Spatiotemporal Variability in Wastewater-Derived Carbapenem-Resistance Genes from Diverse Municipal Sources in the Laurentian Great Lakes Catchment

Spatiotemporal Variability in Wastewater-Derived Carbapenem-Resistance Genes from Diverse Municipal Sources in the Laurentian Great Lakes Catchment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatiotemporal Variability in Wastewater-Derived Carbapenem-Resistance Genes from Diverse Municipal Sources in the Laurentian Great Lakes Catchment Ethan Harrop, Qiudi Geng, Ryland Corchis-Scott, Mackenzie Beach, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8206721/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Antimicrobial resistance is quickly becoming one of the largest threats facing global health. To combat this threat, surveillance is necessary to understand the presence of potential antimicrobial resistance beyond what is identified in clinical isolates. Using wastewater-based surveillance, we conducted a year long sampling campaign of four critical concern carbapenem-resistance genes at five sites to determine spatiotemporal patterns. Environmental factors were also examined to identify potential influencers of carbapenemase gene concentrations in the wastewater. Non-metric multidimensional scaling (NMDS) revealed that these antimicrobial resistance genes exhibited significant site-specific, but not seasonal, clustering. Further investigation into seasonal variation revealed that gene concentrations were significantly different between season and displayed monotonic changes. The four carbapenemase genes did not exhibit similar trends or concentrations across seasons or treatment plants, but all underwent large day-to-day fluxes. Using distance-based redundancy analysis (dbRDA), environmental factors were able to explain ~ 40% of the variation in gene profiles. However, each gene had differing correlations to all of the environmental factors studied here. These results indicate that a complex matrix of factors influence each antimicrobial resistance gene in a unique way with no consistent spatiotemporal patterns across the carbapenemase gene class. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Widespread overuse of antibiotics has resulted in the emergence, selection, and global dissemination of antimicrobial resistance (AMR) amongst bacterial pathogens. The rapid spread of AMR paired with limited advances in new antimicrobial development poses a major threat to the security of modern medicine [1]. In 2019, AMR was estimated to be directly attributable to 1.27 million deaths and associated with an additional 3.68 million deaths [2]. If left unchecked, AMR will be responsible for an estimated 10 million deaths annually by 2050 [3]. The threat posed by AMR prompted the World Health Organization (WHO) to develop a global action plan against AMR, leading many countries to do the same [4–6]. One major objective of these action plans is to understand the prevalence of AMR through large scale surveillance. The first surveillance initiative was the Global Antimicrobial Resistance and Use Surveillance System (GLASS). Organized by the WHO, GLASS receives clinical isolates form 129 countries to monitor the global AMR landscape [7]. Since then, many countries have developed their own surveillance systems, including the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) and the National Antimicrobial Resistance Monitoring System (NARMS) in the USA [8,9]. However, many of these initiatives monitor only human or animal clinical isolates with environmentally focused studies just recently gaining attention [10]. This limited sampling strategy does not represent the true presence of AMR as it ignores both asymptomatic carriers and pathogens that are present in the environment. Thus, a One Health approach incorporating both community wide surveillance and environmental monitoring is necessary to more thoroughly understand the current breadth of AMR and asses the effectiveness of future AMR stewardship programs. Antibiotic resistant bacteria (ARB) can be deposited into the environment by human or animal hosts, accelerating the proliferation and spread of AMR. Environmental reservoirs play a key role in the reinfection cycle as AMR can be passed from the environment back to humans and animals [5]. ARB have been found in the natural and the built environment including soil, surface water, ground water, atmospheric aerosols, hospitals, agricultural land, and sewers [11–17]. Each unique environment fosters its own diverse community of ARB and the antimicrobial resistant genes (ARGs) they harbour [18]. Disparities between ARB and ARGs even exist in closely related environments, such as hospitals and their receiving sewersheds [19–21]. This discrepancy means that clinical antibiotic prescription rates and clinically isolated ARB cannot directly translate to community or environmental ARB/ARG load. Selective pressures such as industry related heavy metal pollution and antimicrobials and their residues from clinical settings, agricultural runoff, and animal waste have previously been shown to escalate the development and spread of AMR through horizontal gene transfer (HGT) [22]. However, emerging research suggests these constituents have little influence over the presence or proliferation of ARB and ARGs within the wastewater [23–26]. Instead, there is increasing evidence that climatic and physical factors, such as temperature, precipitation, climate zone, and climate change, optimize ARB growth and HGT in the wastewater [27,28]. As such, sewers and wastewater treatments plants (WWTPs) are important conduits between human and environmental antimicrobial resistomes where clinically developed AMR is subject to environmental spread. Wastewater surveillance (WS) monitors biomarkers of pathogens and chemical residues from a well-defined community by collection of their waste from a WWTP. WS was first used in 1939 for monitoring of poliovirus but gained traction on a global scale during the COVID-19 pandemic [29]. WS of SARS-CoV-2 bypassed the myriad challenges associated with clinical data while having similar trends, providing actionable data to public health officials and healthcare providers [29,30]. Since then, WS has been used to monitor a multitude of other pathogens including influenza, respiratory syncytial virus, and ARB/ARGs [23,24,31–34]. WS of ARB and ARGs has been employed worldwide using an array of molecular and cultivation approaches, making it easily accessible to many laboratories [35]. Using WS in tandem with clinical testing facilitates a deeper understanding of AMR abundance within the community and includes AMR harboured by asymptomatic carriers and wastewater bacterial communities. In 2018, the WHO released a priority list of ARB that are in urgent need of new antibiotic development [1]. Bacterial species harbouring ARGs protecting against carbapenems were identified as a critical concern [1]. Carbapenems are β-lactam antibiotics frequently used as a last resort to treat multidrug resistant bacteria. Carbapenemases are a group of antibiotic-resistant enzymes that facilitate hydrolysis of carbapenems and other β-lactam antibiotics, reducing their effectiveness [36]. Carbapenemases fall into three classes: class A serine B-lactamases, class B metallo B-lactamases, and class D oxacillin-hydrolyzing B-lactams, with five clinically prominent genes: Klebsiella pneumonia carbapenemase (KPC; class A), New Delhi metallo-beta-lactamase (NDM; class B), Verona integron-encoded metallo-beta-lactamase (VIM; class B), imipenemase (IMP; class B), and oxacillinase-48 (OXA-48, class D) [36,37]. These genes were labelled critical concern due to their ubiquitous presence and effective antimicrobial resistant activity [38]. We hypothesized that carbapenemase gene profiles in wastewater were spatiotemporally unique and that wastewater carbapenemase resistomes were influenced by environmental factors. This was tested through the longitudinal monitoring of four critical concern carbapenemase genes, bla KPC , bla NDM , bla VIM , and bla OXA−48 . Monitoring was carried out at five municipal wastewater treatment sites across two countries and two Köppen climate zones within the Laurentian Great Lakes catchment to develop an understanding of community level spatiotemporal trends [39]. Environmental conditions of monitored municipalities and physicochemical conditions of the wastewater were examined to identify potential factors that influence the abundance of carbapenemase genes at WWTPs. 2. Methods 2.1 Sample collection Wastewater samples were collected from two WWTPs in Ontario, Canada, and one facility in Detroit, Michigan into which three interceptors drain raw influent from separate sewersheds (Table 1 ). A 1L sub-sample of a 24-hour composite screened-influent sample was collected once weekly from LPCC, NIEA, OAK, and JEFF and twice weekly from TB from June 2023 - June 2024. Following collection, all samples were transported to the lab on ice where they were immediately processed. No wastewater sample was collected from NIEA, OAK, or JEFF between May 19, 2024 – May 25, 2024. Table 1 Location and population served by WWTPs Facility Location Population Served Leamington Pollution Control Centre (LPCC) Leamington, Ontario, Canada 20 000 Thunder Bay Water Pollution Control Plant (TB) Thunder Bay, Ontario, Canada 98 000 Detroit Water Resource Recovery Facility (WRRF) North Interceptor-East Arm (NIEA) Detroit, Michigan, USA 1 482 000 Oakwood-Northwest-Wayne County Interceptor (OAK) 840 600 Detroit River Interceptor (JEFF) 492 000 2.2 Sample processing Composite wastewater samples were concentrated using 0.22µm Sterivex PES cartridge filters (MilliporeSigma, Burlington, MA, USA). Sterivex cartridges were attached to 60mL syringes and placed in a caulking gun to aid in filtration. Filtered volume ranged from 50mL − 180mL. After all volume had passed through, cartridges were sealed and flash frozen in liquid nitrogen before nucleic acid extraction. The Sterivex cartridges were thawed, and the filter membrane was cut away for subsequent nucleic acid extraction. Nucleic acids were extracted from filters using the AllPrep PowerViral DNA/RNA kit (Qiagen, Germantown, MD, USA) modified by the addition of 5% 2-mercaptoethanol (v/v) or the RNeasy PowerMicrobiome Kit (Qiagen) modified in the same way. DNase steps were excluded, and total nucleic acid was eluted in 50µL of nuclease-free water. 2.3 qPCR Two quantitative PCR assays were used to measure the concentration of four carbapenem-resistance genes in wastewater samples. A triplex assay targeted bla KPC , bla NDM , and bla VIM−7 , while a duplex assay targeted bla OXA−48 and bla VIM . The bla VIM−7 variant is markedly different from the other bla VIM variants [40]. For this reason, the two bla VIM assays were left separate during further analysis. Reaction wells contained 5µL of extracted DNA (diluted 1:10 with molecular grade water to relieve PCR inhibition) mixed with 10µL of Luna Universal Probe One-Step Reaction Mix (2x, New England Biolabs [NEB], Ipswich, MA, USA), and primers and probes for a final volume of 20µL. All primers and probes were obtained from Integrated DNA Technologies (IDT, Coralville, IA, USA) (Table 2 ). All samples were run in technical triplicates on a MA600 thermocycler (Aumintec, Richmond Hill, ON, Canada). For the triplex assay, polymerase activation was performed at 95°C for 20 sec, followed by 45 cycles of denaturation and annealing/extension at 95°C for 5 sec, and 55°C for 30 sec, respectively. For the duplex assay, polymerase activation was performed at 95°C for 20 sec, followed by 45 cycles of denaturation and annealing/extension at 95°C for 5 sec, and 53°C for 30 sec, respectively. Custom gBlocks (IDT) were diluted in molecular grade water to create an 8-point standard curve to quantify each target ( Table S1 ). No template controls yielded no amplification. The limit of detection (LOD) and limit of quantification (LOQ) for each target was determined though examination of 20 standard curves (Table 2 ). The LOD was defined as the lowest concentration that can be consistently detected with 95% probability and the LOQ was defined as the lowest concentration that can be quantitatively determined with 35% CV. For all samples, an RT-qPCR assay was also performed for Pepper Mild Mottle Virus (PMMoV). PMMoV was used as a standard human fecal indicator to normalize signal related to physicochemical variability associated with the wastewater stream [41]. PMMoV reaction wells contained 2.5µL of extracted RNA (diluted 1:5 with molecular grade water to relieve PCR inhibition) mixed with 10µL of Luna Universal Probe One-Step Reaction Mix (2x), 1µL of Luna WarmStart RT Enzyme Mix (20x, NEB), and primers and probes for a final volume of 20µL. Table 2 Primer and probe sequences Assay Target Primer/ Probe Sequence Final Concentration (nM) Amplicon Size LOD (Copies/µL) LOQ (Copies/µL) Reference Triplex bla KPC KPC-F 5’-GGC CGC CGT GCA ATA C-3' 800 60 1.9 4.8 CDC[42] KPC-R 5’-GCC GCC CAA CTC CTT CA − 3' 800 KPC-P 5’FAM-TGA TAA CGC CGC CGC CAA TTT GT-3’ 400 bla NDM NDM-F 5’-GCA AAT GGA AAC TGG CGA CC-3' 800 274 1.4 3.1 Lund et al.[40] NDM-R 5’-TAC CGC CCA TCT TGT CCT GA-3' 800 NMD-P 5’HEX-TCG CAC CGAATG TCT GGC AGC ACA-3’ 400 bla VIM−7 VIM-7-F 5’-GAG TTG CTT CTT ATT GAT ACA G-3' 800 177 2.6 3.7 Lund et al.[40] VIM-7-R 5’-AGG GTG AGG TGT ACG TTG C-3' 800 VIM-7-P 5’Cy5-CCG ACT CGA TCG TCA TGG AAG TGC GT-3’ 400 Duplex bla OXA−48 OXA-48-F 5’-CTT AAA CGG GCG AAC CAA GC-3' 600 93 1 4.5 Lund et al.[40] OXA-48-R 5’-GTT CAT CCT TAA CCA CGC CC-3' 600 OXA-48-P 5’FAM TTC CCA ATA GCT TGA TCG CCC TCG ATT-3’ 600 bla VIM VIM-F 5’-TTG ATT GAT ACA GCT TGG GGT A-3' 1200 168 3.3 9.5 Lund et al.[40] VIM-R 5’-ACG GYG ATG CGT ACG TTG C-3' 1200 VIM-P 5’Cy5-GAC GCG GTC GTC ATG AAA GTG CGT-3’ 600 PMMoV PMMoV PMMoV-F 5’ -GAG TGG TTT GAC CTT AAC GTT TGA-3’ 200 67 1 3.2 Haramoto et al.