DCID-RS shows similarity with P-RS.

Fig. 3
figure 3

Seasonal Distribution of microbial communities of river and shrimp pond water samples at the phylum level. “Other classified phyla” indicates the sum of the abundance of phyla with their maximum relative abundance percentages lower than 1% in any sample. P1, P2, P3: three different shrimp pond sites; R1, R2, R3, R4: four different river sites. DCOD, DCID: discharged canals. P1, P2, P3, R1, R2, R3, R4 and DCOD located outside the Day River dike. DCID located inside the Day River dike. RS: rainy season; DS: dry season

We found a total of 335 bacterial families in all samples; of these, 38 families have a relative abundance greater than 1% in at least one sample (Additional file 1: Fig. S1A). The relative abundance at the family level in R-RS is higher than in R-DS, P-RS, and P-DS—mainly dominated by Comamonadaceae, Moraxellaceae, Pseudomonadaceae, Cytophagaceae, and Sphingomonadaceae (p < 0.05). The relative abundance of Microbacteriaceae is higher in P-RS than in others. We observe typical families associated with opportunistic pathogens, including Burkholderiaceae, Neisseriaceae, and Xanthomonadaceae (Additional file 1: Fig. S1A) in the river and shrimp pond water samples.

We found 34 bacterial genera that have a relative abundance greater than 1% in at least one sample (Additional file 1: Fig. S1B). Acinetobacter, Cellvibrio, Arcicella, Novosphingobium, and Rhodobacter are more abundant in R-RS (Additional file 1: Fig. S1B), while Candidatus Pelagibacter was found to be more abundant in P-RS (Additional file 1: Fig. S1B). The bacteria with relative abundance greater than 0.1% in at least one sample in R-RS differed at the species level from R-DS, P-RS, P-DS (Additional file 1: Fig. S1C). Opportunistic pathogens, including Acinetobacter baumannii, Acinetobacter junii, Acinetobacter sp., Pseudomonas aeruginosa were mainly observed in R-RS. Otherwise Candidatus Pelagibacter ubique, Candidatus Pelagibacter sp. IMCC9063, and Candidatus Aquiluna rubra were the most prevalent species of P-RS, R-DS, P-DS.

ARGs composition, abundance, and resistance mechanisms

Our Illumina high-throughput sequencing data show that the ARG composition of each sample was quite similar (Fig. 4A). We found a total of 27 ARG types (92 subtypes) categorized by drug class antibiotic. The predominant ARG type was MDR, with ARG conferring resistance to two or more drug class categories, followed by rifamycin, aminoglycoside, and sulfonamide. Although ARG composition in each sample was not different, ARG abundance was higher in R-RS samples (Fig. 4A). Among different MDRs, the most dominant subtype is MDR14a. MDR14a is a subtype of multi-drug-resistant-gene that resists 14 drug classes, including macrolide, fluoroquinolone, monobactam, carbapenem, cephalosporin, cephamycin, penam, tetracycline, peptide, aminocoumarin, diaminopyrimidine, sulfonamide, phenicol, and penem (Additional file 1: Fig. S2A).

Fig. 4
figure 4

Abundance of main ARGs resistance types. ARGs resistance types categorized by drug class resistance type (A) and ARGs resistance mechanisms (B). P1, P2, P3: three different shrimp pond sites; R1, R2, R3, R4: four different river sites. DCOD, DCID: discharged canals. P1, P2, P3, R1, R2, R3, R4 and DCOD located outside the Day River dike. DCID located inside the Day River dike. RS: rainy season; DS: dry season

Using total metagenome sequencing contigs, we found 206 ARGs in our samples. Additional file 1: Fig. S2B shows the abundance of the top 15 ARGs from each sample. The most abundant ARGs were mexK, MuxB, and ugd with the copy of ARG per 16S rRNA gene value as 0.046, 0.045, and 0.038, respectively. Abundance values of individual ARGs in R-DS and P-DS were the lowest with the copy of ARG per 16S rRNA gene value of each ARG below 0.01, except ugd of P-DS.

As to ARG resistance mechanisms, our results in Fig. 4B indicate that “antibiotic efflux” is the predominant resistant mechanism in all samples, followed by "antibiotic target alteration combining antibiotic target replacement", "antibiotic inactivation", "antibiotic target alteration", "antibiotic target protection", "antibiotic target replacement", "reduced permeability to antibiotic", and "antibiotic efflux combining reduced permeability to antibiotic".

