BACKGROUND:Hepatitis A virus (HAV) infection is infrequently diagnosed in Canada, and little data are available regarding its epidemiology. Transmitted fecal-orally, HAV is an ideal candidate for wastewater-based surveillance (WBS). We set out to characterize HAV using WBS in a low-prevalence setting. METHODS:This observational study was conducted in the province of Alberta, Canada. Weekly composite wastewater samples were collected from eight municipalities (populations 7909-1,306,784), and eight neighbourhoods within Calgary, the largest city. HAV genomic material was quantified using RT-qPCR targeted on VP1 and sequenced at the VP1/2A junction. HAV case data from Alberta's Public Health Laboratory and population demographics from census data were correlated with wastewater HAV burden. RESULTS:Between July and December 2023, low levels of HAV were detected in wastewater from 50% of the municipalities and 50% of urban neighbourhoods (with 15.4% and 8.3% of samples testing positive, respectively). Wastewater HAV levels correlated with clinical HAV prevalence in larger communities. For genotyped clinical cases, wastewater with a matching VP1/2A sequence within a two-week period was observed for 8/9 episodes (median 6.42 days). Wastewater-measured HAV associated with population size, population density, and immigration from HAV endemic areas. CONCLUSION:HAV was detectable in wastewater of both municipalities and neighbourhoods within a low prevalence setting. Wastewater HAV was associated with population metrics and socioeconomic factors, including immigration. WBS data correlated strongly with clinical disease revealing a minimal burden of undiagnosed infections. This demonstrates the feasibility of HAV WBS and its potential as a public health tool in non-endemic settings.

Understanding the adaptations of microorganisms to their environment is key to predicting the stability and dynamics of microbial communities. To uncover molecular mechanisms of environmental response, we extracted genomic features from 13,554 prokaryotic isolates, and trained machine learning models to identify which ones are most strongly associated with the microbial salinity, temperature, oxygen, and pH preferences. To extract these features in high throughput, including gene families, non-coding RNAs (ncRNAs), oligonucleotides, and amino acid usage, we built FxTractor, a scalable and adjustable pipeline available at: https://github.com/MGXlab/FxTractor. We validated the performance of our models with experimental data from a newly isolated deep-sea extremophile belonging to the genus Limnochorda that is not well-represented among the ML training sets, showing strong agreement between predictions and the conditions used to isolate this strain. Our analysis revealed specific gene and ncRNA families associated with each of the four environmental parameters, uncovering both established and potentially new molecular mechanisms. Examples include the bacterial large Signaling Recognition Particle in isolates that are able to grow at high temperatures (≥55°C), suggesting a role in translational pausing and structural stability under thermal stress. We also found the anti-hemB ncRNA to be associated with low-salinity (<0.7% NaCl), indicating a conserved antisense mechanism regulating the energetic costs of heme biosynthesis. Together, these findings provide new insights into microbe-environment interactions, and show how FxTractor enables high throughput discovery of genomic associations.

Background:Deep marine cold seeps occurring along the seabed of continental margins are identified by their oasis-like ecosystems, which are largely fueled by the chemical energy of the venting fluids. Seep site 2A-1, situated at ~2,500 m water depth on the Scotian Slope of the North Atlantic was discovered in 2021. The seep hosts a large mussel encrusted, carbonate mound with biogenic methane bubbling up from a single vent. The emitted biogenic methane is primarily sourced from ~1 km below the seafloor within the basin bedrock that resides directly above the crest of an underlying salt diapir. Methods:A 600-m long transect composed of six push cores was collected across the seep structure. Downcore porewater ions and lipidomic profiles of 24 predominantly archaeal in origin lipid classes were tentatively identified and quantified across the transect. Results:The resolved lipidomes comprised of intact polar lipids, core lipids, core lipid degradation products, and photosynthetic pigments. These data were compiled as two-dimensional heatmaps to spatially examine vertical and lateral changes in the subsurface geochemical and microbiological architecture of the seep. Microbially mediated metabolic zones of elevated heterotrophy, denitrification, microbial sulfate reduction, and anaerobic methane oxidation were then mapped across the seep structure based on an integrated analysis of porewater geochemistry, bulk organic matter and its carbon isotope compositions, lipidomic diversity and biomarker proxy patterns. Discussion:Increased lipidomic diversity is shown to exist within the seep particularly at boundaries of high lateral geochemical gradients. Biomarker lipid proxies and porewater gradient changes indicate a microbial community dominated by ANME-1 and -2/-3 archaea that is mixed with, but also surrounded by, an envelope of microbial sulfate reduction. Discussion:Spatial changes in the stratified system highlight the complex interplay of micro- and macro-seepage and provide insights into the seep's evolution and impact on microbial dynamics across the carbonate structure.