[43] PMMoV-R 5’-TTG TCG GTT GCA ATG CAA GT-3’ 200 PMMoV-P 5’Cy5-CCT ACC GAA GCA AAT G-3’ 200 2.4 Wastewater physicochemical parameters and environmental factors In addition to sample collection, partners at the WWTPs provided physicochemical data of the wastewater including temperature (°C), flow rate (million L/day), pH, total phosphorous (mg/L), ammonia (mg/L), carbonaceous biochemical oxygen demand (CBOD, mg/L), and total suspended solids (TSS, mg/L). All wastewater physicochemical properties were averaged by week, ending on the day the composite sample started. Precipitation (mm) and surface temperature (°C) data were obtained from a publicly available weather database [44]. The date range was set to the week leading up to the sample, ending on the day the composite sample was started. Surface temperature measurements were an average of the weekly minimum and maximum temperatures. Precipitation measurements were the total weekly precipitation. The cities used to represent the different Great Lakes Water Authority WRRF interceptors from Michigan are as follows: NIEA – Waterford, MI, USA, OAK – Livonia, MI, USA, and JEFF – Detroit, MI, USA. At LPCC, no ammonia data were available from June 26, 2023 - July 2, 2023, no CBOD data were available from September 25, 2023 – October 1, 2023, and no phosphorous data were available from December 18, 2023 – December 24, 2023. At TB, no influent wastewater temperature data were available from January 10, 2024 – January 16, 2024, due to scheduled maintenance of the WWTP. Sampling points affected by missing physicochemical and environmental factors (PEFs) were omitted from distance-based redundancy analysis (dbRDA) analysis and any Spearman’s correlation analysis involving them. 2.5 Data analysis and visualization All data analysis was performed using R version 4.4.1 with assistance from the tidyverse package for data manipulation and organization [45]. All data visualizations were made using the ggplot2 package with additions from the RColourBrewer package. The rstatix package was used to perform Dunn’s test as a pairwise follow up to Kruskal-Wallis tests to determine statistical significance between groups (p < 0.05). The ggpubr package was used to add the results of Dunn’s test to boxplots. Spearman’s correlation coefficients and p-values between ARGs and PEFs were obtained using the base R functions ‘cor’ and ‘cor.test’, respectively. The trend package was used to preform the Mann-Kendall test for monotonic trends. The vegan package was used to make a Hellinger transformed Bray-Curtiss dissimilarity matrix which was then used for non-metric multidimensional scaling (NMDS), the accompanying PERMANOVA, and dbRDA. Pairwise PERMANOVA tests for NMDS were run using the pairwiseAdonis package. Sampling weeks that were missing a PEF measurement were removed for those sites during dbRDA analysis and for Spearman’s correlations involving those parameters. Additionally, no wastewater sample was collected from NIEA, OAK, or JEFF between May 19, 2024 – May 25, 2024, so this sampling week was removed from Spearman’s correlations involving these sites. 3. Results Between June 2023, and June 2024, a total of 302 wastewater samples were collected from three WWTP interceptors in Michigan and two WWTPs in Ontario. For the purpose of analysis, the year was divided into calendar seasons: summer (June 19, 2023 - September 22, 2023), fall (September 23, 2023 – December 20, 2023), winter (December 21, 2023 - March 19, 2024), and spring (March 20, 2024 – June 19, 2024). In addition to analyzing ARG concentrations, ARGs were normalized to PMMoV concentrations to account for variability in the wastewater environment, dynamic population changes, and sample processing [46]. PMMoV was chosen because it is a marker of human fecal contamination that has been widely used as a normalizing agent for WS and has relatively stable concentrations at the studied sites ( Fig. S1 ) [31,32,47]. 3.1 Spatial variability of carbapenemase genes bla KPC and bla OXA−48 were detected in 100% (302/302) of the samples, bla NDM was detected in 95% (286/302) of the samples, bla VIM was detected in 54% (163/302) of the samples, and bla VIM−7 was detected in 64% (194/302) of the samples. A majority of the non-detects for bla VIM and bla VIM−7 occurred at LPCC and TB. Generally, bla KPC had the highest concentration at all WWTPs followed by bla OXA−48 , bla VIM (where it was detected), bla NDM , and finally bla VIM−7 . Regardless, the average concentrations of ARGs were significantly different across a majority of WWTPs (Fig. 1 a, p < 0.05). The yearly average concentrations for each ARG can be found in Table 3 . Trends in ARG concentration did not change when normalized to PMMoV (Fig. 1 b). However, normalization changed the degree of significance between some WWTPs, in some cases causing pairing to become significant or non-significant. Table 3 Average concentrations and standard deviations for ARGs across the sampling campaign Average Gene Copies/L ± Standard Deviation bla KPC bla NDM bla OXA−48 bla VIM bla VIM−7 LPCC 4.35E + 08 ± 3.84E + 07 1.02E + 05 ± 1.16E + 05 9.51E + 06 ± 8.44E + 06 7.24E + 02 ± 6.78E + 03 2.32E + 02 ± 8.03E + 02 TB 1.27E + 07 ± 6.50E + 06 1.30E + 05 ± 1.13E + 05 5.14E + 06 ± 4.86E + 06 3.82E + 02 ± 4.63E + 03 2.27E + 02 ± 1.78E + 03 NIEA 2.45E + 08 ± 9.57E + 07 1.48E + 06 ± 1.98E + 06 1.14E + 07 ± 8.64E + 06 5.21E + 06 ± 5.54E + 06 4.65E + 04 ± 2.74E + 04 OAK 1.63E + 08 ± 9.72E + 07 7.83E + 05 ± 1.17E + 06 6.78E + 06 ± 5.59E + 06 1.71E + 06 ± 2.12E + 06 2.10E + 04 ± 1.31E + 04 JEFF 1.67E + 08 ± 1.74E + 08 2.56E + 05 ± 1.70E + 05 1.64E + 07 ± 1.58E + 07 8.19E + 05 ± 1.49E + 06 1.38E + 04 ± 1.10E + 04 Spearman’s Rho (ρ) was used to determined correlations of individual ARGs between WWTPs (Fig. 2 , Table S2 ). The strongest positive correlation was observed between bla OXA−48 at NIEA and OAK (Spearman’s ρ = 0.797, p = 1.57e-12), whereas the most negative correlation was between bla VIM−7 at TB and OAK (Spearman’s ρ = -0.229, p = 0.102). Correlations between bla NDM had the least variability with Spearman’s ρ ranging from 0.382 (NIEA and JEFF, p = 0.005) to -0.088 (TB and LPCC, p = 0.530). For all ARGs, correlations between NIEA, OAK, and JEFF were often stronger than those involving LPCC or TB. The strongest correlation for all ARGs except bla NDM occurred between NIEA and OAK. bla KPC , bla NDM , and bla OXA−48 at the Michigan sites had stronger correlation to TB than to LPCC despite TB belonging to a separate Köppen climate zone [39]. The same trend was likely not seen with bla VIM and bla VIM−7 due to the lack of detections at TB and LPCC. When normalized, the strongest positive correlation was seen between bla VIM−7 at NIEA and OAK (Spearman’s ρ = 0.758, p = 7.48E-11) and the most negative correlation was seen between bla VIM−7 at OAK and TB (Spearman’s ρ = -0.209, p = 0.136) ( Fig. S2, Table S2 ). Normalization had varying effects on observed correlations. The largest difference recorded was between bla KPC at NIEA and JEFF where absolute concentrations resulted in a Spearman’s ρ of 0.497 (p = 1.75E-04) and normalized concentration had a Spearman’s ρ of 0.139 (p = 0.325). For all normalized ARGs, the strongest correlation was between NIEA and OAK. NMDS was used to compare spatiotemporal differences in ARG composition (Fig. 3 ). This revealed visually distinct geographical clusters confirmed by PERMANOVA (p = 0.187 for NIEA and OAK, p = 0.003 for TB and LPCC, and p = 0.001 for all other comparisons). The only interceptors that did not have significantly different ARG profiles were NIEA and OAK. R-value cutoffs described previously indicated that sites from different countries were more separated than sites from the same country ( Table S3 ) [48]. Normalized ARGs showed the same geographical clusters (p-values same as above) ( Fig. S3 ). No significant seasonal clustering was observed for absolute or normalized ARG concentrations (p = 0.146 and 0.163, respectively). 3.2 Seasonal variability of carbapenemase genes Visual inspection of longitudinal time series showed ARG concentrations were relatively stable over the study period ( Fig. S1 ). However, comparing seasonal concentrations of ARGs revealed significant differences across seasons at one or more WWTP ( Fig. 4 ). bla NDM , bla VIM , and bla VIM−7 also showed no significant difference in seasonal concentrations at one or more WWTP. Seasons with significantly different ARG concentrations were not uniform across WWTPs. Seasons were then ranked from lowest to highest average concentration (1–4) to compare seasonal trends ( Fig. S4 ). When compared this way, bla KPC and bla OXA−48 had almost identical trends at each WWTP with the highest concentration in the summer, followed by fall and spring, and the lowest concentration in the winter. bla NDM , bla VIM , and bla VIM−7 diverged from this trend and had highest and lowest average concentrations in all four seasons across the WWTPs. However, bla VIM and bla VIM−7 had consistently lower concentrations in the fall. Normalizing ARGs changed which seasonal concentrations were significantly different but did not unify the temporal variability of ARGs across WWTPs ( Fig. S5 ). Normalization had a larger impact on ranked seasonal averages ( Fig. S4 ). For all ARGs, summer and fall rankings decreased whereas winter and spring rankings increased. Fall had the lowest sum of ranks for all ARGs except for bla NDM , where it was only slightly higher than the lowest ranked season, summer. In addition to seasonal averages, monotonic trends were assessed across the study period and within each season using the Mann-Kendall test (Fig. 5 , Table S4 ). Results of bla VIM and bla VIM−7 at TB and LPCC were removed from further analysis involving the Mann-Kendall test due to the lack of detections and low detection signal over the study period. Moderate monotonic trends were seen across the study period at some WWTPs with Kendall’s Tau (τ) ranging from − 0.391 (p = 7.12e-8, bla OXA−48 at TB) to 0.291 (p = 0.002, bla VIM at OAK). Yearlong monotonic trends were consistent in direction across ARGs with a majority (76%, 16/21) showing decreasing trends (Kendall’s τ 0), with Kendall’s τ ranging from − 0.429 (p = 0.037, bla NDM at NIEA in Summer and bla VIM at JEFF in Summer) to 0.513 (p = 0.017, bla NDM at NIEA in Fall). Monotonic trends showed similarities in direction, but not magnitude, across WWTPs and ARGs. Normalizing ARGs switched the direction of yearlong trends with 86% (18/21) showing an increasing trend ( Fig. S6, Table S4 ). Yearlong Kendall’s τ for normalized ARGs ranged from − 0.156 (p = 0.032, bla OXA−48 at TB) to 0.504 (p = 1.14E-7, bla VIM−7 at OAK). Normalized seasonal trends corroborated trends of absolute concentration with a majority (73%, 61/84) also showing increasing trends. Seasonal Kendall’s τ ranged from − 0.516 (p = 0.012, bla VIM−7 at NIEA in Summer) to 0.692 (p = 0.001, bla VIM at OAK in Fall). As seen with absolute concentration, direction of trends was similar for each ARG across WWTPs, but not across all ARGs. For most seasons, the direction of the monotonic trend was the same for normalized and absolute concentrations of ARGs. 3.3 Factors influencing changes in carbapenemase gene concentration dbRDA showed that PEFs had a significant explanatory effect on ARG composition with an adjusted R 2 of 0.398 (p < 0.001) (Fig. 6 ). Flow rate had the strongest influence on ARG composition (R 2 = 0.278, p < 0.001), whereas ammonia had the weakest influence (R 2 = 0.001, p = 0.671). CBOD, surface temperature, and ammonia were the only PEFs that did not have a significant explanatory effect on ARG profiles (p = 0.265, 0.324, and 0.671, respectively). However, all other PEFs had relatively little, albeit significant, explanatory effects on ARG composition ( Table S5 ). PEFs exerted a similar explanatory effect on normalized ARG composition (adjusted R 2 = 0.395, p < 0.001) ( Fig. S7 ). The individual effects of PEFs on normalized ARG composition were similar to the effects on absolute concentration ( Table S5 ). The only PEFs that did not have a significant explanatory effect on normalized ARG composition were CBOD, surface temperature, and ammonia (p = 0.292, 0.311, and 0.681, respectively). Correlations between ARGs, PEFs, and other ARGs at each WWTP were calculated using Spearman’s ρ (Fig. 7 , Table S6 ). Direction and magnitude of correlations showed little similarities within and between WWTPs. However, bla KPC had the highest positive correlation to wastewater temperature rather than any other PEF at all WWTPs except TB, with Spearman’s ρ ranging from 0.600 (OAK, p = 2.25E-6) to 0.298 (TB, p = 0.032). bla KPC and bla OXA−48 had similar direction and significance of correlations to PEFs with some exceptions occurring at OAK and JEFF. Most ARG to pH correlations were consistently low or negative with only bla VIM and bla VIM−7 at OAK being significantly positive. bla NDM , bla VIM , and bla VIM−7 showed consistently low or negative correlation to all PEFs with 87% (117/135) having Spearman’s ρ < 0.200. Often, these three ARGs had stronger correlations to each other than any PEFs. This was especially true for bla VIM and bla VIM−7 whose correlation ranged from Spearman’s ρ = 0.844 (JEFF, p = 3.87E-15) to Spearman’s ρ = 0.704 (NIEA, 5.79E-9) at sites where they were consistently detected. bla NDM had the highest correlation with bla KPC compared to other ARGs at four out of five WWTPs, although the strength of the correlation varied. bla OXA−48 also had the strongest correlation with bla KPC compared to other ARGs at all WWTPs, again with varied strength. Normalizing ARGs to PMMoV had a pronounced effect on correlations to other ARGs and PEFs ( Fig. S8, Table S6 ). Normalized ARGs had strong, positive correlations to each other at each WWTP, except for correlations to bla VIM and bla VIM−7 at LPCC and TB, due to the lack of detections. Additionally, many normalized ARGs had low or negative correlation to all PEFs with 68% (154/225) of ARG to PEF correlations having Spearman’s ρ < 0.100. The strongest correlation was seen between normalized bla VIM−7 and pH at OAK, where Spearman’s ρ = 0.713 (p = 3.10E-9). In contrast to absolute values, most normalized ARG to pH correlations were significantly positive. The strongest negative correlation was seen between normalized bla VIM−7 and wastewater temperature at OAK (Spearman’s ρ = -0.754, p = 1.14E-10). Normalizing ARGs did not reveal any clear, concise correlation patterns across ARGs or WWTPs. 