Composition and abundance of MGEs

We detected a total of 238 MGEs representing four groups including plasmids, transposon, integrons, and insertion sequences (IS) in all samples. Figure 5A demonstrates that the total quantity of transposon-like MGE was the most predominant, followed by IS -like MGEs, plasmid-like MGEs, and integron-like MGEs. Figure 5B shows the top 15 MGEs from each sample, among which tnpA is the most abundant MGE, followed by is9, iscrsp1, istB, and istA.

Fig. 5
figure 5

The Distribution and abundance of MGE types. A The abundance values of MGE group. B The abundance values of the top 15 MGE types of each sample. P1, P2, P3: three different shrimp pond sites; R1, R2, R3, R4: four different river sites. DCOD, DCID: discharged canals. P1, P2, P3, R1, R2, R3, R4 and DCOD located outside the Day River dike. DCID located inside the Day River dike. RS: rainy season; DS: dry season

Correlation among ARGs, MGEs, microbial communities, and environmental factors

Correlation between bacterial communities and environmental factors

Table 1 indicates that the relative abundance of Proteobacteria and Bacteroidetes phyla strongly and positively correlated with pH and NO3 concentration (R > 0.75, p-value < 0.05), whereas the relative abundance of Actinobacteria phylum significantly and positively correlates with temperature and pH. Conductivity, TDS, and salinity significantly and negatively affect the relative abundance of Proteobacteria (R < − 0.5, p-value < 0.05).

Table 1 Pearson correlation between the relative abundance of bacterial communities and the indicated environment factors

Correlation between ARGs/MGEs and environmental factors

Our results revealed that the abundance of ARGs positively correlates with temperature, pH, and NO3 concentration (R > 0.5, p-value < 0.05), whereas it negatively correlates with conductivity, TDS, and salt concentration (R < − 0.5, p-value < 0.05) (Table 2). The abundance of MGEs also significantly positively correlates with NO3 concentration and negatively correlates with conductivity, TDS, and salt concentration. Interestingly, the abundance of plasmids significantly correlates with temperature but not with pH. In contrast, the abundance of integrons and insertional sequences significantly but weakly correlates with pH (p-value < 0.05) but does not correlate with temperature. However, temperature and pH do not significantly affect the abundance of transposons (Table 2).

Table 2 Pearson correlation among the abundance between ARGs, MGEs, and environment factors

Correlation between ARGs or MGEs with bacterial communities

Our data show that the abundance of MGEs and ARGs (top ARGs except for glycopeptide- and mupirocin-resistant-gene) strongly and positively correlated with Proteobacteria and Bacteroidetes phyla (p-value < 0.001) (Table 3). The abundance of Actinobacteria phylum significantly correlates with rifamycin-, aminocoumarin-, and mupirocin-resistant-gene (Table 3). Additionally, the relative abundance of typical species associated with opportunistic pathogens including Acinetobacter baumannii, Acinetobacter junii, Acinetobacter sp., and Pseudomonas aeruginosa has a positively strong correlation with the abundance of ARGs and MGEs (R > 0.79, p-value < 0.001) (Table 4, Additional file 1).

Table 3 Pearson correlation among the abundance of ARGs, MGEs, and bacterial communities at the phylum level
Table 4 Pearson correlation among the abundance of ARGs, MGEs, and bacterial communities at the species level

Correlation between ARGs and MGEs

Table 5 shows that the abundance of total ARGs positively and strongly correlates with the abundance of total MGEs (R = 0.868, p-value < 0.0001). Among the 14 most abundant ARG types, ARG conferring resistance to mupirocin has a weak and insignificant correlation with all types of MGEs. The abundance of penam-resistant-gene significantly correlates with integrases and insertional sequences. The abundance of aminocoumarin-resistant-gene significantly correlates with transposases, intergrases, and insertional sequences. The abundance of all 11 remaining ARGs has a significant correlation with both total MGEs and individual MGE types (R > 0.5) (Table 5). The correlation of ARG conferring resistance to sulfonamide with intergrases is strongest (R = 0.969, p-value < 0.0001), followed by MDRs, macrolide, fluoroquinolone (R > 0.85, p-value < 0.001).

