3.1 Diversity and abundance of ARGs in the HZ
The mixed soil and water sample from S4-3, with a high antibiotic concentration, was used as the target pollutant sample. Of the 271 targeted ARGs associated with 9 antibiotics, a tota of 228 were detected in the sample. The total detection rate of 84.1% obtained in our study was higher than that in the reservoir system (78.9%) (Chen et al. 2019). This result indicates that the existence of more resistant bacteria in the microbial communities of the HZ and complex environmental incentives promoted the emergence of novel antibiotic resistance. The detection rates of ARGs associated with other antibiotics are shown in Fig. 2a. Among them, the highest and lowest relative abundances were obtained for aadA2-03 and tetV, with values of 6.30×10− 2 and 1.47×10− 6, respectively. There was only one vancomycin-related ARG (vanSE, 4.48×10− 3) in the top 100, and the rest were on the order of 10− 6. The 82% detection rate for vancomycin indicates the emergence of an increasing number of vancomycin-resistant bacteria. The concentration ratio of the top 150 ARGs was analyzed (Fig. 2b), and the ARG types followed the order aminoglycoside > beta lactamase > sulfonamide > MGEs > tetracycline > acrolides, lincosamides and type B streptogramin (MLSB) > fluoroquinolones-chloramphenicol (FCA) > vancomycin. Aminoglycosides and beta lactamase ARGs were the dominant types of ARGs, which was similar to the results found in the Wen-Rui Tang River in China (Zhou et al. 2017).
In the present study, 17 ARGs belonging to different categories (aac(6’)-Ib-3, aadA-01 and aadA2-03 for aminoglycosides; blaOXA1, blaOXA10-02 and cepA for beta-lactam; sul1 and sul2 for sulfonamides; tetG-02, tetM-01 and tetX for tetracyclines; catB3, msr(D) and mexF for MLSB/FCA; intI1, tnpA-03 and tnpA-04 for MGEs) were investigated comprehensively (Fig. 3). As shown in Fig. 3a, 15 of the 17 ARGs were successfully detected in the surface water; those that were not blaOXA10-2 and cepA. The total relative abundances of ARGs ranged from 0.16 to 0.29, with a main focus on S2, S3, and S4. The distribution characteristics of ARGs at each sampling point were relatively similar, with an order of those associated with sulfonamide > aminoglycoside > MLSB/FCA > tetracycline > beta-lactamase. The distribution ratios of ARGs at each sampling point are shown in Fig. 3b. It is obvious that tnpA-04 represented a large percentage of the total ARGs at each sampling point, up to 64%. The relative abundances of the top five other ARGs were in the order of sul1 > sul2 > aadA2-03 > intI1 > mexF. The two sulfonamide ARGs reached a total amount of up to 5.06×10− 2 and 2.57×10− 2, respectively. A high abundance of sulfonamide ARGs is commonly detected in water environments such as the Haihe River (Luo et al. 2011). This may be due to the long-term selective pressure caused by low levels of sulfonamide antibiotics and their relatively low hydrolysis rate (Wang 2019). 16 of the 17 ARGs were successfully detected during the groundwater (blaOXA10-2 was not detected) (Fig. 3c). The order of the ARGs in terms of abundance was consistent with the pattern observed for the surface water, but the total abundance was slightly higher than that in the surface water. In contrast to the water samples, the 17 ARGs were successfully detected to varying degrees in the sediments (Fig. 3e). The distribution of the ARGs in the sediments followed the order MGEs > aminoglycoside > sulfonamide > tetracycline > beta-lactamase > MLSB/FCA. The relative abundances of the top five ARGs showed the order tnpA-04 > aadA2-03 > sul1 > sul2 > aadA2-01 (Fig. 3f), and the total amounts of these ARGs were all above 10− 1. All types of ARGs expressed their maximum abundance at S5. This is because the sediments are mainly clay and contain a high TOC content. In the HZ, due to the periodic interaction, the overall difference in the abundance of ARGs in the media was not large, but the groundwater and sediment phases were prone to harbor more diverse ARGs and some high-abundance ARGs. Maybe the facultative anaerobic conditions of groundwater are particularly suitable for the growth of resistant bacteria. More attention should be given to addressing this insufficiency in future research, especially in some unique and different types of HZs.