4. Discussion This study used WS to monitor spatial and temporal trends of four critical concern carbapenemase genes at five sites over a one-year period, adding to the ever-growing knowledge base that supports WS as an effective and accurate means to monitor community wide AMR [20,23,24,33,35]. Absolute concentration of carbapenemase genes were significantly different across WWTPs with distinct geographical, but not seasonal, clusters revealed by NMDS. However, ARGs displayed differences in seasonal concentrations and monotonic trends with varying magnitudes based on target and site. While some correlations between ARGs and PEF were strong, factors were not identified that explained variation in concentration across all monitored ARGs. This study corroborates previous studies that have identified spatial differences in ARGs [24,33,34,48]. A study by the Global Sewage Surveillance project consortium compared ARG composition from 60 different countries and found a higher degree of variability between countries than within them [24]. Other studies that have conducted similar comparisons across a country or large metropolitan area have also shown that sampling points with geographical proximity show less AMR variability [33,34]. The present study is unique in that four of the monitored sites are spatially close but are separated by an international boundary. Additionally, the Michigan sites and LPCC lie in Köppen climate zone Dfa whereas TB belongs to zone Dfb [39]. This allowed a comparison of ARG profiles across international and environmental boundaries. NMDS revealed clustering of WWTPs from the same countries despite further analysis determining most comparisons to be significantly different. Conversely, Michigan sites had stronger Spearman’s correlations with TB rather than LPCC even though they belong to separate climate zones. These findings suggest that country had a larger impact on ARG profiles than climate zone. However, it has been shown previously that population size could contribute to changes in ARG composition [23]. The WRRF services ~ 3 million people whereas LPCC and TB only service 20 000 and 98 000 inhabitants, respectively, suggesting the clustering could be based on population differences as well. Normalizing ARGs to population (gene copies/day/person) before analysis had no effect on the resulting NMDS (data not shown). The geographic clusters may also be formed based on the characteristics of the municipalities. NIEA, OAK, and JEFF all contain urban centres in their sewersheds where the municipalities in the drainage area of LPCC and TB are rural settings. Future studies involving cross border communities would help to elucidate the influence of these factors on ARG profiles. Regardless, most sites were significantly different in the NMDS, indicating country, climate, and population could all contribute to spatial differences in ARG composition. The role of geography on ARG composition seems to decrease between sites within the same city. This was confirmed by studies in New York City, USA, and Barcelona, Spain suggesting this effect is not country specific [49,50]. While not the same city, this study compared closely connected communities in Michigan. Comparisons of individual ARGs as well as resistomes identified similarities between the three Michigan interceptors, but JEFF was more dissimilar than NIEA and OAK. This difference could also be due to municipality characteristics as JEFF drains the downtown core of Detroit where NIEA and OAK serve the greater metropolitan area. In contrast, no significant difference was seen between seven ARGs across all New York City boroughs [50]. However, the drainage area of New York City is three times less than that of the WRRF [51,52]. Investigation into temporal variability of ARGs revealed that all ARGs experienced seasonal variations at one or more WWTP. However, not all ARGs displayed seasonal variation at every WWTP, meaning that seasonal fluctuations in ARGs are also spatially distinct. Other studies have also reported varying results for seasonal patterns of ARGs in wastewater. Continuous, long-term studies often found that ARG levels were stable across the year, except for qnrS which had higher loads in summer compared to winter in Bath, UK [23,53]. Conversely, long-term studies with infrequent sampling found differences in ARG concentrations between seasons, though there was no consensus on seasonal patterns [50,54,55]. One reason for this discrepancy could be that ARGs exhibit large fluxes in concentration between days and weeks of sampling. Therefore, sampling one day intermittently over a study period (be it a composite or grab sample) may not be indicative of the true ARG presence and does not provide precise temporal comparisons. Additionally, looking at seasonal changes as an attribute of antimicrobial gene class rather than individual genes may generate misleading results. As observed in the present study, each of the carbapenemase genes behaved in a different way with only select attributes being shared amongst them. Wrapping all carbapenemase genes together could mask trends exhibited by individual genes or cancel out opposing trends observed between two genes. Even when targeting specific genes, seasonal trends will likely not be congruent across spatially separated sites. Ranking seasonal averages revealed that absolute concentrations peaked in summer for bla KPC and bla OXA−48 , but had more stability throughout the year for bla NDM , bla VIM , and bla VIM−7 . Varying results for seasonal trends of ARGs have been reported previously based on location and ARGs studied [23,33,50,55]. In contrast, carbapenemase-producing Enterobacteriaceae (CPE) infections occured at higher frequencies in summer and fall months across multiple, geographically distinct locations [56–59]. This implies that clinical cases of CPEs do not stimulate concentration fluxes in all carbapenemase genes in the wastewater. Clinical CPE infections may have some explanatory effect on the elevated bla KPC concentrations observed in summer and fall as a major portion of clinical CPEs and CPEs found in the wastewater harbour bla KPC genes [13,17]. However, presence of hospitals in a sewershed often has little influence on the resistome at WWTPs, with some ARGs being identified in the sewershed months before clinical detection [20,21,33,60,61]. Normalized ARGs peaked at different times of the year than the absolute concentration of ARGs with a notable increase in winter and spring averages. It is, however, difficult to determine if the seasonal variation observed in absolute or normalized ARGs is true seasonality or due to random flux. To accurately model seasonality, a time series must contain at least two cycles, or in this case two years, meaning we cannot confidently infer seasonality from this one-year study [62]. Future work with longer study periods is needed to further explore temporal trends of ARGs. Spearman’s correlation analysis and dbRDA were used to identify a relationship between PEFs and ARGs. dbRDA determined that PEFs explained ~ 40% of the variation in ARG composition (absolute and normalized concentrations) with flow rate having the most significant impact. Low to mild mixing velocities provide optimal conditions for HGT by increasing the chance of bacterial contact and subsequent conjugation [63]. Flow rate is the mixing velocity determinant in wastewater and has the potential to positively or negatively affect HGT [64]. This could be why flow rate explained a large portion of the variation in ARG profiles where all other PEFs investigated in this study had relatively little explanatory effects. Contrary to these findings, one study found that ammonium had the largest influence on ARG composition [34]. Other studies have drawn no conclusive correlation between wastewater parameters and ARGs or ARBs [26,50]. Yang and colleagues showcased that certain PEFs did have an effect on some ARG concentrations with relationships that changed based on season, but they did not identify any factors that dominated all genes [54]. Absolute concentration of ARGs had varying correlations to the nine PEFs analyzed in the present study and differing correlation strengths across WWTPs. The highest degree of similarity across WWTPs and targets was that bla KPC had strong positive correlations to wastewater temperature and strong negative correlations to pH at most WWTPs. Other targets showed no consistency with which PEFs they had strong positive or negative correlations. From dbRDA, PEFs explained less than half of the variation in ARG profiles, meaning there are a multitude of other unidentified factors that influence ARG composition and abundance. It has been theorized that the presence of antimicrobials, their residues, and heavy metals provide selective forces for AMR proliferation and spread in various environments [65,66]. A growing body of evidence has found that this is not always the case in wastewater and these selective pressures have little or negative correlation to ARGs and ARB [23–26]. These authors suggested that the ARGs and ARB in the wastewater might be present in the human gut prior to release into the sewershed and do not evolve from selective pressures in the wastewater, or that the process of AMR evolving from selective pressures is longer than the study period [23,25]. This study corroborates the inclination that antibiotic use has negligible effects on the ARGs present in the wastewater. Thunder Bay district health unit had a significantly lower antibiotic prescription rate than the state of Michigan and the Windsor-Essex County district health unit (where LPCC resides), yet NMDS determined LPCC to have gene profiles more similar to TB than to the Michigan sites [67,68]. Future studies could implement time-lagged cross correlation to see if changes in antimicrobial usage have a delayed effect on the presence of ARB and ARG in the sewershed. Others have found that socioeconomic factors, such as household deprivation, population age, and educational attainment, had significant but modest explanatory effects on ARG abundances [33,34]. The sites examined in this study had unique socioeconomic factors that did not have an apparent effect on the ARG profiles. For example, Leamington hosts the largest concentration of migrant agri-farm workers in Canada, most of which originate from the global south, and Thunder Bay has the largest urban Indigenous population in Canada, yet these two sites were often similar in analysis [69]. Comparing other socioeconomic factors of Leamington, Thunder Bay, and an average of the three Michigan counties in the WRRF sewershed (Macomb, Oakland, and Wayne) revealed that they had similar low-income status percentage and median age but had differing proportions of educational attainment [70–72]. A 2019 study that surveyed AMR in 60 different countries found that ARG profiles clustered based on geography rather than diet, World Bank income, or human development index of that country, suggesting socioeconomic factors may not play a large role in the geographical differences of ARGs [24]. However, the same study did see an increase in ARG abundances in regions with lower degrees of sanitation and general health practices, which often occur in socioeconomically depressed regions [24]. Many have also proposed that bacterial community composition may have substantial influence on ARG abundance. It has been shown that bacterial community composition in sewers changes with season, often displaying higher diversity of bacterial taxa in summer compared to winter [25,50,54]. LaMartina et al. found that bacterial community composition in wastewater enters two predictable steady states throughout the year with short transition periods in between [73]. It is possible that the explanatory effects of PEFs on ARGs could be occurring indirectly via influences on bacterial communities and their HGT capabilities. Many PEFs have demonstrated the ability to influence the diversity and complexity of bacterial communities with temperature being a major driving force [54,73]. This in turn would affect ARG abundance as each ARG is harboured in a select variety of bacterial species at different proportions. For example, a study of multi-drug-resistant gram-negative bacteria showed a large proportion of bla KPC is found in Klebsiella pneumonia whereas other ARGs, such as bla NDM , were found in lower proportions in a wider variety of species [13]. This could have led to the larger fluxes seen in bla KPC abundances across seasons, whereas bla NDM stayed more stable. Additionally, bla KPC and bla OXA−48 were both primarily found in K. pneumonia and Acinetobacter baumannii which could explain why their behaviour was more similar to each other than other ARGs studied here [13]. Temperature is also an integral component for HGT kinetics of ARGs. Plasmids containing bla KPC and bla NDM reached optimal conjugation stability and efficiency at 25 ºC and 30 ºC, respectively [74]. Conjugation of bla KPC encoding plasmids also relies on donor/recipient species and substrate, with some plasmids lacking the ability to transfer in broth [75]. This suggests that higher temperatures would increase bacterial diversity, conjugation stability of bla KPC containing plasmids, and bla KPC plasmid conjugation recipients, leading to higher proliferation and dissemination of bla KPC in the wastewater environment. This is reflected here as bla KPC had high correlation to wastewater temperature and the highest concentration in summer at all WWTPs. bla NDM on the other hand, had low or negative correlation to temperature indicating there is a complex matrix of influencing factors at play. More research into the role of PEFs in wastewater HGT of crabapenemase genes is needed to help predict the dissemination of these ARGs in sewer systems. It is likely that PEFs and general healthcare practices influence the sewer microbial communities and the efficiency of their HGT which leads to the changes observed in ARG concentrations. Given that sewer environments and their human inputs are spatiotemporally unique, ARG profiles would follow accordingly [73,76]. The biggest limitation of this study was the limited number of ARGs studied, especially since they were all from one AMR class. Each carbapenemase gene exhibited different behaviour, but other ARG classes may display more uniform behaviour over longitudinal studies. This study was also limited to one temporal cycle when two cycles (two years) are needed to accurately model seasonality [62]. While the current study included longitudinal sampling across two countries, more conclusive results would have been gathered had the study recruited cross-border communities of comparable populations to differentiate between geographical and population effects. Finally, it was demonstrated that ARG concentrations undergo large day-to-day fluxes. A majority of the WWTPs were sampled weekly for this study, which does not account for these day-to-day changes. In future studies, higher frequency sampling would provide a better estimate of true ARG concentrations. 