Table 5 Pearson correlation between the abundance of ARGs and the abundance of MGEs

Discussion

Bacterial community composition and abundance

In the present study, we found that bacterial community structures at the phylum level differed slightly by season (rainy vs. dry) or location (river vs. shrimp pond). Proteobacteria, Bacteroidetes, and Actinobacteria were the most dominant phyla in all water samples collected (Fig. 3), consistent with the previous studies done in Vietnam [26, 44], China [29, 45], and the US [32]. The relative abundance of Proteobacteria and Bacteroidetes are significantly different compared to others (not show data), suggesting that environmental factors may affect the abundance of bacterial communities, especially on Proteobacteria and Bacteroidetes. The abundance of the bacterial community found in the river water during the rainy season includes the following families: Comamonadaceae, Moraxellaceae, Pseudomonadaceae, Cytophagaceae, and Microbacteriaceae (Additional file 1: Fig. S1A); genera: Acinetobacter, Cellvibrio, Arcicella, Novosphingobium, Rhodobacter, and Candidatus Pelagibacter (Additional file 1: Fig. S1B). Comamonadacea is often associated with high nutrient conditions such as urban streams, soil, activated sludge, and wastewater [46,47,48]. Typical families associated with opportunistic pathogens were also observed, such as Burkholderiaceae, Neisseriaceae, Xanthomonadaceae. The following species were found to be most abundant in the river water during the rainy season: Acinetobacter sp., Pseudomonas aeruginosa, Acinetobacter baumannii, Acinetobacter junii, Pseudomonas stutzeri, Pseudomonas pseudoalcaligenes. These bacteria are potentially pathogenic and could cause many diseases such as urinary tract infection, pneumonia, meningitis, dermatitis, and are the primary cause of nosocomial infections [49]. The noticeable detection of these bacteria in both the river water and shrimp ponds suggests that the Day River Downstream can be a reservoir of pathogenic bacteria and thereby pose potential risks to shrimp and human health, especially during the rainy season.

ARGs and MGEs composition and abundance

Our data revealed that the pattern of major ARGs and MGEs just slightly varies by season and location. Overall, the abundance of ARGs and MGEs is higher in the river water during the rainy season (Figs. 4 and 5). These results suggest that the quantity of ARGs and MGEs might relate to each other. Presumably, rainfall might bring pollutants from the soil into the natural water bodies. This may lead to intensive pollution of the natural water bodies, increase the amount of ARGs [50], and even stimulate the river water flow to bring contaminants and ARGs into the downstream environment [51], changing the physicochemical properties of the water environment. Once the water environment becomes contaminated with ARGs, the ARGs will persist as pollutants and pose a challenge to eliminate [11, 52].

Overall, MDR- (especial MDR14a subtype), rifamycin- and aminoglycoside-resistant-genes are predominant in both shrimp ponds and the Day River (Fig. 4A; Additional file 1: Fig. S2A), suggesting that the above antibiotics are the most prevalent antibiotics consumed in the study area. Previous studies have indicated that phenicol, tetracycline, and sulfonamide were commonly used in Vietnam [19, 27, 53, 54]. Our data is consistent with Ronald et al. [24] that Vietnam is one of the leading users of antibiotic compounds [24].

On the other hand, transposases MGEswere predominant in all samples collected (Fig. 5A). Transposases and integrases are the main acquisition drivers of ARGs by MGEs [55]. TnpA and intI1 are important MGE markers, frequently found in various environments [56, 57]. Class 1 integron (intI1) might be a good indicator of antibiotic resistance associated with anthropogenic pollution due to the positive correlations with ARGs and anthropogenic pollution [58]. Our data revealed that tnpA is the most abundant type of transposase, and intI1 was in the top 15 MGEs of each sample (Fig. 5B). We also found a significant correlation between ARGs and MGEs (Table 5). Thus, pollution from human activity might contribute to antibiotic resistance.