3.2 Distribution differences in ARGs and dominant ARGs
The abundance data for the 17 ARGs in three types of media are represented with box plots (Fig. 4). These ARGs were classified into two groups: One group contained the dominant ARGs, with the top 5 in terms of abundance being tnpA-04, cepA, sul1, aadA2-03 and sul2. Their abundances were 8.38×10− 2, 3.04×10− 2, 2.61×10− 2, 2.22×10− 2 and 1.84×10− 2. The other group contained non-dominant ARGs, and their average values were below 10− 3. And the high outliers and great degree of dispersion (especially for aadA-01, aadA2-03 and sul1) resulted from the differences in three types of media.
PCA based on the Bray-Curtis distance was applied to analyze the ordination plot of the ARG distribution patterns in association with the variation in media (Fig. 5). Overall, as summarized in Fig. 5a, the B1 site was distinct from the other sampling sites. The first two PCs explained 65.5% of the total ARG abundance variation, with PC1 explaining 43.2% of the variation. The 25 sites were divided into three groups: Class 1 mainly included sediment along the line (C1, C2, B2, C4-3 and C5), Class 2 mainly included groundwater and surface water (A2, A3, A4, A5, B3, B4-1, B4-2, B4-3, B4-4 and B4-5), and Class 3 mainly included lateral sediments (C0, C3, C4-1, C4-3, C4-4 and C4-5). It is worth noting that surface water and groundwater were in the same class, which suggests the existence of similarities in the water phase. The sampling points were randomly distributed, indicating that geographic location was not the main factor affecting the change in ARGs. The sampling sites in the sediments were clustered into Class 1 and Class 3, and there were obvious horizontal and lateral differences. Overall, the ARGs in water and sediment showed obvious differences. Antibiotic residues and physiochemical properties result in the creation of different microorganism habitats and cause the evolution of diverse antibiotic-resistant bacteria and ARGs. Zhou et al. (2017) showed that ARGs had little relationship with geographic location and were mainly affected by the composition of the bacterial colonies. The unique media of the HZ represent good habitats for resistant bacteria. The results also indicate that the media environments were a driving force affecting the distribution of ARGs. Figure 5b shows the results of the PCA in terms of MGE abundance. Class 3 included the three types of media in the distribution characteristics of the MGEs (Fig. 5b), which was consistent with the phenomenon that high-abundance MGEs exist in the three types of media in Sect. 3.1. This result indicates that the distribution of MGEs at the sampling sites showed a certain degree of randomness. In addition, filtering out ARGs with significant differences in abundance is of great significance for exploring the formation of dominant ARGs in different media (Fig. 6). The X-axis represents the logarithmic value of the fold change with 2 as the base (log2 (fold change)), and the Y-axis represents the negative logarithm of the p value (-log10 (p-value)). In the groundwater/surface water, only aac(6')-Ib-3 expression was significantly upregulated among the 15 ARGs, indicating that aac(6')-Ib-3 was most prevalent in the groundwater environment. In the sediments/surface water, the expression of 5 ARGs (tnpA-04, catB3, tetG-02, tetM-01, and msr(D)) was significantly upregulated. Differences in illumination and temperature can affect the expression of ARGs. Compared with those in the surface water, the steadier temperatures and low light availability in the sediments resulted in a higher abundance of ARGs. In the sediments/groundwater, catB3 was significantly upregulated, while tnpA-04 and mexF were significantly downregulated. Han et al. (2017) found that mexF was amplified by 105 times in sediments due to the addition of fishmeal. Therefore, there may be other factors that cause mexF to become more prevalent in groundwater. It is worth mentioning that although have different abundances. The difference of catB3 and mexF in sediments and groundwater may be due to the expression of different resistance mechanisms. Overall, more than half of the total detected ARGs were shared among the environmental compartments, indicating that these ARGs are widespread and persistent in the HZ environment. The existence of dominant ARGs in the different phases (9 ARGs) suggests that these genes might be primordial genes in their corresponding phase, which is important for understanding the production of ARGs in the HZ.