5. Conclusion This study featured a longitudinal comparison of ARGs at multiple WWTPs differing in population served, spanning Köppen climate zones, and crossing international boundaries. The four carbapenemase genes studied here displayed geographical differences in absolute concentration and when normalized to the human fecal biomarker PMMoV. Additionally, these ARGs presented seasonal variation, but not at every WWTP, indicating site, more than season, influences the concentration of ARGs. Carbapenemase gene concentration underwent large day-to-day and week-to-week fluxes. It is suggested that increasing sampling frequency would improve assessment of ARG presence during longitudinal surveillance campaigns. Normalizing ARGs to PMMoV did not reveal any underlying spatiotemporal trends that were not apparent with absolute ARG concentration. PEFs had a significant explanatory effect on ARG profiles but accounted for less than half of the variation observed. All ARGs undergo complex interactions with their environment in unique ways, which could explain the lack of explanatory effects. Given that each city has differing environments within their sewers, WWTPs, and seasons, each ARG will behave in a different way in each of these environments and thus it is incredibly complicated to accurately predict total ARG presence based on surveillance of select ARGs. The knowledge generated here will add to a growing database that will help to unravel the complex relationships between ARGs in humans, animals, and the environment. Declarations Acknowledgements We extend appreciation to Operators, Laboratory Team, and Managers of the following wastewater treatment facilities: Wastewater Resource Recovery Facility, Great Lakes Water Authority; Pollution Control Centre, Municipality of Leamington; Water Pollution Control Plant, City of Thunder Bay. Corresponding Author R. Michael McKay - Great Lakes Institute for Environmental Research, University of Windsor, Windsor, ON N9B 3P4, Canada ; Department of Biological Sciences, Bowling Green State University, Bowling Green, Ohio 43403, United States ; https://orcid.org/0000-0003-2723-5371; Email: [email protected] Author Contributions EH: Conceptualization, Formal analysis, Investigation, Visualization, Writing–original draft, Writing–review and editing. QG: Investigation, Methodology, Validation, Writing–review and editing. RC-S: Investigation, Writing–review and editing. MB: Investigation, Writing–review and editing. OC-S: Investigation. JN: Resources. AB: Resources. SB: Resources. IM: Resources, Writing–review and editing. MA: Writing–review and editing. RM: Conceptualization, Funding acquisition, Project administration, Supervision, Writing–review and editing. Funding Funding in support of the Ontario Wastewater Surveillance Initiative was provided by the Ontario Ministry of Environment, Conservation, and Parks. We acknowledge additional support of the Government of Canada’s New Frontiers in Research Fund (NFRF; NFRFR-2022-00416), the Canada Biomedical Research Fund (CBRF; CBRF2-2023-00008) and from Ontario Genomics (COVID-19 Regional Genomics Initiative). Data Availability Contact the corresponding author for any queries regarding data generated in this study. Ethics Approval Not applicable. Consent to publish Not applicable. Competing Interests The authors declare no competing interests. Consent to Participate Not applicable. Clinical Trial Number Not applicable. 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1","display":"","copyAsset":false,"role":"figure","size":485675,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots of \u003cstrong\u003ea) \u003c/strong\u003eabsolute concentrations of ARGs (gene copies/L) and \u003cstrong\u003eb) \u003c/strong\u003ePMMoV normalized ARG concentrations\u003cstrong\u003e \u003c/strong\u003efor each of the five WWTPs across the sampling period plotted on a log scale. A value of 1 and 1E-9 was added to absolute concentrations and normalized concentrations, respectively, so values of 0 would appear on the log scale. Significance between sites was determined by a Kruskal-Wallis test followed by Dunn’s test with Holm-Bonferroni adjusted p-values. * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001, **** p \u0026lt; 0.0001\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8206721/v1/d22286d3699cb55bfe3a71f0.jpeg"},{"id":100253466,"identity":"b9ddb4ea-6fc2-4458-b711-0c0d98d23839","added_by":"auto","created_at":"2026-01-14 15:36:19","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":600699,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of absolute ARG concentrations (gene copies/L) between the five WWTPs. Concentrations were averaged by week, then correlation was determined using Spearman’s ρ. No wastewater sample was collected from NIEA, OAK, or JEFF between May 19, 2024 – May 25, 2024, so this week was removed from correlation analyses involving these sites. Bolding represents correlations that were significant (p \u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8206721/v1/ec7a5cfa9ed387a9ed2907f6.jpeg"},{"id":100371724,"identity":"a1c1d249-dae2-4574-bbe3-5dacad2ef86e","added_by":"auto","created_at":"2026-01-16 08:10:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33387,"visible":true,"origin":"","legend":"\u003cp\u003eNMDS ordination plot of Bray-Curtis dissimilarity matrix from Hellinger transformed ARG concentrations (gene copies/L) showing spatiotemporal differences in the resistome\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8206721/v1/c6137627fd99382fa59c69e5.png"},{"id":100371621,"identity":"aa320367-c410-4604-b9ab-c1d3da81e531","added_by":"auto","created_at":"2026-01-16 08:10:37","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":498004,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots of absolute ARG concentrations (gene copies/L) separated by season and plotted on a log scale. Significant differences between seasons were determined using a Kruskal-Wallis test followed by a Dunn’s test with Holm-Bonferroni adjusted p-values. Seasons that do not share at least one letter were significantly different from each other (p \u0026lt; 0.05). Letters are relative to the four seasons for each ARG-WWTP combination\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8206721/v1/5a8d8296a421af3d8be33f72.jpeg"},{"id":100253476,"identity":"80407dbd-e9ae-4a5f-9860-7806def78d4c","added_by":"auto","created_at":"2026-01-14 15:36:20","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":559077,"visible":true,"origin":"","legend":"\u003cp\u003eMann-Kendall test for monotonic trends in\u003cstrong\u003e \u003c/strong\u003eabsolute\u003cstrong\u003e \u003c/strong\u003eARG concentrations (gene copies/L) across the sampling period and each season. Bolding represents Kendall’s τ where the degree of the trend was significant (p \u0026lt; 0.05). Determination of Kendall’s τ for \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e in some seasons at TB and LPCC was not possible due to the lack of samples with a detectable signal\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8206721/v1/d147070f55e4ad3fc0541c8a.jpeg"},{"id":100372511,"identity":"7e181c91-4d05-490e-b01b-02d53da977b5","added_by":"auto","created_at":"2026-01-16 08:12:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":119086,"visible":true,"origin":"","legend":"\u003cp\u003edbRDA showing the explanatory effects of physicochemical and environmental factors on\u003cstrong\u003e \u003c/strong\u003ethe carbapenemase gene profile across WWTPs. No ammonia, CBOD, or phosphorous data were available at LPCC from June 26, 2023 – July 2, 2023, September 25, 2023 – October 1, 2023, or December 18, 2023 - December 24, 2023, respectively. No wastewater temperature data were available at TB from January 10, 2024 – January 16, 2024. No wastewater sample was collected from NIEA, OAK, or JEFF between May 19, 2024 – May 25, 2024. For these sites, these weeks were removed from the dataset prior to dbRDA analysis\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8206721/v1/2d79bda0f8ac56c10d9099ae.png"},{"id":100371914,"identity":"f88e2737-f6ad-4e61-a14c-14e76d8c3e71","added_by":"auto","created_at":"2026-01-16 08:11:12","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1090020,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix of absolute ARG concentration (gene copies/L) and physicochemical and environmental factors at each WWTP. Spearman’s ρ was determined using weekly averages of variables. Bolding represents correlations that were significant (p \u0026lt; 0.05). Weeks with unavailable data (described previously) were removed from correlation analysis involving those factors or sites\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8206721/v1/c35051157daf611771d2fe68.jpeg"},{"id":104421841,"identity":"5f64f2c6-ca87-46e5-814f-8341ce116d28","added_by":"auto","created_at":"2026-03-11 13:59:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4425720,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8206721/v1/b634112d-e2dc-4059-933d-c898a9c6ec3c.pdf"},{"id":100253471,"identity":"1fef6a22-b374-4a5e-bff4-fcbfa0a70bc4","added_by":"auto","created_at":"2026-01-14 15:36:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1386093,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationSpatiotemporalVariabilityinWastewaterDerivedCarbapenemResistanceGenesfromDiverseMunicipalSourcesintheLaurentianGreatLakesCa.docx","url":"https://assets-eu.researchsquare.com/files/rs-8206721/v1/aac2c769c30b1e86df01e554.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatiotemporal Variability in Wastewater-Derived Carbapenem-Resistance Genes from Diverse Municipal Sources in the Laurentian Great Lakes Catchment","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWidespread overuse of antibiotics has resulted in the emergence, selection, and global dissemination of antimicrobial resistance (AMR) amongst bacterial pathogens. The rapid spread of AMR paired with limited advances in new antimicrobial development poses a major threat to the security of modern medicine [1]. In 2019, AMR was estimated to be directly attributable to 1.27\u0026nbsp;million deaths and associated with an additional 3.68\u0026nbsp;million deaths [2]. If left unchecked, AMR will be responsible for an estimated 10\u0026nbsp;million deaths annually by 2050 [3]. The threat posed by AMR prompted the World Health Organization (WHO) to develop a global action plan against AMR, leading many countries to do the same [4\u0026ndash;6]. One major objective of these action plans is to understand the prevalence of AMR through large scale surveillance. The first surveillance initiative was the Global Antimicrobial Resistance and Use Surveillance System (GLASS). Organized by the WHO, GLASS receives clinical isolates form 129 countries to monitor the global AMR landscape [7]. Since then, many countries have developed their own surveillance systems, including the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) and the National Antimicrobial Resistance Monitoring System (NARMS) in the USA [8,9]. However, many of these initiatives monitor only human or animal clinical isolates with environmentally focused studies just recently gaining attention [10]. This limited sampling strategy does not represent the true presence of AMR as it ignores both asymptomatic carriers and pathogens that are present in the environment. Thus, a One Health approach incorporating both community wide surveillance and environmental monitoring is necessary to more thoroughly understand the current breadth of AMR and asses the effectiveness of future AMR stewardship programs.\u003c/p\u003e \u003cp\u003eAntibiotic resistant bacteria (ARB) can be deposited into the environment by human or animal hosts, accelerating the proliferation and spread of AMR. Environmental reservoirs play a key role in the reinfection cycle as AMR can be passed from the environment back to humans and animals [5]. ARB have been found in the natural and the built environment including soil, surface water, ground water, atmospheric aerosols, hospitals, agricultural land, and sewers [11\u0026ndash;17]. Each unique environment fosters its own diverse community of ARB and the antimicrobial resistant genes (ARGs) they harbour [18]. Disparities between ARB and ARGs even exist in closely related environments, such as hospitals and their receiving sewersheds [19\u0026ndash;21]. This discrepancy means that clinical antibiotic prescription rates and clinically isolated ARB cannot directly translate to community or environmental ARB/ARG load. Selective pressures such as industry related heavy metal pollution and antimicrobials and their residues from clinical settings, agricultural runoff, and animal waste have previously been shown to escalate the development and spread of AMR through horizontal gene transfer (HGT) [22]. However, emerging research suggests these constituents have little influence over the presence or proliferation of ARB and ARGs within the wastewater [23\u0026ndash;26]. Instead, there is increasing evidence that climatic and physical factors, such as temperature, precipitation, climate zone, and climate change, optimize ARB growth and HGT in the wastewater [27,28]. As such, sewers and wastewater treatments plants (WWTPs) are important conduits between human and environmental antimicrobial resistomes where clinically developed AMR is subject to environmental spread.\u003c/p\u003e \u003cp\u003eWastewater surveillance (WS) monitors biomarkers of pathogens and chemical residues from a well-defined community by collection of their waste from a WWTP. WS was first used in 1939 for monitoring of poliovirus but gained traction on a global scale during the COVID-19 pandemic [29]. WS of SARS-CoV-2 bypassed the myriad challenges associated with clinical data while having similar trends, providing actionable data to public health officials and healthcare providers [29,30]. Since then, WS has been used to monitor a multitude of other pathogens including influenza, respiratory syncytial virus, and ARB/ARGs [23,24,31\u0026ndash;34]. WS of ARB and ARGs has been employed worldwide using an array of molecular and cultivation approaches, making it easily accessible to many laboratories [35]. Using WS in tandem with clinical testing facilitates a deeper understanding of AMR abundance within the community and includes AMR harboured by asymptomatic carriers and wastewater bacterial communities.