Correlation between bacterial communities and environmental factors

Different physicochemical properties (e.g. pH, temperature, NO3, TDS, salinity) and antibiotic usage patterns lead to variation in the bacterial community in aquatic environments [59,60,61,62]. Our data shows that pH affected the relative abundance values of all three of the most abundant bacterial phyla (Proteobacteria, Bacteroidetes, Actinobacteria) (Table 1). Previous studies have indicated that pH value is one of the most significant environmental factors influencing the microbial communities of freshwater [63,64,65,66,67,68,69]. In addition, NO3 concentration strongly and positively correlates with the two most abundant phyla; Proteobacteria and Bacteroidetes. Conductivity, TDS, and salinity significantly negatively correlated with the relative abundance of Proteobacteria. As mentioned above, Proteobacteria and Bacteroidetes are substantially more abundant in the river water during the rainy season. We also observe that during the rainy season, the pH value and NO3 concentration of the river water increases, while the salinity, TDS, and conductivity decrease (Additional file 1: Table S1), which might facilitate the growth of Proteobacteria and Bacteroidetes, contributing to the emerging abundance of these bacterial communities. Proteobacteria play essential roles in denitrification, phosphorus removal, and organic degradation [70]. Bacteroidetes decompose complex organic compounds and polymers to create simpler molecules for other microorganisms’ utilization [71]. Actinobacteria decompose tough compounds and certain toxic compounds [72]. These bacteria might play a significant contribution to removing river water pollutants during the rainy season and therefore may be why there is a high abundance of them in our samples.

ARGs mechanisms, and correlation between ARGs and MGEs

The primary antibiotic resistance mechanism used in the shrimp pond water is quite similar to those in the river water, "antibiotic efflux", followed by "antibiotic target alteration combining antibiotic target replacement" is quite similar to the mechanism used in the river water (Fig. 4B). Moreover, the ARGs belonging to "resistance nodulation cell division (RND) antibiotic efflux pump" (including MexK, MuxB, acrB, MexF, adeF, mtrA, MexB, ceoB, adeJ, adeI, CRP, OprM, OprN, mdtB, and OpmH), "pmr phosphoethanolamine transferase" (ugd), and "multidrug and toxic compound extrusion (MATE) transporter" (pmpM) are the most abundance ARGs found in all samples. These results suggest that "antibiotic efflux" is the most abundant mechanism, thus intrinsic resistance mechanisms play essential roles in water environments in this study.

MGEs are crucial in transferring ARGs to new hosts to generate new resistant strains through horizontal gene transfer. Metagenomic studies observed the horizontal co-transfer of ARGs and MGEs [73,74,75]. Our data reveals that the abundance of ARGs and MGEs is strongly and significantly correlated (Table 5). This result suggests that horizontal gene transfer might also contribute to the co-transfer of ARGs and MGEs, resulting in antibiotic resistance formation at the Day River downstream.

Correlation of ARGs/MGEs with bacterial communities and environmental factors

In our study, the abundance of ARGs and MGEs is strongly correlated with Proteobacteria and Bacteroidetes (Table 3). Jian-Hua Wang et al. indicated that Proteobacteria carried more resistance genes than other phyla [76]. The dispersion of ARGs among pathogenic bacteria already had a significant effect on shrimp cultures worldwide, linked to massive losses in production [77]. Thi Thu Hang Pham et al. indicated that multi-resistant bacteria in intensive shrimp cultures might disseminate in the natural environment [78]. Thus, current water environments containing ARGs/MGEs carrier bacteria pose a risk of the dispersion of ARGs among pathogenic by horizontal gene transfer.

Our data also revealed that the abundance of ARGs and MGEs positively correlates with temperature, pH, and NO3 concentration, whereas negatively correlates with conductivity, TDS, and salt concentration (Table 2). However, the correlation of MGEs with temperature and pH was found to be insignificant. This might be due to the temperature specifically affecting the abundance of plasmids, while the pH affecting integrons and insertional sequences. The NO3 concentration affected all types of MGEs (Table 2). Thus, our data suggest environmental factors are associated with ARG abundance and MGEs in the Day River downstream.

Availability of data and materials

Our DNA sequences data was submitted to Genbank with project accession number PRJNA770010.

References

  1. Cully M (2014) Public health: the politics of antibiotics. Nature 509(7498):S16–S17

    Article  CAS  PubMed  Google Scholar 

  2. FDA, U.F.a.D.A. 2010 summary report on antimicrobials sold or distributed for use in food producing animals. http://www.fda.gov/downloads/ForIndustry/UserFees/2011

  3. Rushton J (2015) Anti-microbial use in animals: how to assess the trade-offs. Zoonoses Public Health 62(Suppl 1):10–21

    Article  PubMed  PubMed Central  Google Scholar 

  4. Ayukekbong JA, Ntemgwa M, Atabe AN (2017) The threat of antimicrobial resistance in developing countries: causes and control strategies. Antimicrob Resist Infect Control 6:47