3.3 Environmental factors influencing the profiles of ARGs
In general, residues of antibiotics and environmental factors may cause the potential risk of promoting the development of bacterial resistance genes through genetic mutation and HGT (Bouki et al. 2013). Research in recent years has found that the correlation between ARGs and antibiotics is not consistent ( Guo et al. 2018, Luo et al. 2011). The contribution of antibiotics to ARGs in the HZ needs to be further addressed. The correlations among physicochemical parameters, antibiotics and ARGs were evaluated with RDA (Fig. 7). In the surface water (Fig. 7a), antibiotics provided a smaller contribution to the overall variance than environmental factors and were filtered out in the forward selection results. Of the total variation, 88.6% could be explained by axis 1 and 7.7% could be explained by axis 2. The order of the sampling sites projected to the line of ARGs indicated that the ARGs at sites 1, 2 and 4 were greatly affected by COD. A positive correlation was still found between ARGs and most environmental factors (temperature and TN). However, DO (p < 0.05) showed a negative correlation with the ARG levels. In the surface water, antibiotic residues may only exert a small amount of selective pressure influencing the occurrence of ARGs. Bengtsson-Palme and Larsson (2016) consider that the low concentrations of antibiotics in the water environment are not sufficient to promote the proliferation and spread of ARGs. Other residual chemicals may provide selection or co-selection effects, such as heavy metals and polycyclic aromatic hydrocarbons (Wang et al. 2017). In contrast to the relationships in the surface water, SMZ and OTC were positively correlated with most ARGs in the groundwater, especially sul1 and sul2 (p < 0.05) (Fig. 7b). Of the total variation, 78.0% could be explained by axis 1 and 18.5% could be explained by axis 2. Sulfonamide antibiotics have a certain inducing effect on their corresponding ARGs, and it have been reported to occur in landfills in China (Wu et al. 2017). At S1, cepA, tetM-01, aadA2-03 and tnpA-03 were most affected by OTC and TN (p < 0.05). All the sampling sites except for S1 were arranged in clusters, indicating that the expression of ARGs at these sites was affected by similar environmental factors. The sampling sites along the line were most affected by DO, while the lateral sampling sites in the HZ showed greater influences of COD, TOC, and temperature. Compared with the observations in the water phase, the antibiotics in sediments were found to contribute more to the total variance in ARGs, especially for SMZ (Fig. 7c). Of the total variation, 62.6% could be explained by axis 1 and 7.7% could be explained by axis 2. The order of the sampling sites projected to the line of ARGs indicated that S2 and S5 had the highest abundances of ARGs, which were driven by TOC and clay overall. Nutrients are an essential factor allowing antibiotic-resistant bacteria to maintain normal life activities. The results for all three types of media in the HZ suggested that carbon energy sources and major nutrients (TN) were key factors driving the microbial community composition. Most ARGs were found at sampling sites S1 and S2, which was related to the pollution source. External pollutants were imported from the Zaohe River into the Weihe River and then were easily absorbed or bound by small molecules such as clay minerals, hydrated oxides, and dissolved organic matter after being recharged by the surface water and groundwater. The combined effect of these pollutants may become a potential incentive for the development of ARGs. Furthermore, the TOC in the clay can regulate the production and spread of ARGs. The plasmid DNA adsorbed onto the clay particles can escape the risk of transformation and degradation due to the physical protection of TOC, thereby regulating the occurrence and permanent dissemination of ARGs in the environment (Mao et al. 2014). In addition, the low concentration of DO indicated serious water pollution and the proliferation of anaerobic bacteria (Jia et al. 2018). The microbial community structure in the HZ was influenced by the response to nitrogen speciation and DO for microbial-mediated reactions, which were jointly controlled by the chemistry of the solution and sediment and fluid residence time.
As an urban river, the Weihe River is particularly susceptible to the effects of human activities. In addition, WWTPs also typically represent anthropogenic activity. The continuous discharge of wastewater has significantly altered the quality of urban river habitats. The unique environmental characteristics created by the periodic interaction of surface water and groundwater in the HZ and the environmental factors (TOC, TN, DO, and particle size) greatly contribute to the distribution and diversity of ARGs as a result of coregulatory effects. In fact, shifts in the composition of the microbial community under the influence of environmental factors represent the internal driving force. Nevertheless, the weak effects of antibiotics on ARGs should not be overlooked. With the rise in antibiotic accumulation, the role of antibiotics in the spread of ARGs is increasingly important.