\u003c/p\u003e \u003cp\u003eIn 2018, the WHO released a priority list of ARB that are in urgent need of new antibiotic development [1]. Bacterial species harbouring ARGs protecting against carbapenems were identified as a critical concern [1]. Carbapenems are β-lactam antibiotics frequently used as a last resort to treat multidrug resistant bacteria. Carbapenemases are a group of antibiotic-resistant enzymes that facilitate hydrolysis of carbapenems and other β-lactam antibiotics, reducing their effectiveness [36]. Carbapenemases fall into three classes: class A serine B-lactamases, class B metallo B-lactamases, and class D oxacillin-hydrolyzing B-lactams, with five clinically prominent genes: \u003cem\u003eKlebsiella pneumonia\u003c/em\u003e carbapenemase (KPC; class A), New Delhi metallo-beta-lactamase (NDM; class B), Verona integron-encoded metallo-beta-lactamase (VIM; class B), imipenemase (IMP; class B), and oxacillinase-48 (OXA-48, class D) [36,37]. These genes were labelled critical concern due to their ubiquitous presence and effective antimicrobial resistant activity [38]. We hypothesized that carbapenemase gene profiles in wastewater were spatiotemporally unique and that wastewater carbapenemase resistomes were influenced by environmental factors. This was tested through the longitudinal monitoring of four critical concern carbapenemase genes, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e, and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;48\u003c/sub\u003e. Monitoring was carried out at five municipal wastewater treatment sites across two countries and two K\u0026ouml;ppen climate zones within the Laurentian Great Lakes catchment to develop an understanding of community level spatiotemporal trends [39]. Environmental conditions of monitored municipalities and physicochemical conditions of the wastewater were examined to identify potential factors that influence the abundance of carbapenemase genes at WWTPs.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample collection\u003c/h2\u003e \u003cp\u003eWastewater samples were collected from two WWTPs in Ontario, Canada, and one facility in Detroit, Michigan into which three interceptors drain raw influent from separate sewersheds (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A 1L sub-sample of a 24-hour composite screened-influent sample was collected once weekly from LPCC, NIEA, OAK, and JEFF and twice weekly from TB from June 2023 - June 2024. Following collection, all samples were transported to the lab on ice where they were immediately processed. No wastewater sample was collected from NIEA, OAK, or JEFF between May 19, 2024 \u0026ndash; May 25, 2024.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLocation and population served by WWTPs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFacility\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation Served\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLeamington Pollution Control Centre (LPCC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeamington, Ontario, Canada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eThunder Bay Water Pollution Control Plant (TB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThunder Bay, Ontario, Canada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98 000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDetroit Water Resource Recovery Facility (WRRF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorth Interceptor-East Arm (NIEA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDetroit, Michigan, USA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 482 000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOakwood-Northwest-Wayne County Interceptor (OAK)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e840 600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDetroit River Interceptor (JEFF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e492 000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample processing\u003c/h2\u003e \u003cp\u003eComposite wastewater samples were concentrated using 0.22\u0026micro;m Sterivex PES cartridge filters (MilliporeSigma, Burlington, MA, USA). Sterivex cartridges were attached to 60mL syringes and placed in a caulking gun to aid in filtration. Filtered volume ranged from 50mL \u0026minus;\u0026thinsp;180mL. After all volume had passed through, cartridges were sealed and flash frozen in liquid nitrogen before nucleic acid extraction. The Sterivex cartridges were thawed, and the filter membrane was cut away for subsequent nucleic acid extraction. Nucleic acids were extracted from filters using the AllPrep PowerViral DNA/RNA kit (Qiagen, Germantown, MD, USA) modified by the addition of 5% 2-mercaptoethanol (v/v) or the RNeasy PowerMicrobiome Kit (Qiagen) modified in the same way. DNase steps were excluded, and total nucleic acid was eluted in 50\u0026micro;L of nuclease-free water.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 qPCR\u003c/h2\u003e \u003cp\u003eTwo quantitative PCR assays were used to measure the concentration of four carbapenem-resistance genes in wastewater samples. A triplex assay targeted \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e, and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e, while a duplex assay targeted \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;48\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e. The \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e variant is markedly different from the other \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e variants [40]. For this reason, the two \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e assays were left separate during further analysis. Reaction wells contained 5\u0026micro;L of extracted DNA (diluted 1:10 with molecular grade water to relieve PCR inhibition) mixed with 10\u0026micro;L of Luna Universal Probe One-Step Reaction Mix (2x, New England Biolabs [NEB], Ipswich, MA, USA), and primers and probes for a final volume of 20\u0026micro;L. All primers and probes were obtained from Integrated DNA Technologies (IDT, Coralville, IA, USA) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll samples were run in technical triplicates on a MA600 thermocycler (Aumintec, Richmond Hill, ON, Canada). For the triplex assay, polymerase activation was performed at 95\u0026deg;C for 20 sec, followed by 45 cycles of denaturation and annealing/extension at 95\u0026deg;C for 5 sec, and 55\u0026deg;C for 30 sec, respectively. For the duplex assay, polymerase activation was performed at 95\u0026deg;C for 20 sec, followed by 45 cycles of denaturation and annealing/extension at 95\u0026deg;C for 5 sec, and 53\u0026deg;C for 30 sec, respectively. Custom gBlocks (IDT) were diluted in molecular grade water to create an 8-point standard curve to quantify each target (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). No template controls yielded no amplification. The limit of detection (LOD) and limit of quantification (LOQ) for each target was determined though examination of 20 standard curves (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The LOD was defined as the lowest concentration that can be consistently detected with 95% probability and the LOQ was defined as the lowest concentration that can be quantitatively determined with 35% CV.\u003c/p\u003e \u003cp\u003eFor all samples, an RT-qPCR assay was also performed for Pepper Mild Mottle Virus (PMMoV). PMMoV was used as a standard human fecal indicator to normalize signal related to physicochemical variability associated with the wastewater stream [41]. PMMoV reaction wells contained 2.5\u0026micro;L of extracted RNA (diluted 1:5 with molecular grade water to relieve PCR inhibition) mixed with 10\u0026micro;L of Luna Universal Probe One-Step Reaction Mix (2x), 1\u0026micro;L of Luna WarmStart RT Enzyme Mix (20x, NEB), and primers and probes for a final volume of 20\u0026micro;L.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer and probe sequences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssay\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimer/ Probe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFinal Concentration (nM)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAmplicon Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLOD (Copies/\u0026micro;L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLOQ (Copies/\u0026micro;L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eTriplex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKPC-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-GGC CGC CGT GCA ATA C-3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCDC[42]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKPC-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-GCC GCC CAA CTC CTT CA \u0026minus;\u0026thinsp;3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKPC-P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;FAM-TGA TAA CGC CGC CGC CAA TTT GT-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNDM-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-GCA AAT GGA AAC TGG CGA CC-3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLund et al.[40]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNDM-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-TAC CGC CCA TCT TGT CCT GA-3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNMD-P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;HEX-TCG CAC CGAATG TCT GGC AGC ACA-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIM-7-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-GAG TTG CTT CTT ATT GAT ACA G-3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLund et al.[40]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIM-7-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-AGG GTG AGG TGT ACG TTG C-3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIM-7-P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;Cy5-CCG ACT CGA TCG TCA TGG AAG TGC GT-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eDuplex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;48\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOXA-48-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-CTT AAA CGG GCG AAC CAA GC-3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLund et al.[40]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOXA-48-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-GTT CAT CCT TAA CCA CGC CC-3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOXA-48-P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;FAM TTC CCA ATA GCT TGA TCG CCC TCG ATT-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIM-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-TTG ATT GAT ACA GCT TGG GGT A-3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLund et al.[40]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIM-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-ACG GYG ATG CGT ACG TTG C-3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIM-P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;Cy5-GAC GCG GTC GTC ATG AAA GTG CGT-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePMMoV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePMMoV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePMMoV-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo; -GAG TGG TTT GAC CTT AAC GTT TGA-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHaramoto et al.[43]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePMMoV-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-TTG TCG GTT GCA ATG CAA GT-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePMMoV-P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;Cy5-CCT ACC GAA GCA AAT G-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Wastewater physicochemical parameters and environmental factors\u003c/h2\u003e \u003cp\u003eIn addition to sample collection, partners at the WWTPs provided physicochemical data of the wastewater including temperature (\u0026deg;C), flow rate (million L/day), pH, total phosphorous (mg/L), ammonia (mg/L), carbonaceous biochemical oxygen demand (CBOD, mg/L), and total suspended solids (TSS, mg/L). All wastewater physicochemical properties were averaged by week, ending on the day the composite sample started. Precipitation (mm) and surface temperature (\u0026deg;C) data were obtained from a publicly available weather database [44]. The date range was set to the week leading up to the sample, ending on the day the composite sample was started. Surface temperature measurements were an average of the weekly minimum and maximum temperatures. Precipitation measurements were the total weekly precipitation. The cities used to represent the different Great Lakes Water Authority WRRF interceptors from Michigan are as follows: NIEA \u0026ndash; Waterford, MI, USA, OAK \u0026ndash; Livonia, MI, USA, and JEFF \u0026ndash; Detroit, MI, USA. At LPCC, no ammonia data were available from June 26, 2023 - July 2, 2023, no CBOD data were available from September 25, 2023 \u0026ndash; October 1, 2023, and no phosphorous data were available from December 18, 2023 \u0026ndash; December 24, 2023. At TB, no influent wastewater temperature data were available from January 10, 2024 \u0026ndash; January 16, 2024, due to scheduled maintenance of the WWTP. Sampling points affected by missing physicochemical and environmental factors (PEFs) were omitted from distance-based redundancy analysis (dbRDA) analysis and any Spearman\u0026rsquo;s correlation analysis involving them.