    Article  PubMed  PubMed Central  Google Scholar 

  5. Manyi-Loh C et al (2018) Antibiotic use in agriculture and its consequential resistance in environmental sources: potential public health implications. Molecules. https://doi.org/10.3390/molecules23040795

    Article  PubMed  PubMed Central  Google Scholar 

  6. Chen B et al (2013) Metagenomic profiles of antibiotic resistance genes (ARGs) between human impacted estuary and deep ocean sediments. Environ Sci Technol 47(22):12753–12760

    Article  CAS  PubMed  Google Scholar 

  7. Holmes AH et al (2016) Understanding the mechanisms and drivers of antimicrobial resistance. Lancet 387(10014):176–187

    Article  CAS  PubMed  Google Scholar 

  8. Lupo A, Coyne S, Berendonk TU (2012) Origin and evolution of antibiotic resistance: the common mechanisms of emergence and spread in water bodies. Front Microbiol 3:18

    Article  PubMed  PubMed Central  Google Scholar 

  9. Mullany P (2014) Functional metagenomics for the investigation of antibiotic resistance. Virulence 5(3):443–447

    Article  PubMed  PubMed Central  Google Scholar 

  10. Suzuki S et al (2017) Editorial: antibiotic resistance in aquatic systems. Front Microbiol 8:14

    PubMed  PubMed Central  Google Scholar 

  11. Pruden A et al (2006) Antibiotic resistance genes as emerging contaminants: studies in northern Colorado. Environ Sci Technol 40(23):7445–7450

    Article  CAS  PubMed  Google Scholar 

  12. Rico A, Satapornvanit K, Haque MM, Min J, Nguyen PT, Telfer TC, Van Den Brink PJ (2012) Use of chemicals and biological products in Asian aquaculture and their potential environmental risks: a critical review. Rev Aquac 4:75–93

    Article  Google Scholar 

  13. Cabello FC et al (2016) Aquaculture as yet another environmental gateway to the development and globalisation of antimicrobial resistance. Lancet Infect Dis 16(7):e127–e133

    Article  PubMed  Google Scholar 

  14. Henriksson PJG et al (2018) Unpacking factors influencing antimicrobial use in global aquaculture and their implication for management: a review from a systems perspective. Sustain Sci 13(4):1105–1120

    Article  PubMed  Google Scholar 

  15. Muziasari WI et al (2016) Aquaculture changes the profile of antibiotic resistance and mobile genetic element associated genes in Baltic Sea sediments. FEMS Microbiol Ecol 92(4):fiw052

    Article  PubMed  CAS  Google Scholar 

  16. FAO. 2018 The state of world fisheries and aquaculture (SOFIA). Meeting the sustainable development goals. http://www.fao.org/documents/card/en/c/I9540EN. Accessed 14 April 2020.

  17. Dang ST et al (2011) Impact of medicated feed on the development of antimicrobial resistance in bacteria at integrated pig-fish farms in Vietnam. Appl Environ Microbiol 77(13):4494–4498

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Nguyen KV et al (2013) Antibiotic use and resistance in emerging economies: a situation analysis for Viet Nam. BMC Public Health 13:1158

    Article  PubMed  PubMed Central  Google Scholar 

  19. Uchida K et al (2016) Monitoring of antibiotic residues in aquatic products in urban and rural areas of Vietnam. J Agric Food Chem 64(31):6133–6138

    Article  CAS  PubMed  Google Scholar 

  20. MARD, M.o.A.a.R.D.M., List of drugs, chemicals and antibiotics of banned or limited use for aquaculture and veterinary purposes. MARD: Hanoi 2014, 2014.

