Plasmidome derived antibiotic resistome reveals the partitioning of different geographic regions and treatment compartments in the urban water systems

doi:10.21203/rs.3.rs-5064567/v1

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Plasmidome derived antibiotic resistome reveals the partitioning of different geographic regions and treatment compartments in the urban water systems

https://doi.org/10.21203/rs.3.rs-5064567/v1

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Mobile genetic elements such as plasmids drive the dissemination and evolution of antibiotic resistance in the microbiome. And plasmid-mediated antibiotic resistance has been a growing concern in the wastewaters since urban water systems (UWSs) are known to breed and spread antibiotic resistance to the downstream rivers, which constitutes environmental exposure risks to human health. Here we investigated the plasmid derived antibiotic resistance at different treatment compartments in three comparable UWSs located in Spain, Denmark, and the United Kingdom using direct plasmid DNA metagenome sequencing. We assembled contigs (circular and linear) and removed chromosome ones using bioinformatic tools. Thereby, a plasmidome dataset was generated and analysed. We identified 225 different ARGs belonging to 180 groups of ARG families in the wastewater plasmidome. Genes conferring resistance to aminoglycoside, tetracycline, macrolide and phenicol drug contributed to more than half of the plasmidome resistome abundance across samples. The plasmid resistome richness and relative abundance detected in Spanish UWS samples were significantly (p < 0.05) higher than the other studied countries, which mirrors the remarkable domestic antibiotic use in Spain. And we only detected significant (p < 0.05) differences in ARG risk scores between the hospital and residential sewers in Spain. Meanwhile, different sewer compartments showed a partitioning role for the resistome richness and abundance distributions. Intriguingly, we perceived a group of shared ARGs among the three countries regardless of treatment stages. Further, ≥ 80% of ARG types in the wastewater treatment plants could be found in the sewer sources, which implies these ARGs were persistent in the UWSs. Overall, this study shed light on the plasmidome derived resistome as an integral part of the total resistome, and this resistome is shaped by geographic-regional and UWS-sectional variations in the UWS environment.

Environmental Engineering

wastewater

plasmidome

resistome

partitioning

treatment compartments

Unban water systems (UWSs) are designed to evacuate wastes from the human activity area (such as hospitals and residential places) to low human exposure areas and gradually reinstate them into natural watercourses. Meanwhile, abundant and diverse ARGs are also leaked and carried by the flow due to the globalization and widespread of antibiotic resistance genes (ARGs) in the natural environment and clinical settings, which has become one of the biggest health concerns in the current century ¹. UWSs as the unique conduit between humans and environments are indisputable hotspots of horizontal gene transfer (HGT) for the spread and evolution of ARGs ^{2, 3, 4, 5}. Mobile genetic elements (MGEs), especially plasmids and integrative conjugative elements (ICEs), are the primary vectors disseminating ARGs among close and/or remote phylogenetically related microbes, as well as causing the accompanying multidrug resistance (MDR) issues in the problematic nosocomial pathogens of the ESKAPEE group (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter spp., and Escherichia coli) ⁶.

Extensive studies have been paid to uncover the antibiotic residues, ARGs and antibiotic resistome (i.e., the entire set of ARG populations ⁷) in the wastewater treatment plants (WWTPs) ^{5, 8, 9}. And some researchers forethoughtfully explored the linkage among the resistome, plasmidome (all plasmids within a given environment or microbial community ¹⁰) and the microbiome in activated sludges from the WWTPs ^{11, 12}. Much work has been done to determine the specific resistome features in a particular treatment chamber in the WWTPs, especially the biological treatment processes (BTPs), which provide helpful advice for the wastewater risk management and further operational modification ^{13, 14, 15}. A nine-year longitude temporal metagenomic study deciphers the consistent relationship between the ARGs and MGEs in the activated sludges ¹⁶, although long-term characterizations of the sludges reveal variable changes in the microbiome composition ^{17, 18}. Meanwhile, Yin et al. compared the resistome and mobilome (i.e., all MGEs in a studied microbiome ¹⁹) of the influent, activated sludge and effluent samples from different WWTPs with the purpose of assessing the spatial variations within the WWTPs ²⁰. In general, the aforementioned studies comprehensively investigate the composition and distribution of the antibiotic resistome and mobilome in the WWTPs or receiving rivers, whereas lacking a full picture of taking the whole UWS into consideration, for instance, the upstream sewer catchments.