3.4 Heavy metals and ARGs
Analyzing the six heavy metals in the sediments of HZ, the concentrations of heavy metals at the 10 sampling points showed similar characteristics. (Fig. S4) From the average point of view, the order is: Zn > Cr > Cu > Pb > Ni > Cd; The lower Cd may be due to their low adsorption capacity and accumulation rate in sediments (Li et al. 2018). The Zn and Cr with highest concentrations will show a certain degree of toxicity to the entire biological group. In addition, the speciation of heavy metals is more worthy of attention. The occurrence, valence and speciation of heavy metals is an important basis for understanding the impact of heavy metals on microbial communities and ARGs. In Fig. 8, there is little difference in the distribution of heavy metal at different sampling points, but the composition of different metal is slightly different. The composition of Cd and Zn is relatively uniform, and The other metals are mainly distributed in the residual fraction (F5) and organically bound fraction (F4). The exchangeable fraction (F1) of Cd, Zn and Ni have a significantly higher proportion. The larger proportion of "unsteady states" indicates that these metals are more likely to migrate and cause harm to the environment and human health.
Metals and antibiotics which be adsorbed in sediments will accelerate the selection pressure of ARGs via cross-selection and co-selection mechanisms, leading to the rapid spread of ARGs in bacterial communities (mediated by HGT and VGT). Therefore, it is of great significance to analyze the relationships of ARGs, heavy metals and their speciation (Fig. 9). In terms of total amount, except for Cr, the other 5 heavy metals are negatively correlated with ARGs and MGEs. Among them, aadA-01, sul1, sul2, tetX, tnpA-03 and tnpA-04 are significantly positively correlated with Cr (r > 0.6, p < 0.05), indicating that Cr can promote the production and proliferation of ARGs. The content of Cr in sediments ranges from 27.04 to 94.19 mg/kg. Due to the non-biodegradable, the high concentration of Cr in the sediment can provide long-term selective pressure for bacteria. According to the degree on the selection of ARGs, the six metals roughly behave in the order: Cr > Pb > Cd > Ni > Cu > Fe. However, Ohore et al. (2020) found that the degree of influence of metals on ARGs is Cd > Ni > Cu > Zn > Cr. This different research result shows that the impact of heavy metals on soil and sediments is more dependent on specific environmental characteristics. In terms of the speciation of heavy metals, there is no significant correlation between the exchangeable fraction (F1) and ARGs (p > 0.05). The carbonate binding fraction (F2) of Cr and Zn has a significant positive correlation with mexF. The host bacteria of mexF are generally multi-drug resistant bacteria. The F2 fraction is more likely to be released into the environment and compared with total extractable state, especially bioavailable metals are more effective in regulating the occurrence and distribution of ARGs (Guo et al. 2018). The iron-manganese oxidation fraction (F3) shows a significant positive correlation with aac(6)-Ib-3 and aadA-01 in Cr, and a significant negative correlation with tetM-01 in both Cd and Pb. The organic binding fraction (F4) only showed a significant positive correlation with aadA-01 and sul1 in Pb. The residual fraction (F5) has a certain significant correlation in other metals except Cu, especially the Ni and sul1, sul2, tetX, tnpA-03, tnpA-04 all show a very significant positive correlation (r > 0.5, p < 0.01). On the one hand, the residual Ni is relatively stable with a relatively large proportion in the environment. On the other hand, it also promotes the production of two transposases (tnpA-03 and tnpA-04) and intI1, and accelerates the frequency of horizontal transfer of ARGs. In recent research, Ohore et al. (2020) have found that Ni has a strong influence on the selection of ARGs. However, the analysis of the occurrence speciation has not been carried out. From the results of this study, it is more likely that the residual fraction of Ni plays a leading role in ARGs. The factors that affect the occurrence of ARGs are not single, and follow-up studies need to pay more attention to antibiotics, MGEs and heavy metals speciation, especially the synergy between the environmental factors of production and transmission
3.5 The roles of MGEs in the HGT of ARGs in different media