  21. Braun G et al (2019) Pesticides and antibiotics in permanent rice, alternating rice-shrimp and permanent shrimp systems of the coastal Mekong Delta, Vietnam. Environ Int 127:442–451

    Article  CAS  PubMed  Google Scholar 

  22. Strom GH et al (2019) Antibiotic use by small-scale farmers for freshwater aquaculture in the upper Mekong Delta, Vietnam. J Aquat Anim Health 31(3):290–298

    Article  PubMed  CAS  Google Scholar 

  23. Thuy HT, Nga PL, Loan TT (2011) Antibiotic contaminants in coastal wetlands from Vietnamese shrimp farming. Environ Sci Pollut Res Int 18(6):835–41

    Article  CAS  PubMed  Google Scholar 

  24. Lulijwa R, Rupia EJ, Alfaro AC (2019) Antibiotic use in aquaculture, policies and regulation, health and environmental risks: a review of the top 15 major producers. Rev Aquac 12(2):640–663

    Article  Google Scholar 

  25. Thornber K et al (2020) Evaluating antimicrobial resistance in the global shrimp industry. Rev Aquac 12(2):966–986

    Article  PubMed  Google Scholar 

  26. Le LA et al (2020) Microbiome dataset analysis from a shrimp pond in Ninh Thuan, Vietnam using shotgun metagenomics. Data Brief 31:105731

    Article  PubMed  PubMed Central  Google Scholar 

  27. Nguyen Dang Giang C et al (2015) Occurrence and dissipation of the antibiotics sulfamethoxazole, sulfadiazine, trimethoprim, and enrofloxacin in the Mekong Delta, Vietnam. PLoS ONE 10(7):e0131855

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Nguyen HN et al (2014) Molecular characterization of antibiotic resistance in Pseudomonas and Aeromonas isolates from catfish of the Mekong Delta, Vietnam. Vet Microbiol 171(3–4):397–405

    Article  CAS  PubMed  Google Scholar 

  29. Fang H et al (2019) Metagenomic analysis of bacterial communities and antibiotic resistance genes in the Eriocheir sinensis freshwater aquaculture environment. Chemosphere 224:202–211

    Article  CAS  PubMed  Google Scholar 

  30. Gao Q et al (2018) Diverse and abundant antibiotic resistance genes from mariculture sites of China’s coastline. Sci Total Environ 630:117–125

    Article  CAS  PubMed  Google Scholar 

  31. Guo XP et al (2018) Seasonal and spatial distribution of antibiotic resistance genes in the sediments along the Yangtze Estuary, China. Environ Pollut 242(Pt A):576–584

    Article  CAS  PubMed  Google Scholar 

  32. Laperriere SM et al (2020) Headwater stream microbial diversity and function across agricultural and urban land use gradients. Appl Environ Microbiol. https://doi.org/10.1128/AEM.00018-20

    Article  PubMed  PubMed Central  Google Scholar 

  33. Sun Y, Li X, Liu J, Yao Q, Jin J, Liu X, Wang G (2019) Comparative analysis of bacterial community compositions between sediment and water in different types of wetlands of northeast China. J Soils Sediments 19:3083–3097

    Article  CAS  Google Scholar 

  34. Zainab SM et al (2020) Antibiotics and antibiotic resistant genes (ARGs) in groundwater: A global review on dissemination, sources, interactions, environmental and human health risks. Water Res 187:116455

    Article  CAS  PubMed  Google Scholar 

  35. Dang NM, Babel MS, Luong HT (2011) Evaluation of food risk parameters in the Day River Flood Diversion Area, Red River Delta, Vietnam. Nat Hazards 56:169–194

    Article  Google Scholar 

  36. Hoi HT (2020) Current Situation of Water pollution in Vietnam and Some Recommendations. IOP Conf Ser Earth Environ Sci 442(2020):012014

    Google Scholar 

  37. Bengtsson-Palme J et al (2015) METAXA2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Mol Ecol Resour 15(6):1403–1414

    Article  CAS  PubMed  Google Scholar 

  38. Jia B et al (2017) CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res 45(D1):D566–D573

    Article  CAS  PubMed  Google Scholar 

  39. Li PE et al (2017) Enabling the democratization of the genomics revolution with a fully integrated web-based bioinformatics platform. Nucleic Acids Res 45(1):67–80

    Article  CAS  PubMed  Google Scholar 

  40. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25(14):1754–1760

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Parnanen K et al (2018) Maternal gut and breast milk microbiota affect infant gut antibiotic resistome and mobile genetic elements. Nat Commun 9(1):3891

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Buchfink B, Xie C, Huson DH (2015) Fast and sensitive protein alignment using DIAMOND. Nat Methods 12(1):59–60

    Article  CAS  PubMed  Google Scholar 

  43. Krawczyk PS, Lipinski L, Dziembowski A (2018) PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. Nucleic Acids Res 46(6):e35