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data analysis and visualization\u003c/h2\u003e \u003cp\u003eAll data analysis was performed using R version 4.4.1 with assistance from the tidyverse package for data manipulation and organization [45]. All data visualizations were made using the ggplot2 package with additions from the RColourBrewer package. The rstatix package was used to perform Dunn\u0026rsquo;s test as a pairwise follow up to Kruskal-Wallis tests to determine statistical significance between groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The ggpubr package was used to add the results of Dunn\u0026rsquo;s test to boxplots. Spearman\u0026rsquo;s correlation coefficients and p-values between ARGs and PEFs were obtained using the base R functions \u0026lsquo;cor\u0026rsquo; and \u0026lsquo;cor.test\u0026rsquo;, respectively. The trend package was used to preform the Mann-Kendall test for monotonic trends. The vegan package was used to make a Hellinger transformed Bray-Curtiss dissimilarity matrix which was then used for non-metric multidimensional scaling (NMDS), the accompanying PERMANOVA, and dbRDA. Pairwise PERMANOVA tests for NMDS were run using the pairwiseAdonis package. Sampling weeks that were missing a PEF measurement were removed for those sites during dbRDA analysis and for Spearman\u0026rsquo;s correlations involving those parameters. Additionally, no wastewater sample was collected from NIEA, OAK, or JEFF between May 19, 2024 \u0026ndash; May 25, 2024, so this sampling week was removed from Spearman\u0026rsquo;s correlations involving these sites.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eBetween June 2023, and June 2024, a total of 302 wastewater samples were collected from three WWTP interceptors in Michigan and two WWTPs in Ontario. For the purpose of analysis, the year was divided into calendar seasons: summer (June 19, 2023 - September 22, 2023), fall (September 23, 2023 \u0026ndash; December 20, 2023), winter (December 21, 2023 - March 19, 2024), and spring (March 20, 2024 \u0026ndash; June 19, 2024). In addition to analyzing ARG concentrations, ARGs were normalized to PMMoV concentrations to account for variability in the wastewater environment, dynamic population changes, and sample processing [46]. PMMoV was chosen because it is a marker of human fecal contamination that has been widely used as a normalizing agent for WS and has relatively stable concentrations at the studied sites (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e) [31,32,47].\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Spatial variability of carbapenemase genes\u003c/h2\u003e \u003cp\u003e \u003cem\u003ebla\u003c/em\u003e \u003csub\u003eKPC\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;48\u003c/sub\u003e were detected in 100% (302/302) of the samples, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e was detected in 95% (286/302) of the samples, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e was detected in 54% (163/302) of the samples, and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e was detected in 64% (194/302) of the samples. A majority of the non-detects for \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e occurred at LPCC and TB. Generally, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e had the highest concentration at all WWTPs followed by \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;48\u003c/sub\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e (where it was detected), \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e, and finally \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e. Regardless, the average concentrations of ARGs were significantly different across a majority of WWTPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The yearly average concentrations for each ARG can be found in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Trends in ARG concentration did not change when normalized to PMMoV (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). However, normalization changed the degree of significance between some WWTPs, in some cases causing pairing to become significant or non-significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage concentrations and standard deviations for ARGs across the sampling campaign\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eAverage Gene Copies/L\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;48\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.35E\u0026thinsp;+\u0026thinsp;08\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84E\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02E\u0026thinsp;+\u0026thinsp;05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.51E\u0026thinsp;+\u0026thinsp;06\u0026thinsp;\u0026plusmn;\u0026thinsp;8.44E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.24E\u0026thinsp;+\u0026thinsp;02\u0026thinsp;\u0026plusmn;\u0026thinsp;6.78E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.32E\u0026thinsp;+\u0026thinsp;02\u0026thinsp;\u0026plusmn;\u0026thinsp;8.03E\u0026thinsp;+\u0026thinsp;02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27E\u0026thinsp;+\u0026thinsp;07\u0026thinsp;\u0026plusmn;\u0026thinsp;6.50E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30E\u0026thinsp;+\u0026thinsp;05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.14E\u0026thinsp;+\u0026thinsp;06\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.82E\u0026thinsp;+\u0026thinsp;02\u0026thinsp;\u0026plusmn;\u0026thinsp;4.63E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.27E\u0026thinsp;+\u0026thinsp;02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.45E\u0026thinsp;+\u0026thinsp;08\u0026thinsp;\u0026plusmn;\u0026thinsp;9.57E\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48E\u0026thinsp;+\u0026thinsp;06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14E\u0026thinsp;+\u0026thinsp;07\u0026thinsp;\u0026plusmn;\u0026thinsp;8.64E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.21E\u0026thinsp;+\u0026thinsp;06\u0026thinsp;\u0026plusmn;\u0026thinsp;5.54E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.65E\u0026thinsp;+\u0026thinsp;04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.74E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOAK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.63E\u0026thinsp;+\u0026thinsp;08\u0026thinsp;\u0026plusmn;\u0026thinsp;9.72E\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.83E\u0026thinsp;+\u0026thinsp;05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.78E\u0026thinsp;+\u0026thinsp;06\u0026thinsp;\u0026plusmn;\u0026thinsp;5.59E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.71E\u0026thinsp;+\u0026thinsp;06\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.10E\u0026thinsp;+\u0026thinsp;04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJEFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.67E\u0026thinsp;+\u0026thinsp;08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74E\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.56E\u0026thinsp;+\u0026thinsp;05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.64E\u0026thinsp;+\u0026thinsp;07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58E\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.19E\u0026thinsp;+\u0026thinsp;05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.38E\u0026thinsp;+\u0026thinsp;04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpearman\u0026rsquo;s Rho (ρ) was used to determined correlations of individual ARGs between WWTPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eTable S2\u003c/b\u003e). The strongest positive correlation was observed between \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;48\u003c/sub\u003e at NIEA and OAK (Spearman\u0026rsquo;s ρ\u0026thinsp;=\u0026thinsp;0.797, p\u0026thinsp;=\u0026thinsp;1.57e-12), whereas the most negative correlation was between \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e at TB and OAK (Spearman\u0026rsquo;s ρ = -0.229, p\u0026thinsp;=\u0026thinsp;0.102). Correlations between \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e had the least variability with Spearman\u0026rsquo;s ρ ranging from 0.382 (NIEA and JEFF, p\u0026thinsp;=\u0026thinsp;0.005) to -0.088 (TB and LPCC, p\u0026thinsp;=\u0026thinsp;0.530). For all ARGs, correlations between NIEA, OAK, and JEFF were often stronger than those involving LPCC or TB. The strongest correlation for all ARGs except \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e occurred between NIEA and OAK. \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e, and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;48\u003c/sub\u003e at the Michigan sites had stronger correlation to TB than to LPCC despite TB belonging to a separate K\u0026ouml;ppen climate zone [39]. The same trend was likely not seen with \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e due to the lack of detections at TB and LPCC. When normalized, the strongest positive correlation was seen between \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e at NIEA and OAK (Spearman\u0026rsquo;s ρ\u0026thinsp;=\u0026thinsp;0.758, p\u0026thinsp;=\u0026thinsp;7.48E-11) and the most negative correlation was seen between \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e at OAK and TB (Spearman\u0026rsquo;s ρ = -0.209, p\u0026thinsp;=\u0026thinsp;0.136) (\u003cb\u003eFig. S2, Table S2\u003c/b\u003e). Normalization had varying effects on observed correlations. The largest difference recorded was between \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e at NIEA and JEFF where absolute concentrations resulted in a Spearman\u0026rsquo;s ρ of 0.497 (p\u0026thinsp;=\u0026thinsp;1.75E-04) and normalized concentration had a Spearman\u0026rsquo;s ρ of 0.139 (p\u0026thinsp;=\u0026thinsp;0.325). For all normalized ARGs, the strongest correlation was between NIEA and OAK.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNMDS was used to compare spatiotemporal differences in ARG composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This revealed visually distinct geographical clusters confirmed by PERMANOVA (p\u0026thinsp;=\u0026thinsp;0.187 for NIEA and OAK, p\u0026thinsp;=\u0026thinsp;0.003 for TB and LPCC, and p\u0026thinsp;=\u0026thinsp;0.001 for all other comparisons). The only interceptors that did not have significantly different ARG profiles were NIEA and OAK. R-value cutoffs described previously indicated that sites from different countries were more separated than sites from the same country (\u003cb\u003eTable S3\u003c/b\u003e) [48]. Normalized ARGs showed the same geographical clusters (p-values same as above) (\u003cb\u003eFig. S3\u003c/b\u003e). No significant seasonal clustering was observed for absolute or normalized ARG concentrations (p\u0026thinsp;=\u0026thinsp;0.146 and 0.163, respectively).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Seasonal variability of carbapenemase genes\u003c/h2\u003e \u003cp\u003eVisual inspection of longitudinal time series showed ARG concentrations were relatively stable over the study period (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). However, comparing seasonal concentrations of ARGs revealed significant differences across seasons at one or more WWTP \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e, and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e also showed no significant difference in seasonal concentrations at one or more WWTP. Seasons with significantly different ARG concentrations were not uniform across WWTPs. Seasons were then ranked from lowest to highest average concentration (1\u0026ndash;4) to compare seasonal trends (\u003cb\u003eFig. S4\u003c/b\u003e). When compared this way, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;48\u003c/sub\u003e had almost identical trends at each WWTP with the highest concentration in the summer, followed by fall and spring, and the lowest concentration in the winter. \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e, and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e diverged from this trend and had highest and lowest average concentrations in all four seasons across the WWTPs. However, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e had consistently lower concentrations in the fall. Normalizing ARGs changed which seasonal concentrations were significantly different but did not unify the temporal variability of ARGs across WWTPs (\u003cb\u003eFig. S5\u003c/b\u003e). Normalization had a larger impact on ranked seasonal averages (\u003cb\u003eFig. S4\u003c/b\u003e). For all ARGs, summer and fall rankings decreased whereas winter and spring rankings increased. Fall had the lowest sum of ranks for all ARGs except for \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e, where it was only slightly higher than the lowest ranked season, summer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition to seasonal averages, monotonic trends were assessed across the study period and within each season using the Mann-Kendall test (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cb\u003eTable S4\u003c/b\u003e). Results of \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e at TB and LPCC were removed from further analysis involving the Mann-Kendall test due to the lack of detections and low detection signal over the study period. Moderate monotonic trends were seen across the study period at some WWTPs with Kendall\u0026rsquo;s Tau (τ) ranging from \u0026minus;\u0026thinsp;0.391 (p\u0026thinsp;=\u0026thinsp;7.12e-8, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;48\u003c/sub\u003e at TB) to 0.291 (p\u0026thinsp;=\u0026thinsp;0.002, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e at OAK). Yearlong monotonic trends were consistent in direction across ARGs with a majority (76%, 16/21) showing decreasing trends (Kendall\u0026rsquo;s τ\u0026thinsp;\u0026lt;\u0026thinsp;0). This is not the case when looking at seasonal trends where a majority (68%, 57/84) showed increasing trends (Kendall\u0026rsquo;s τ\u0026thinsp;\u0026gt;\u0026thinsp;0), with Kendall\u0026rsquo;s τ ranging from \u0026minus;\u0026thinsp;0.429 (p\u0026thinsp;=\u0026thinsp;0.037, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e at NIEA in Summer and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e at JEFF in Summer) to 0.513 (p\u0026thinsp;=\u0026thinsp;0.017, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e at NIEA in Fall). Monotonic trends showed similarities in direction, but not magnitude, across WWTPs and ARGs. Normalizing ARGs switched the direction of yearlong trends with 86% (18/21) showing an increasing trend (\u003cb\u003eFig. S6, Table S4\u003c/b\u003e). Yearlong Kendall\u0026rsquo;s τ for normalized ARGs ranged from \u0026minus;\u0026thinsp;0.156 (p\u0026thinsp;=\u0026thinsp;0.032, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;48\u003c/sub\u003e at TB) to 0.504 (p\u0026thinsp;=\u0026thinsp;1.14E-7, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e at OAK). Normalized seasonal trends corroborated trends of absolute concentration with a majority (73%, 61/84) also showing increasing trends. Seasonal Kendall\u0026rsquo;s τ ranged from \u0026minus;\u0026thinsp;0.516 (p\u0026thinsp;=\u0026thinsp;0.012, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e at NIEA in Summer) to 0.692 (p\u0026thinsp;=\u0026thinsp;0.001, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e at OAK in Fall). As seen with absolute concentration, direction of trends was similar for each ARG across WWTPs, but not across all ARGs. For most seasons, the direction of the monotonic trend was the same for normalized and absolute concentrations of ARGs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Factors influencing changes in carbapenemase gene concentration\u003c/h2\u003e \u003cp\u003edbRDA showed that PEFs had a significant explanatory effect on ARG composition with an adjusted R\u003csup\u003e2\u003c/sup\u003e of 0.398 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Flow rate had the strongest influence on ARG composition (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.278, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas ammonia had the weakest influence (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.671). CBOD, surface temperature, and ammonia were the only PEFs that did not have a significant explanatory effect on ARG profiles (p\u0026thinsp;=\u0026thinsp;0.265, 0.324, and 0.671, respectively). However, all other PEFs had relatively little, albeit significant, explanatory effects on ARG composition (\u003cb\u003eTable S5\u003c/b\u003e). PEFs exerted a similar explanatory effect on normalized ARG composition (adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.395, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cb\u003eFig. S7\u003c/b\u003e). The individual effects of PEFs on normalized ARG composition were similar to the effects on absolute concentration (\u003cb\u003eTable S5\u003c/b\u003e). The only PEFs that did not have a significant explanatory effect on normalized ARG composition were CBOD, surface temperature, and ammonia (p\u0026thinsp;=\u0026thinsp;0.292, 0.311, and 0.681, respectively).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCorrelations between ARGs, PEFs, and other ARGs at each WWTP were calculated using Spearman\u0026rsquo;s ρ (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, \u003cb\u003eTable S6\u003c/b\u003e). Direction and magnitude of correlations showed little similarities within and between WWTPs. However, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e had the highest positive correlation to wastewater temperature rather than any other PEF at all WWTPs except TB, with Spearman\u0026rsquo;s ρ ranging from 0.600 (OAK, p\u0026thinsp;=\u0026thinsp;2.25E-6) to 0.298 (TB, p\u0026thinsp;=\u0026thinsp;0.032). \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;48\u003c/sub\u003e had similar direction and significance of correlations to PEFs with some exceptions occurring at OAK and JEFF. Most ARG to pH correlations were consistently low or negative with only \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e at OAK being significantly positive. \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e, and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e showed consistently low or negative correlation to all PEFs with 87% (117/135) having Spearman\u0026rsquo;s ρ\u0026thinsp;\u0026lt;\u0026thinsp;0.200. Often, these three ARGs had stronger correlations to each other than any PEFs. This was especially true for \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e whose correlation ranged from Spearman\u0026rsquo;s ρ\u0026thinsp;=\u0026thinsp;0.844 (JEFF, p\u0026thinsp;=\u0026thinsp;3.87E-15) to Spearman\u0026rsquo;s ρ\u0026thinsp;=\u0026thinsp;0.704 (NIEA, 5.79E-9) at sites where they were consistently detected. \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e had the highest correlation with \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e compared to other ARGs at four out of five WWTPs, although the strength of the correlation varied. \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;48\u003c/sub\u003e also had the strongest correlation with \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e compared to other ARGs at all WWTPs, again with varied strength. Normalizing ARGs to PMMoV had a pronounced effect on correlations to other ARGs and PEFs (\u003cb\u003eFig. S8, Table S6\u003c/b\u003e). Normalized ARGs had strong, positive correlations to each other at each WWTP, except for correlations to \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e at LPCC and TB, due to the lack of detections. Additionally, many normalized ARGs had low or negative correlation to all PEFs with 68% (154/225) of ARG to PEF correlations having Spearman\u0026rsquo;s ρ\u0026thinsp;\u0026lt;\u0026thinsp;0.100. The strongest correlation was seen between normalized \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e and pH at OAK, where Spearman\u0026rsquo;s ρ\u0026thinsp;=\u0026thinsp;0.713 (p\u0026thinsp;=\u0026thinsp;3.10E-9). In contrast to absolute values, most normalized ARG to pH correlations were significantly positive. The strongest negative correlation was seen between normalized \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e and wastewater temperature at OAK (Spearman\u0026rsquo;s ρ = -0.754, p\u0026thinsp;=\u0026thinsp;1.14E-10). Normalizing ARGs did not reveal any clear, concise correlation patterns across ARGs or WWTPs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study used WS to monitor spatial and temporal trends of four critical concern carbapenemase genes at five sites over a one-year period, adding to the ever-growing knowledge base that supports WS as an effective and accurate means to monitor community wide AMR [20,23,24,33,35]. Absolute concentration of carbapenemase genes were significantly different across WWTPs with distinct geographical, but not seasonal, clusters revealed by NMDS. However, ARGs displayed differences in seasonal concentrations and monotonic trends with varying magnitudes based on target and site. While some correlations between ARGs and PEF were strong, factors were not identified that explained variation in concentration across all monitored ARGs.\u003c/p\u003e \u003cp\u003eThis study corroborates previous studies that have identified spatial differences in ARGs [24,33,34,48]. A study by the Global Sewage Surveillance project consortium compared ARG composition from 60 different countries and found a higher degree of variability between countries than within them [24]. Other studies that have conducted similar comparisons across a country or large metropolitan area have also shown that sampling points with geographical proximity show less AMR variability [33,34]. The present study is unique in that four of the monitored sites are spatially close but are separated by an international boundary. Additionally, the Michigan sites and LPCC lie in K\u0026ouml;ppen climate zone Dfa whereas TB belongs to zone Dfb [39]. This allowed a comparison of ARG profiles across international and environmental boundaries. NMDS revealed clustering of WWTPs from the same countries despite further analysis determining most comparisons to be significantly different. Conversely, Michigan sites had stronger Spearman\u0026rsquo;s correlations with TB rather than LPCC even though they belong to separate climate zones. These findings suggest that country had a larger impact on ARG profiles than climate zone. However, it has been shown previously that population size could contribute to changes in ARG composition [23]. The WRRF services\u0026thinsp;~\u0026thinsp;3\u0026nbsp;million people whereas LPCC and TB only service 20 000 and 98 000 inhabitants, respectively, suggesting the clustering could be based on population differences as well. Normalizing ARGs to population (gene copies/day/person) before analysis had no effect on the resulting NMDS (data not shown). The geographic clusters may also be formed based on the characteristics of the municipalities. NIEA, OAK, and JEFF all contain urban centres in their sewersheds where the municipalities in the drainage area of LPCC and TB are rural settings. Future studies involving cross border communities would help to elucidate the influence of these factors on ARG profiles. Regardless, most sites were significantly different in the NMDS, indicating country, climate, and population could all contribute to spatial differences in ARG composition. The role of geography on ARG composition seems to decrease between sites within the same city. This was confirmed by studies in New York City, USA, and Barcelona, Spain suggesting this effect is not country specific [49,50]. While not the same city, this study compared closely connected communities in Michigan. Comparisons of individual ARGs as well as resistomes identified similarities between the three Michigan interceptors, but JEFF was more dissimilar than NIEA and OAK. This difference could also be due to municipality characteristics as JEFF drains the downtown core of Detroit where NIEA and OAK serve the greater metropolitan area. In contrast, no significant difference was seen between seven ARGs across all New York City boroughs [50]. However, the drainage area of New York City is three times less than that of the WRRF [51,52].\u003c/p\u003e \u003cp\u003eInvestigation into temporal variability of ARGs revealed that all ARGs experienced seasonal variations at one or more WWTP. However, not all ARGs displayed seasonal variation at every WWTP, meaning that seasonal fluctuations in ARGs are also spatially distinct. Other studies have also reported varying results for seasonal patterns of ARGs in wastewater. Continuous, long-term studies often found that ARG levels were stable across the year, except for \u003cem\u003eqnrS\u003c/em\u003e which had higher loads in summer compared to winter in Bath, UK [23,53]. Conversely, long-term studies with infrequent sampling found differences in ARG concentrations between seasons, though there was no consensus on seasonal patterns [50,54,55]. One reason for this discrepancy could be that ARGs exhibit large fluxes in concentration between days and weeks of sampling. Therefore, sampling one day intermittently over a study period (be it a composite or grab sample) may not be indicative of the true ARG presence and does not provide precise temporal comparisons. Additionally, looking at seasonal changes as an attribute of antimicrobial gene class rather than individual genes may generate misleading results. As observed in the present study, each of the carbapenemase genes behaved in a different way with only select attributes being shared amongst them. Wrapping all carbapenemase genes together could mask trends exhibited by individual genes or cancel out opposing trends observed between two genes. Even when targeting specific genes, seasonal trends will likely not be congruent across spatially separated sites.\u003c/p\u003e \u003cp\u003eRanking seasonal averages revealed that absolute concentrations peaked in summer for \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;48\u003c/sub\u003e, but had more stability throughout the year for \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e, and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eVIM\u0026minus;7\u003c/sub\u003e. Varying results for seasonal trends of ARGs have been reported previously based on location and ARGs studied [23,33,50,55]. In contrast, carbapenemase-producing Enterobacteriaceae (CPE) infections occured at higher frequencies in summer and fall months across multiple, geographically distinct locations [56\u0026ndash;59]. This implies that clinical cases of CPEs do not stimulate concentration fluxes in all carbapenemase genes in the wastewater. Clinical CPE infections may have some explanatory effect on the elevated \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e concentrations observed in summer and fall as a major portion of clinical CPEs and CPEs found in the wastewater harbour \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e genes [13,17]. However, presence of hospitals in a sewershed often has little influence on the resistome at WWTPs, with some ARGs being identified in the sewershed months before clinical detection [20,21,33,60,61]. Normalized ARGs peaked at different times of the year than the absolute concentration of ARGs with a notable increase in winter and spring averages. It is, however, difficult to determine if the seasonal variation observed in absolute or normalized ARGs is true seasonality or due to random flux. To accurately model seasonality, a time series must contain at least two cycles, or in this case two years, meaning we cannot confidently infer seasonality from this one-year study [62]. Future work with longer study periods is needed to further explore temporal trends of ARGs.\u003c/p\u003e \u003cp\u003eSpearman\u0026rsquo;s correlation analysis and dbRDA were used to identify a relationship between PEFs and ARGs. dbRDA determined that PEFs explained\u0026thinsp;~\u0026thinsp;40% of the variation in ARG composition (absolute and normalized concentrations) with flow rate having the most significant impact. Low to mild mixing velocities provide optimal conditions for HGT by increasing the chance of bacterial contact and subsequent conjugation [63]. Flow rate is the mixing velocity determinant in wastewater and has the potential to positively or negatively affect HGT [64]. This could be why flow rate explained a large portion of the variation in ARG profiles where all other PEFs investigated in this study had relatively little explanatory effects. Contrary to these findings, one study found that ammonium had the largest influence on ARG composition [34]. Other studies have drawn no conclusive correlation between wastewater parameters and ARGs or ARBs [26,50]. Yang and colleagues showcased that certain PEFs did have an effect on some ARG concentrations with relationships that changed based on season, but they did not identify any factors that dominated all genes [54]. Absolute concentration of ARGs had varying correlations to the nine PEFs analyzed in the present study and differing correlation strengths across WWTPs. The highest degree of similarity across WWTPs and targets was that \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e had strong positive correlations to wastewater temperature and strong negative correlations to pH at most WWTPs. Other targets showed no consistency with which PEFs they had strong positive or negative correlations.