In this study, we used our previously documented UWS plasmidome dataset, which applied a direct plasmid DNA metagenome sequencing approach to assess different treatment stages in three distinct UWSs of three European countries ²¹. We benchmarked our rigorous experimental methodology for harvesting almost pure plasmid DNA from the sewer/wastewater samples ²¹. Accordingly, we believed the assembled circular and linear contigs were all plasmids (at least predominantly, there may be other MGEs included), while the linear contigs were not circularized due to sequencing depth or intrinsic unique genetic contexts. Then we removed the chromosome noise in the dataset, and the remaining pool of the contigs constituted the target plasmidome in this study. Through in silico analysis, we showed the dynamic plasmidome derived mobile resistome compositions, abundances, and distributions from sewer sources [hospital sewer (HS) and residential sewer (RS) lines], to the end of sewer lines (also known as the mixed sewer, i.e., MS, was often regarded as the WWTP influent), and finally in the BTP of the WWTP (Fig. 1A). Meanwhile, we detected the common ARGs shared by the three UWSs and inspected the epidemic origins of the ARGs emerging in BTPs by source tracking. Further, we recruited the microbiome and high-throughput qPCR datasets of the same sampling campaign ²². Accordingly, the unrevealing correlations among the plasmid-mediated antibiotic resistome, microbiome and the ‘total’ resistome were demonstrated. Overall, this study contributes to the monitoring of antibiotic resistome in the perspective of high mobility for transmission and evolution, and a manifest understanding of the mobile gene reservoir that potentially originates ARGs dissemination in the UWSs.

Sampling construction, sample pre-treatment and plasmidome sequencing

Detailed information regarding the sampling campaign and sample pre-treatment refer to a previous study ²¹. Plasmidome was probed using a direct plasmid DNA metagenomic sequencing strategy skipping transposon-aided capture and multiple displacement amplification. A dedicated plasmid purification step with an exonuclease Plasmid-Safe™ (Lucigen, Madison, WI, USA) digestion was performed to shear and remove chromosomal DNA before building the sequencing libraries ²³. An Illumina NextSeq platform (Illumina, San Diego, CA, USA) with a v2.5 sequencing kit (Illumina) was utilized to perform the plasmidome sequencing. A full description of the employed plasmidome methodology has been introduced before ²¹.

High-throughput qPCR array and microbiome documentation

Archived datasets of the high-throughput qPCR array targeting 120 ARGs and MGEs together with 16S-rRNA amplicon sequencing regarding the studied UWSs were recruited from our collaborators’ work ²².

Bioinformatic analysis

The computational work for raw reads processing and assembly for the putative circular and linear plasmid contigs was performed through the Plaspline pipeline (https://github.com/Wanli-HE/Plaspline.git) and described as previously ²¹. Gene calling for the plasmidome dataset was performed by using Prodigal (version: 2.6.3) ²⁴. Thereafter, the non-redundant gene set was generated by CD-hit (version: 4.6.2) ²⁵ with a gene sequence identity of > 90% and coverage of > 95% in each gene cluster. CARD database (version: 3.0.9) ²⁶ was then applied for ARGs annotation. Briefly, the gene abundance was calculated using the reads mapping method. Samtools (version: 1.9) ²⁷ was exploited for the observation of reads mapped to contigs. Mapped reads were filtered with a requirement of alignment length ≥ 90 bp and coverage ≥ 95%. Accordingly, a relative abundance table was generated from mSamtools (version: 0.9.6, https://github.com/arumugamlab/msamtools). A final step of normalization for the gene abundance was performed by the varying wastewater conditions (e.g., volatile suspended solids), theoretical cell volume, and carbon content per unit of cell volume according to the report by Li et al. ²².