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Nakayama T et al (2017) Water metagenomic analysis reveals low bacterial diversity and the presence of antimicrobial residues and resistance genes in a river containing wastewater from backyard aquacultures in the Mekong Delta, Vietnam. Environ Pollut 222:294–306

    Article  CAS  PubMed  Google Scholar 

  45. Xiong W et al (2015) Antibiotics, antibiotic resistance genes, and bacterial community composition in fresh water aquaculture environment in China. Microb Ecol 70(2):425–432

    Article  CAS  PubMed  Google Scholar 

  46. Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (2014) The prokaryotes: Alphaproteobacteria and Betaproteobacteria. Springer-Verlag, Berlin

    Book  Google Scholar 

  47. Roberto AA, Van Gray JB, Leff LG (2018) Sediment bacteria in an urban stream: spatiotemporal patterns in community composition. Water Res 134:353–369

    Article  CAS  PubMed  Google Scholar 

  48. Simonin M et al (2019) In search of microbial indicator taxa: shifts in stream bacterial communities along an urbanization gradient. Environ Microbiol 21(10):3653–3668

    Article  PubMed  Google Scholar 

  49. Decker BK, Palmore TN (2013) The role of water in healthcare-associated infections. Curr Opin Infect Dis 26(4):345–351

    Article  PubMed  PubMed Central  Google Scholar 

  50. Di Cesare A et al (2017) Rainfall increases the abundance of antibiotic resistance genes within a riverine microbial community. Environ Pollut 226:473–478

    Article  PubMed  CAS  Google Scholar 

  51. Garner E et al (2017) Stormwater loadings of antibiotic resistance genes in an urban stream. Water Res 123:144–152

    Article  CAS  PubMed  Google Scholar 

  52. Su S et al (2020) Distribution of antibiotic resistance genes in three different natural water bodies-a lake, river and sea. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph17020552

    Article  PubMed  PubMed Central  Google Scholar 

  53. Luu QH, Nguyen TB, Nguyen TL, Do TT, Dao TH, Padungtod P (2021) Antibiotics use in fish and shrimp farms in Vietnam. Aquac Rep. https://doi.org/10.1016/j.aqrep.2021.100711

    Article  Google Scholar 

  54. Managaki S et al (2007) Distribution of macrolides, sulfonamides, and trimethoprim in tropical waters: ubiquitous occurrence of veterinary antibiotics in the Mekong Delta. Environ Sci Technol 41(23):8004–8010

    Article  CAS  PubMed  Google Scholar 

  55. Noguchi H, Park J, Takagi T (2006) MetaGene: prokaryotic gene finding from environmental genome shotgun sequences. Nucleic Acids Res 34(19):5623–5630

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Li DC et al (2020) Emergence and spread patterns of antibiotic resistance genes during two different aerobic granular sludge cultivation processes. Environ Int 137:105540

    Article  CAS  PubMed  Google Scholar 

  57. Scott KP (2002) The role of conjugative transposons in spreading antibiotic resistance between bacteria that inhabit the gastrointestinal tract. Cell Mol Life Sci 59(12):2071–2082

    Article  CAS  PubMed  Google Scholar 

  58. Gillings MR et al (2015) Using the class 1 integron-integrase gene as a proxy for anthropogenic pollution. ISME J 9(6):1269–1279

    Article  CAS  PubMed  Google Scholar 

  59. Gao P et al (2012) Occurrence of sulfonamide and tetracycline-resistant bacteria and resistance genes in aquaculture environment. Water Res 46(7):2355–2364

    Article  CAS  PubMed  Google Scholar 

  60. Gilbert JA et al (2012) Defining seasonal marine microbial community dynamics. ISME J 6(2):298–308

    Article  CAS  PubMed  Google Scholar 

  61. Tang Y et al (2014) Identification of bacterial community composition in freshwater aquaculture system farming of Litopenaeus vannamei reveals distinct temperature-driven patterns. Int J Mol Sci 15(8):13663–13680

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Wobus A et al (2003) Microbial diversity and functional characterization of sediments from reservoirs of different trophic state. FEMS Microbiol Ecol 46(3):331–347

    Article  CAS  PubMed  Google Scholar 

  63. Giordani A, Rodriguez RP, Sancinetti GP, Hayashi EA, Beli E, Brucha G (2019) Effect of low pH and metal content on microbial community structure in an anaerobic sequencing batch reactor treating acid mine drainage. Miner Eng. https://doi.org/10.1016/j.mineng.2019.105860