To explore the relationship between ARGs and MGEs in different media, a correlation analysis of 13 ARGs and 3 MGEs with a high detection rate was carried out (Fig. 10, Table S3). According to the results, intI1 had a good correlation with ARGs in all media. The intI1, aadA-01, blaOXA-1 of water and three tetracycline ARGs (tetG-02, tetM-01 and tetX) of sediments had a significant positive correlation. Research has suggested that aadA can form a gene cassette with other ARGs and then generate multidrug resistance genes, which are easily captured by integrons. The resistance gene cassette carried by intI1 mainly encodes resistance genes related to aminoglycosides, β-lactams and chloramphenicol resistance (Zheng et al. 2018). Tetracyclines are widely used in humans and animals, and gram-negative bacteria are sensitive to them. Regardless of whether the water phase, sediment phase or both phases were considered, four ARGs (aadA-01, sul1, sul2, and tetX) showed a significant positive correlation with intI1, among which sul1 and sul2 were extremely significantly correlated (r = 0.880, p = 0.000; r = 0.821; p = 0.000, respectively). This is because most of the intI1 contains sul1 at the 3' end. The intI1 in proteobacteria is captured by the transposon Tn402, and the transposon and integrons integrate into a sulfa-resistant gene, such as sul1 (Rosewarne et al. 2010). There was a high correlation between intI1 and the abundance of all ARGs except for aac(6')-Ib-3, aadA2-03, catB3, msr(D), and mexF, indicating that the spread of ARGs was mainly attributed to the transfer of the integron in the HZ. Research shows that intI1 is a very important pathway for the proliferation of ARGs, especially under certain selective pressure (Cesare et al. 2016). Therefore, the type of integron may reflect the interference intensity of human activities on the microbial community. In addition to being the genes necessary for transposition, transposons also carry other specialized genes, including ARGs and metal-resistance genes (Fig. 10). In the water phase, the tetracycline ARGs (tetM-01, tetX) showed a very significant correlation with tnpA-03 (r = 0.763, p = 0.000; r = 0.589, p = 0.004). tetM and tetW are always present in transposons and other MGEs (Gao et al. 2012). In the sediment, most ARGs, such as aac(6’)-Ib-3, aadA-01, sul1, sul2 and tetG-02, were significantly correlated with tnpA-04. Wang et al. (2014) found the same positive correlation and that tnpA-04 was the most enriched transposase gene in reclaimed water irrigation samples. This result indicated that WWTP effluent surroundings may be particularly likely to produce tnpA-04. In both phases together, ARGs were significantly correlated with tnpA-03 (p < 0.05), except for mexF and sul1. It is worth noting that the four ARGs of aac(6')-Ib-3, catB3, msr(D) and mexF were correlated only with transposase genes. Aminoglycosides and chloramphenicol ARGs (aac(6')-Ib-3, catB3) mostly appear in the form of gene cassettes. The macrolide resistance genes msr(D) and mexF are resistant to antibiotics through the action of the efflux pump. A previous study showed that bacteriophages can integrate msr(D) into the Streptococcus suis genome, supporting the horizontal transmission of mefA/msr(D) in different strains as well as clonal propagation (Daly et al. 2004). This is more likely to indicate that the behaviors of these four ARGs in the HZ relate to other MGEs, such as transposons and bacteriophages.
Overall, the ARGs in water were significantly related to intI1 and tnpA-03, the ARGs in the sediments were significantly related to intI1 and tnpA-04, and the ARGs in three types of media were significantly related to intI1 and tnpA-03. The transposases of tnpA-03 and tnpA-04 belong to different gene families. As the carrier of ARGs, the microbial community also plays an important role in their proliferation and transmission. Based on the Ct value of the abundance of 16S rRNA in the different phases, the comparison of the results among the phases indicated that the sediment phase had the most bacterial biomass, followed by the surface water phase and finally the groundwater phase. The HZ can introduce transposons or other MGEs harboring ARGs into the microbiome in other phases, and the high mobility and versatility of these elements may contribute to ARG propagation among the many native bacteria of the different phases. Martínez et al. (2015) considered that the ARGs residing in the MGEs harbored in human bacterial pathogens have the highest risk level. In our study, we observed significantly positive correlations between ARGs and transposase abundance. More factors driving resistance in the HZ environment should be considered to understand how resistance emerges and is disseminated to control the persistent pollution caused by ARGs.