The ARG risk score is an indicator of the possibility of wild environmental ARGs transferring and conferring resistance to human pathogens ²⁸. In light of the importance of surveillance on ARGs with high mobility ²⁰, we examined the risk scores of the plasmidome derived ARGs with MetaCompare ²⁹.

The plasmidome derived resistome ARG richness index was evaluated by “skbio.diversity” (version: 0.4.2, http://scikit-bio.org/docs/0.4.2/diversity.html) in Python ³⁰. Principal coordinates analysis (PCoA) based Bray-Curtis distance was applied to visualize the different patterns of ARG populations in different UWS compartments. Meanwhile, variations of geographic regions and UWS compartments were examined by Wilcoxon rank-sum test, and the similarities of the ARGs (ANOSIM analysis) were performed by “skbio” packages (version: 0.5.7, http://scikit-bio.org/) in python.

Resistome map of the UWS plasmidome

HS, RS, MS and BTP samples (78 in total) constructed at three reported comparable UWSs located in Denmark (DK), Spain (SP), and the United Kingdom (UK) were recruited for this study (Fig. 1A). We showed the UWS plasmidome derived antibiotic resistome with a diversity of 225 ARGs belonging to 180 groups of ARG families according to the ARO id in CARD database ²⁶. In terms of ARG richness of the entire UWS in a studied country, the plasmidome resistome in Spain was significantly (p < 0.05) higher than the other two countries, while Denmark and the United Kingdom showed no significant difference (Fig. 1B). We observed 15 ARGs that included lnu, ant(6), aph(3'), sul, aac(6'), dfr, abc-f, cat, qnr, mph, erm, tet, bla_OXA, ant(3'') and mfs, contributed > 60% to the total relative abundance of the plasmidome derived resistome at the ARG family level (Fig. 1C). Meanwhile, we found aminoglycoside, tetracycline, macrolide and phenicol were the dominant (50%) drug classes of the plasmidome resistome associated ARGs (Fig. 1D).

Specifically, we discovered that ARG richness in HS was considerably (p < 0.05) higher than RS, and RS showed no significant difference with MS in all three countries (Fig. 2A). Remarkably, ARG richness in MS was significantly (p < 0.001) higher than BTP in Spain and Denmark. Further, we illustrated the ARG richness in the three sewer lines (HS, RS and MS) were dissimilar from each other, yet MS showed high similarity to BTP in Spain and the United Kingdom (Fig. 2B). Intriguingly, ARGs in the two operated HS lines (HS_a and HS_b) in Denmark exhibited significantly (p < 0.001) different similarities. ARGs in HS_b were surprisingly similar to RS in Denmark, whereas HS_a was dissimilar from those two.

The common UWS plasmidome resistome

Since ARGs have been globally disseminated through the MGEs mediated HGT, we anticipated a shared ARG pool from different countries. Herein, we showed a number of ARGs that could be shared among different countries in every treatment compartment of the UWSs (Fig. 3). The ‘biggest share’ of common ARGs was found between Spain and Denmark in the sewer lines, while Spain and the United Kingdom shared more ARGs in the BTP. Overall, HS harboured the largest number of shared ARGs, and BTP possessed the least.

The intersection in the Venn diagrams represents the commonly shared plasmids between/among countries.

Risk scoring for the plasmid harbouring resistome in the UWSs

Risk scores were calculated and compared among different treatment compartments in each country (Fig. 4A). No significant differences in risk scores were detected among the different treatment compartments in Denmark and the United Kingdom. We exclusively discovered a remarkable (p < 0.05) difference between HS and RS in Spain.