    Article  Google Scholar 

  64. Fierer N et al (2007) Environmental controls on the landscape-scale biogeography of stream bacterial communities. Ecology 88(9):2162–2173

    Article  PubMed  Google Scholar 

  65. Hullar MA, Kaplan LA, Stahl DA (2006) Recurring seasonal dynamics of microbial communities in stream habitats. Appl Environ Microbiol 72(1):713–722

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Liu J et al (2019) Response of microbial communities and interactions to thallium in contaminated sediments near a pyrite mining area. Environ Pollut 248:916–928

    Article  CAS  PubMed  Google Scholar 

  67. Jamieson J et al (2018) Identifying and quantifying the intermediate processes during nitrate-dependent iron(II) oxidation. Environ Sci Technol 52(10):5771–5781

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Nino-Garcia JP, Ruiz-Gonzalez C, Del Giorgio PA (2016) Interactions between hydrology and water chemistry shape bacterioplankton biogeography across boreal freshwater networks. ISME J 10(7):1755–1766

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Sun J et al (2016) Effect of oxalic acid treatment on sediment arsenic concentrations and lability under reducing conditions. J Hazard Mater 311:125–133

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Cydzik-Kwiatkowska A, Zielinska M (2016) Bacterial communities in full-scale wastewater treatment systems. World J Microbiol Biotechnol 32(4):66

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Zhang B, Xu X, Zhu L (2017) Structure and function of the microbial consortia of activated sludge in typical municipal wastewater treatment plants in winter. Sci Rep 7(1):17930

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Nguyen LN et al (2020) Genome sequencing as a new window into the microbial community of membrane bioreactors—a critical review. Sci Total Environ 704:135279

    Article  CAS  PubMed  Google Scholar 

  73. Lundstrom SV et al (2016) Minimal selective concentrations of tetracycline in complex aquatic bacterial biofilms. Sci Total Environ 553:587–595

    Article  PubMed  CAS  Google Scholar 

  74. Murray AK et al (2018) Novel insights into selection for antibiotic resistance in complex microbial communities. mBio. https://doi.org/10.1128/mBio.00969-18

    Article  PubMed  PubMed Central  Google Scholar 

  75. Sun J et al (2016) Co-transfer of blaNDM-5 and mcr-1 by an IncX3-X4 hybrid plasmid in Escherichia coli. Nat Microbiol 1:16176

    Article  CAS  PubMed  Google Scholar 

  76. Wang JH et al (2018) Metagenomic analysis of antibiotic resistance genes in coastal industrial mariculture systems. Bioresour Technol 253:235–243

    Article  CAS  PubMed  Google Scholar 

  77. Letchumanan V et al (2015) Occurrence and antibiotic resistance of Vibrio parahaemolyticus from shellfish in Selangor, Malaysia. Front Microbiol 6:1417

    PubMed  PubMed Central  Google Scholar 

  78. Pham TTH et al (2018) Analysis of antibiotic multi-resistant bacteria and resistance genes in the effluent of an intensive shrimp farm (Long An, Vietnam). J Environ Manage 214:149–156

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The study was funded by VAST project QTKR01.03/18-19. This study was also supported, in part, by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2016R1A6A1A03012862). We would like to thank Kelly Irene Morgan (TRACE Wildlife Forensics Network) for improving the English of this manuscript.

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SGN writes a proposal, designs this study, does the experiment, analyses data, and writes the manuscript. CTH and TU edit the submission, sampling, edit the manuscript. All authors summary the data and edit the manuscript. All the authors read and approved the final manuscript.

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Correspondence to Cuong Tu Ho or Tatsuya Unno.

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Additional file 1:

Table S1. Physicochemical parameter of water environment by sample location. Table S2. Summary of sequences, de novo assembly result and genome context of ARGs. Figure S1. Seasonal distribution of microbial communities of river and shrimp pond water samples at the family, genus, and species level. Figure S2. Abundance of main ARGs resistance types.

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Nguyen, S.G., Raza, S., Ta, L.T. et al. Metagenomic investigation of the seasonal distribution of bacterial community and antibiotic-resistant genes in Day River Downstream, Ninh Binh, Vietnam. Appl Biol Chem 65, 26 (2022). https://doi.org/10.1186/s13765-022-00687-w

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