Viruses are common causes of acute gastroenteritis worldwide. They are detected in large quantities in raw sewage making them amenable to wastewater-based surveillance (WBS). To monitor the prevalence of gastroenteritis viruses in wastewater and assess their correlation with clinical cases, wastewater samples collected between July 2020 and June 2024 from 12 wastewater treatment plants across Alberta, Canada were analyzed for norovirus (NoV) GI & GII, rotavirus (RoV), adenovirus (AdV), sapovirus (SaV) and astrovirus (AsV). Among the 5726 wastewater samples tested, AdV (80.5%) had the highest detection rate followed by NoV GII (75.6%), SaV (63.2%), NoV GI (59.4%), RoV (42.2%) and AsV (20.2%). Winter and spring seasonality was found for NoV and RoV in both wastewater and clinical disease. Public health interventions especially in the 1st year of the COVID-19 pandemic had a significant impact on their burden with marked reduction in wastewater detected viruses and clinical cases. NoV showed a strong correlation between its level in wastewater and the number of clinical cases, while moderate correlation was observed for the other four viruses. Cross-correlation analysis showed that changes of viral RNA concentration in wastewater lagged behind reported gastroenteritis cases by approximately 6 days to 3 weeks. To our knowledge, this is the longest multi-region WBS study monitoring multiple gastroenteritis viruses spanning both COVID-19 pandemic and post-pandemic periods. The data obtained from this study supported WBS as a complementary tool to track population-based circulation of gastroenteritis viruses, providing actionable public health data.

Wastewater-based surveillance (WBS) is a complementary tool for infectious disease surveillance, providing independent data that is inclusive of entire populations. We evaluated the role of WBS in tracking seasonal respiratory viral infections caused by influenza A (IAV) and B (IBV), and respiratory syncytial virus (RSV) - pathogens responsible for significant morbidity and mortality and imposing substantial burdens on healthcare systems. Longitudinal surveillance was performed 1-3 times weekly from January 2022-June 2024, encompassing multiple respiratory virus seasons, across the three municipal wastewater treatment plants serving Calgary, Canada's fourth largest city. Flow-normalized viral loads (gene copies/day) of IAV, IBV and RSV in 24-h composite wastewater were analyzed and compared to confirmed cases leveraging metadata from the single provincial health authority - Alberta Health Services. IAV and RSV RNA in wastewater peaked in winter months (November to February), with maximum viral loads of 4.4 × 1013 and 7.9 × 1013 (gene copies/day), respectively. In contrast, IBV was generally observed in late winter/early spring with low and infrequent periods of activity. Wastewater signals for all three respiratory viruses strongly correlated with clinical metrics including laboratory-confirmed cases, test percent positivity, and respiratory virus-associated hospital admissions (Spearman r value range: 0.66-0.90). Notably, WBS provided a 1-week lead time relative to traditional clinical respiratory viral surveillance indicators. WBS is a versatile technology that provides objective information that can augment traditional case-based surveillance of respiratory viruses and be used to predict changing disease prevalence and health resource requirements across communities.