Plasmid-borne ARGs source tracking in the UWSs

Previously, we showed source-trackable antibiotic resistance encoding plasmids in the same dataset ²¹. In this study, we employed a similar trajectory model for inspecting the ARG flow from sewer sources HS (cyan-coloured) and RS (green-coloured) to MS (pink-coloured), then to the BTP (orange-coloured) as shown in Fig. 4B. RS and HS resembled playing equivalent roles in transporting ARGs to MS in terms of ARG diversity. Approximately 53%, 36% and 63% of the ARGs in MS were retained in the BTP of Spain, Denmark and the United Kingdom, respectively. As for the BTP emerging ARGs, we discovered comparable numbers of ARG types were also found in both sewer sources, i.e., HS and RS, in Spain and Denmark, whereas a higher number of ARG types was found in RS than HS in the United Kingdom (Fig. 4B).

Linking the UWS plasmidome resistome to the microbiome and total resistome

Correlation analyses were performed for investigating the interplay of plasmidome derived antibiotic resistome with the wastewater microbiome and the total resistome (data from qPCR array ²²) in the studied UWSs. We found a significant (p < 0.0001) negative correlation between the plasmidome derived resistome richness and the microbiome richness (Fig. 5A), and a significant (p < 0.01) positive correlation between the plasmidome derived resistome relative abundance and ARG relative abundance detected by qPCR (Fig. 5B). Meanwhile, we extracted the beta-lactamase resistome by recruiting all detected beta-lactamase genes and pooling their relative abundances (Fig. 5C). We detected all four classes of beta-lactamase genes in the plasmidome (Class A: 59%, Class B: 23%, Class C: 9%, Class D: 9%). bla_OXA was the most prevalent and abundant beta-lactamase gene across the UWSs. Further, we perceived the relative abundance of plasmidome derived beta-lactamase resistome notably (p < 0.0001) positively correlated with the relative abundance of beta-lactamase genes detected by qPCR (Fig. 5D).

The dynamic plasmidome derived resistome in the UWSs

Potential roles of antibiotic use in different countries

Our previous plasmidome study has exclusively focused on the circular contigs in the dataset, while we might miss the messages from those linear contigs, such as ARGs. In the present study, we recalled all the assembled contigs and removed those that were chromosomally associated. Therefore, we generated the ‘full-version’ of the UWS plasmidome. We showed the 15 ARGs [lnu, ant(6), aph(3'), sul, aac(6'), dfr, abc-f, cat, qnr, mph, erm, tet, bla_OXA, ant(3'') and mfs] out of the detected 225 ARGs in total dominated the ‘full-version’ plasmidome derived resistome (Fig. 1C). We then focused on these 15 ARGs to censor their dynamics in the UWSs. By comparing these 15 ARGs' relative abundances between different countries in each UWS compartment, we found significant (p < 0.05) differences mainly emerged in the sewer lines, namely HS, RS and MS (Tables S1 to S3), while the relative abundances were not significantly different in BTP between different countries for most ARGs. This could be explained by the varied selection pressure caused by the sewer conditions (e.g., antibiotic residues, temperature) among different countries ¹, and BTP in the WWTPs universally removed ARGs to some extent, which would result in low abundance comparsions ³¹. Similarly, the comparison of the ARGs’ relative abundances in the drug class level showed the two sewer sources (HS and RS) were significantly (p < 0.05) different among different countries (Tables S4 to S6).

Generally, we discovered the resistome richness and relative abundance in Spain were significantly different (p < 0.05) from the other two countries considering the UWS compartments of HS and RS (Fig. 1B, Tables S1, S2, S4 and S5). This could be attributed to the fact that antibiotics have been extensively used in Spain in both community and hospital sectors ³², and higher relative abundances of ARGs in the wastewaters have been more frequently detected in Spain than in western and northern European countries ^{9, 33}. Interestingly, ARGs belonging to the glycopeptide drug class exhibited exclusively relative high abundance in HS and also in the Spanish MS (Fig. 1D), which indicates this group of ARGs was more clinically relevant and its spread of contamination in the sewer lines of Spain.