\u003c/p\u003e \u003cp\u003eFrom dbRDA, PEFs explained less than half of the variation in ARG profiles, meaning there are a multitude of other unidentified factors that influence ARG composition and abundance. It has been theorized that the presence of antimicrobials, their residues, and heavy metals provide selective forces for AMR proliferation and spread in various environments [65,66]. A growing body of evidence has found that this is not always the case in wastewater and these selective pressures have little or negative correlation to ARGs and ARB [23\u0026ndash;26]. These authors suggested that the ARGs and ARB in the wastewater might be present in the human gut prior to release into the sewershed and do not evolve from selective pressures in the wastewater, or that the process of AMR evolving from selective pressures is longer than the study period [23,25]. This study corroborates the inclination that antibiotic use has negligible effects on the ARGs present in the wastewater. Thunder Bay district health unit had a significantly lower antibiotic prescription rate than the state of Michigan and the Windsor-Essex County district health unit (where LPCC resides), yet NMDS determined LPCC to have gene profiles more similar to TB than to the Michigan sites [67,68]. Future studies could implement time-lagged cross correlation to see if changes in antimicrobial usage have a delayed effect on the presence of ARB and ARG in the sewershed.\u003c/p\u003e \u003cp\u003eOthers have found that socioeconomic factors, such as household deprivation, population age, and educational attainment, had significant but modest explanatory effects on ARG abundances [33,34]. The sites examined in this study had unique socioeconomic factors that did not have an apparent effect on the ARG profiles. For example, Leamington hosts the largest concentration of migrant agri-farm workers in Canada, most of which originate from the global south, and Thunder Bay has the largest urban Indigenous population in Canada, yet these two sites were often similar in analysis [69]. Comparing other socioeconomic factors of Leamington, Thunder Bay, and an average of the three Michigan counties in the WRRF sewershed (Macomb, Oakland, and Wayne) revealed that they had similar low-income status percentage and median age but had differing proportions of educational attainment [70\u0026ndash;72]. A 2019 study that surveyed AMR in 60 different countries found that ARG profiles clustered based on geography rather than diet, World Bank income, or human development index of that country, suggesting socioeconomic factors may not play a large role in the geographical differences of ARGs [24]. However, the same study did see an increase in ARG abundances in regions with lower degrees of sanitation and general health practices, which often occur in socioeconomically depressed regions [24].\u003c/p\u003e \u003cp\u003eMany have also proposed that bacterial community composition may have substantial influence on ARG abundance. It has been shown that bacterial community composition in sewers changes with season, often displaying higher diversity of bacterial taxa in summer compared to winter [25,50,54]. LaMartina et al. found that bacterial community composition in wastewater enters two predictable steady states throughout the year with short transition periods in between [73]. It is possible that the explanatory effects of PEFs on ARGs could be occurring indirectly via influences on bacterial communities and their HGT capabilities. Many PEFs have demonstrated the ability to influence the diversity and complexity of bacterial communities with temperature being a major driving force [54,73]. This in turn would affect ARG abundance as each ARG is harboured in a select variety of bacterial species at different proportions. For example, a study of multi-drug-resistant gram-negative bacteria showed a large proportion of \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e is found in \u003cem\u003eKlebsiella pneumonia\u003c/em\u003e whereas other ARGs, such as \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e, were found in lower proportions in a wider variety of species [13]. This could have led to the larger fluxes seen in \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e abundances across seasons, whereas \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e stayed more stable. Additionally, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eOXA\u0026minus;48\u003c/sub\u003e were both primarily found in \u003cem\u003eK. pneumonia\u003c/em\u003e and \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e which could explain why their behaviour was more similar to each other than other ARGs studied here [13]. Temperature is also an integral component for HGT kinetics of ARGs. Plasmids containing \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e reached optimal conjugation stability and efficiency at 25 \u0026ordm;C and 30 \u0026ordm;C, respectively [74]. Conjugation of \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e encoding plasmids also relies on donor/recipient species and substrate, with some plasmids lacking the ability to transfer in broth [75]. This suggests that higher temperatures would increase bacterial diversity, conjugation stability of \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e containing plasmids, and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e plasmid conjugation recipients, leading to higher proliferation and dissemination of \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e in the wastewater environment. This is reflected here as \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eKPC\u003c/sub\u003e had high correlation to wastewater temperature and the highest concentration in summer at all WWTPs. \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eNDM\u003c/sub\u003e on the other hand, had low or negative correlation to temperature indicating there is a complex matrix of influencing factors at play. More research into the role of PEFs in wastewater HGT of crabapenemase genes is needed to help predict the dissemination of these ARGs in sewer systems. It is likely that PEFs and general healthcare practices influence the sewer microbial communities and the efficiency of their HGT which leads to the changes observed in ARG concentrations. Given that sewer environments and their human inputs are spatiotemporally unique, ARG profiles would follow accordingly [73,76].\u003c/p\u003e \u003cp\u003eThe biggest limitation of this study was the limited number of ARGs studied, especially since they were all from one AMR class. Each carbapenemase gene exhibited different behaviour, but other ARG classes may display more uniform behaviour over longitudinal studies. This study was also limited to one temporal cycle when two cycles (two years) are needed to accurately model seasonality [62]. While the current study included longitudinal sampling across two countries, more conclusive results would have been gathered had the study recruited cross-border communities of comparable populations to differentiate between geographical and population effects. Finally, it was demonstrated that ARG concentrations undergo large day-to-day fluxes. A majority of the WWTPs were sampled weekly for this study, which does not account for these day-to-day changes. In future studies, higher frequency sampling would provide a better estimate of true ARG concentrations.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study featured a longitudinal comparison of ARGs at multiple WWTPs differing in population served, spanning K\u0026ouml;ppen climate zones, and crossing international boundaries. The four carbapenemase genes studied here displayed geographical differences in absolute concentration and when normalized to the human fecal biomarker PMMoV. Additionally, these ARGs presented seasonal variation, but not at every WWTP, indicating site, more than season, influences the concentration of ARGs. Carbapenemase gene concentration underwent large day-to-day and week-to-week fluxes. It is suggested that increasing sampling frequency would improve assessment of ARG presence during longitudinal surveillance campaigns. Normalizing ARGs to PMMoV did not reveal any underlying spatiotemporal trends that were not apparent with absolute ARG concentration. PEFs had a significant explanatory effect on ARG profiles but accounted for less than half of the variation observed. All ARGs undergo complex interactions with their environment in unique ways, which could explain the lack of explanatory effects. Given that each city has differing environments within their sewers, WWTPs, and seasons, each ARG will behave in a different way in each of these environments and thus it is incredibly complicated to accurately predict total ARG presence based on surveillance of select ARGs. The knowledge generated here will add to a growing database that will help to unravel the complex relationships between ARGs in humans, animals, and the environment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend appreciation to Operators, Laboratory Team, and Managers of the following wastewater treatment facilities: Wastewater Resource Recovery Facility, Great Lakes Water Authority; Pollution Control Centre, Municipality of Leamington; Water Pollution Control Plant, City of Thunder Bay.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eR. Michael McKay\u003c/strong\u003e - \u003cem\u003eGreat Lakes Institute for Environmental Research, University of Windsor, Windsor, ON N9B 3P4, Canada\u003c/em\u003e; \u0026nbsp;\u003cem\u003eDepartment of Biological Sciences, Bowling Green State University, Bowling Green, Ohio 43403, United States\u003c/em\u003e; \u0026nbsp;https://orcid.org/0000-0003-2723-5371; Email: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEH: Conceptualization, Formal analysis, Investigation, Visualization, Writing\u0026ndash;original draft, Writing\u0026ndash;review and editing. QG: Investigation, Methodology, Validation, Writing\u0026ndash;review and editing. RC-S: Investigation, Writing\u0026ndash;review and editing. MB: Investigation, Writing\u0026ndash;review and editing. OC-S: Investigation. JN: Resources. AB: Resources. SB: Resources. IM: Resources, Writing\u0026ndash;review and editing. MA: Writing\u0026ndash;review and editing. RM: Conceptualization, Funding acquisition, Project administration, Supervision, Writing\u0026ndash;review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding in support of the Ontario Wastewater Surveillance Initiative was provided by the Ontario Ministry of Environment, Conservation, and Parks. We acknowledge additional support of the Government of Canada\u0026rsquo;s New Frontiers in Research Fund (NFRF; NFRFR-2022-00416), the Canada Biomedical Research Fund (CBRF; CBRF2-2023-00008) and from Ontario Genomics (COVID-19 Regional Genomics Initiative).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContact the corresponding author for any queries regarding data generated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTacconelli E, Carrara E, Savoldi A, Harbarth S, Mendelson M, Monnet DL, Pulcini C, Kahlmeter G, Kluytmans J, Carmeli Y, Ouellette M, Outterson K, Patel J, Cavaleri M, Cox EM, Houchens CR, Grayson ML, Hansen P, Singh N, Theuretzbacher U, Magrini N, WHO Pathogens Priority List Working Group. 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Sci Rep. 2020 Feb 20;10:3033. https://doi.org/10.1038/s41598-020-59292-w.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8206721/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8206721/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAntimicrobial resistance is quickly becoming one of the largest threats facing global health. To combat this threat, surveillance is necessary to understand the presence of potential antimicrobial resistance beyond what is identified in clinical isolates. Using wastewater-based surveillance, we conducted a year long sampling campaign of four critical concern carbapenem-resistance genes at five sites to determine spatiotemporal patterns. Environmental factors were also examined to identify potential influencers of carbapenemase gene concentrations in the wastewater. Non-metric multidimensional scaling (NMDS) revealed that these antimicrobial resistance genes exhibited significant site-specific, but not seasonal, clustering. Further investigation into seasonal variation revealed that gene concentrations were significantly different between season and displayed monotonic changes. The four carbapenemase genes did not exhibit similar trends or concentrations across seasons or treatment plants, but all underwent large day-to-day fluxes. Using distance-based redundancy analysis (dbRDA), environmental factors were able to explain\u0026thinsp;~\u0026thinsp;40% of the variation in gene profiles. However, each gene had differing correlations to all of the environmental factors studied here. These results indicate that a complex matrix of factors influence each antimicrobial resistance gene in a unique way with no consistent spatiotemporal patterns across the carbapenemase gene class.\u003c/p\u003e","manuscriptTitle":"Spatiotemporal Variability in Wastewater-Derived Carbapenem-Resistance Genes from Diverse Municipal Sources in the Laurentian Great Lakes Catchment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-14 15:36:10","doi":"10.21203/rs.3.rs-8206721/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fb4c7e63-915a-4c04-9879-93beceb20b58","owner":[],"postedDate":"January 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T13:58:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-14 15:36:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8206721","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8206721","identity":"rs-8206721","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}