The partitioning patterns of different UWS compartments

We used a comparative analysis for the ARG relative abundances of different UWS compartments in each country. Accordingly, we found that the HS resistome relative abundance was generally significantly different from other sewers (RS and MS) and the wastewater in BTP in all three countries no matter in the ARG gene family level (Tables S7 to S9) or drug class level (Tables S10 to S12). This demonstrates the relatively high ARG load in the HS, and other researchers have reported that HS harboured more prevalent antibiotic-resistant bacteria than RS ³⁴ and more abundant ARGs were discovered in HS than RS since a more intense amount of antibiotics were used in the nosocomial environment ³⁵. RS basically showed no significant difference from MS in view of ARG relative abundances whilst they arguable exhibited dissimilarity in ARG richness (Tables S7 to S12, Fig. 2B). This implies some ARGs were lost during the flow from RS to MS, while the abundances of the retained ARGs were not significantly affected by the sewer transportation. And Auguet et al. also showed the relative concentrations of ARGs (qnrS, sul1, sul2, bla_TEM, bla_KPC, ermB, tetM and tetW) didn’t significantly decrease due to the sewer pumping in the sewer pipe ³⁶.

On the other hand, the ARG richness differed in the studied UWS treatment compartments, and we noticed an overall pattern of HS > RS, HS > MS and MS > BTP in all three countries (Fig. 2A). We were also aware of the close relations of ARG richness between MS and BTP in Spain and Denmark (Fig. 2B) and the significant differences in relative abundance between them (Tables S7 and S9). This suggests the ARG diversity was kept, while its abundance dropped remarkably after the WWTP processing in Spain and Denmark. And it could arguably indicate the BTP in the United Kingdom removed several types of ARGs and reduced ARG abundances in the meantime (Fig. 2B, Table S8). This difference emerged because a distinct BTP facility was employed in the United Kingdom (biofilter) compared to the systems used in Denmark and Spain (activated-sludge-based biological treatment basin). Although the ARG richness in the two constructed HS lines (HS_a and HS_b) was not significantly different, they demonstrated remarkable dissimilarity in the PCoA analysis based on BrayCurtis distances (Fig. 2B). ARG richness in HS_a was more ‘HS’-alike based on the observation of the ‘ordinary’ pattern of HS in the other two countries; whereas HS_a was more RS-alike. This was mainly due to the acceptance of this line was predominantly domestic sewage produced in the hospital surroundings.

MDR was prevalent along with the UWS processing (Fig. 1D). Their emergence and abundance (> 40% relatively) in the BTPs constituted the potential risk of being transferred to pathogenic bacteria considering this step usually involved high biomass and retention time ³⁷. Above all, our findings of these dynamic variations of the plasmidome derived resistome in the UWSs displayed the specific partitioning patterns of the different treatment compartments and roles of antibiotic uses in different countries. This draws attention to the necessity of monitoring transmissible resistome in different stages of UWSs to improve risk management and determine barriers to antibiotic resistance prevention ²⁰.

Plasmid-borne ARGs are shared and transported as common goods in the UWSs

WWTPs are playgrounds of various ARGs and MGEs ^{5, 38}. In light of our discovery of shared plasmids from different countries along with the UWS processing ²¹, we thereby detected the possible shared pools of ARGs. We illustrated that there was always a common reservoir of shared plasmid-borne ARGs in every UWS compartment (Fig. 3), suggesting that these shared ARGs have been internationally spread. Considering the potential risks brought by the detected ARGs in the local circumstance, we then applied MetaCompare to uncover the relatively different environmental resistome risks associated with each assessed plasmidome sample and matrix at different UWS compartments ²⁹. Ekwanzala et al. reported the highest resistome risk score of a South African UWS was observed in the HS (mean = 46.34%), followed by the WWTP inlet (mean = 39.67%), WWTP’s BTP (mean = 33.53%), WWTP effluent (mean = 28.84%) and riverbed sediment (mean = 25.55%) ³⁹. Herein, we displayed the general pattern of high resistome risk scores in different UWSs (Fig. 4A). We didn’t detect significant differences among different treatment compartments except between the raw sewers (HS vs RS) in Spain. This, again, describes the high ARG contamination in the Spanish HS.

ARGs as cargo genes located on the plasmid manifest beneficial traits for the host’s survival during environmental selection ¹. Therefore, the plasmid-borne ARGs’ dynamics are closely related to the plasmid fitness and persistence, and here we anticipated some ARGs would be constantly found in different UWS compartments since we have discovered persistent AR plasmids in the UWSs ²¹. We applied an ARG trajectory analysis and observed RS and HS both played important roles in spreading different types of ARGs to MS. More than half of the ARG types from the MS flow remained in the BTP of Spain and the United Kingdom, while only ca. 36% of the ARG types in MS were retained in Denmark (Fig. 4B). These maintained ARGs constituted a potential transmissible resistance pool to the post-WWTP facilities and the WWTP downstream surface waters ². In an attempt to identify such a pool’s origin, we applied the so-called ARG source tracking analysis to check whether these ARGs were also discovered in the sewer sources. A comparably high number of BTP-retained ARGs were found in HS and RS in Spain and Denmark, while more BTP-retained ARGs could be found in RS than in HS in the United Kingdom. It is thus critical to acknowledge the ARGs that emerged in the BTPs have a genetic background in the sewer sources. However, further analyses are needed in order to understand these ARGs' genetic context, positioning on the plasmids (co-located and distance with other MGEs) and involvement in horizontal transmission.

Plasmid derived resistome frames the total resistome

ARGs harbouring plasmids can functionally shape the microbiome community in some specific environment and contribute to the ever evolution resistome, resulting in MDR ¹⁰. Associations have been investigated between ARGs-encoding plasmids and bacterial communities using various target or nontarget experimental approaches ⁴⁰. However, facts underlying the success of many of these associations remain unknown. One may apply the abundance-trending correlation analysis of the plasmidome and a corresponding microbial community profiling dataset to decipher the link between detected plasmids and their potential hosts ^{2, 8}. Although spurious correlations would occur throughout the calculations ⁴¹, such analysis has been constantly employed to explore the linkages of resistome, plasmidome and the microbiome ⁴².

In this study, the plasmidome resistome richness significantly (p < 0.0001) negatively correlated with the corresponding microbiome richness (Fig. 5A), which illustrates the

probable emergence of plasmid fitness cost in the populations when the microbiome is more diverse (under an alleviated selective environment), while the resistome develops and expands its map in specific groups of microbes (under possibly multiple selective environments). On the other side, the plasmidome resistome relative abundance significantly (p < 0.01) positively correlated with the qPCR documented ARG relative abundance (Fig. 5B), this evidences the role of plasmidome resistome contributing to the total resistome.

Currently, beta-lactams are one of the most widely used groups of antibiotics in the world. Beta-lactamase is the major resistance determinant for beta-lactams in Gram-negative bacteria ⁴³. Critically, beta-lactamases can often ride on plasmids and co-occur with other types of ARGs ^{44, 45}, creating difficulties in clinical therapies. Herein, we presented a highly prevalent and abundant pattern of Class D beta-lactamase gene bla_OXA in the UWS plasmidome regardless of compartment and country (Fig. 5C). In addition, bla_MOX and bla_IMP were also abundant in the sewer lines though both were eliminated in the BTPs in Spain and Denmark. Meanwhile, we showed a similar finding in Class A and Class B beta-lactamase genes accounting for the most part for the beta-lactamase resistome as detected by high-throughput qPCR ²². Moreover, a significant (p < 0.0001) positive correlation was drawn between the relative abundance of the plasmidome derived beta-lactamase resistome and the relative abundance of beta-lactamase genes detected by qPCR (Fig. 5D). Consequently, for the other time, we proved the plasmidome resistome was an integral part of the total resistome and plasmid facilitated beta-lactamase genes were core members of the beta-lactamase resistome.

Overall, our discoveries highlight the indispensable role of plasmidome derived resistome in the total antibiotic resistome. Regarding this case study, the UWS plasmidome derived resistome was dominantly shaped by the regional variation (i.e., country’s antibiotic use) and the treatment compartments. The emergence of common and persistent plasmid harbouring ARGs during the UWS processing implies the prevalence and propagation of plasmid-mediated ARGs in the UWS environment, which emphasises the urgency to implement regular surveillance and risk management that to be appropriate for the different regions and treatment stages ^{20, 33}.

Competing Interests
The authors declare no other competing financial interests.

Contributions

ZFY constructed the pre-treatment, sequencing library preparation for the samples, and wrote the manuscript; WLH performed the computing analysis and produced the figures; JN and FK gave recommendations for the bioinformatic pipelines and statistical analysis; WK and LHH processed the plasmidome sequencing and produced the raw reads; AD organised regular meetings and shared the metadata files regarding the sampling campaigns; BS and SJS made the initial proposal of this study; all the co-authors have reviewed and gave suggestions for this manuscript.

Acknowledgements

This research was funded by a Joint Programming Initiative-Antimicrobial Resistance grant (JPI-AMR; DARWIN project #7044-00004B) to BFS, and the DFF-Research Project 2 Grants from the Danish Council for Independent Research | Technology and Production (7017-00210A) to BFS.

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The authors declare no competing interests.

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Plasmidome derived antibiotic resistome reveals the partitioning of different geographic regions and treatment compartments in the urban water systems

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Sampling construction, sample pre-treatment and plasmidome sequencing

High-throughput qPCR array and microbiome documentation

Bioinformatic analysis

Results

Resistome map of the UWS plasmidome

The common UWS plasmidome resistome

Risk scoring for the plasmid harbouring resistome in the UWSs

Plasmid-borne ARGs source tracking in the UWSs

Linking the UWS plasmidome resistome to the microbiome and total resistome

Discussion

The dynamic plasmidome derived resistome in the UWSs

Potential roles of antibiotic use in different countries

The partitioning patterns of different UWS compartments

Plasmid-borne ARGs are shared and transported as common goods in the UWSs

Plasmid derived resistome frames the total resistome

Declarations

Competing Interests
The authors declare no other competing financial interests.

Contributions

Acknowledgements

References

Additional Declarations

Supplementary Files

Status:

Version 1

Plasmidome derived antibiotic resistome reveals the partitioning of different geographic regions and treatment compartments in the urban water systems

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Sampling construction, sample pre-treatment and plasmidome sequencing

High-throughput qPCR array and microbiome documentation

Bioinformatic analysis

Results

Resistome map of the UWS plasmidome

The common UWS plasmidome resistome

Risk scoring for the plasmid harbouring resistome in the UWSs

Plasmid-borne ARGs source tracking in the UWSs

Linking the UWS plasmidome resistome to the microbiome and total resistome

Discussion

The dynamic plasmidome derived resistome in the UWSs

Potential roles of antibiotic use in different countries

The partitioning patterns of different UWS compartments

Plasmid-borne ARGs are shared and transported as common goods in the UWSs

Plasmid derived resistome frames the total resistome

Declarations

Competing Interests The authors declare no other competing financial interests.

Contributions

Acknowledgements

References

Additional Declarations

Supplementary Files

Status:

Version 1

Competing Interests
The authors declare no other competing financial interests.