Background Wastewater-based surveillance can be an important part of pandemic management, especially when testing capacity of individuals is limited. Statistical modeling can be used to examine the relationship between wastewater pathogen levels and clinical cases. The objective of this study was to examine the utility of distributed lag nonlinear modeling to derive the relationship between wastewater SARS-CoV-2 RNA levels and COVID-19 clinical cases across communities in Alberta, Canada when clinical testing was comprehensive.Methods This retrospective cohort study used data from 24-hour composite wastewater collected and tested two to three times per week from 11 wastewater treatment plants (WWTPs) in Alberta, Canada during May 10, 2020, to March 15, 2022. The number of daily new cases of COVID-19 downloaded from Alberta Health's centralized dataset of clinical surveillance of COVID-19 were mapped to each sewershed. Distributed lag nonlinear models were fit to describe the exposure-response relationship between the 7-day rolling average of SARS-CoV-2 RNA and daily new cases for each WWTP separately.Results The 11 WWTPs served a population of 3,422,062 (77% of Alberta's population) and 386,528 cases were documented during the study period. From 2021 onward, peaks in both wastewater viral RNA levels and cases tracked reasonably well. For almost all WWTPs, the best fitting model was a Poisson additive model with a P-spline for time. Models for the larger communities had better fits than smaller communities as represented by adjusted pseudo-R2 ranging from 80.7% to 94.4%. Models followed the same general trends as the actual COVID-19 cases over time.Conclusions With relationships between wastewater viral RNA levels for SARS-CoV-2 and COVID-19 cases expected to vary over time and to be non-linear, distributed lag nonlinear models are promising. While the form of the models was similar across WWTPs, the resulting estimates were different among sites suggesting site-specific analyses are essential.

Abstract. Fjords provide an important ecological service by burying and storing organic carbon (OC) more rapidly than most other marine environments. Quantifying this OC sink regionally requires an understanding of both the burial and remineralization processes that determine net OC sequestered. Within two sites from two Nunatsiavut fjords (Nachvak Fjord and Saglek Fjord, Labrador, Canada), we measured geochemistry associated with carbon, oxygen, and nutrient cycling and used a 1-D reactive transport model with an inverse modelling approach to simulate the geochemistry in the sampled sediment. A lower OC burial rate in sediment from Nachvak Fjord likely reflected significantly lower OC content in surface sediments and bottom sediments. Nachvak Fjord sediment modelling yielded a higher nitrification rate and a higher nitrate-dependent iron oxidation rate despite low heterotrophic denitrification compared to Saglek Fjord model results. Findings from microbial 16S rRNA gene sequencing were consistent with modelled biogeochemistry results from both fjords considering relative contributions from nitrate and sulfate reduction. Significantly higher OC content in Saglek Fjord sediments aligned with ~2X OC burial annually compared to Nachvak Fjord sediments. Differences in OC content and biogeochemical cycling might result from relative location chosen for sampling within each fjord, noting that we sampled outer fjord sediments in Nachvak Fjord, and inner fjord sediments in Saglek Fjord. Nevertheless, despite cold temperatures and good ventilation, both fjord basins utilized denitrification pathways (heterotrophic or nitrate-dependent iron oxidation) as the dominant nitrogen sink. Using both OC burial rates as end members, we estimate that northern Nunatsiavut fjords collectively store 8,000–33,000 tonnes OC yr−1. Our findings underscore the importance of additional studies with process-based measurements paired with OC burial estimates to understand further how climate change may alter benthic cycling and OC storage.

OBJECTIVE:Vancomycin-resistant Enterococcus (VRE) is an important cause of healthcare-associated infections. We adapted wastewater-based surveillance as a tool to longitudinally monitor VRE in hospitals through the detection of vancomycin resistance genes vanA and vanB. METHODS:Wastewater from four tertiary-care hospitals (three adult and one pediatric, totaling >2300 inpatient beds) and all three municipal wastewater treatment plants (WWTP) in Calgary, Canada (∼1.8 million) was sampled weekly (March to September 2022) and every other week (September 2022 to March 2023). Wastewater pellets were collected, DNA extracted, and vanA and vanB quantified by qPCR. vanA and vanB gene copies were assessed as raw (copies/mL) and normalized with three different fecal biomarkers - total bacterial 16S-rRNA, Bacteroides HF183 16S-rRNA, and human 18S-rRNA. Raw and normalized vanA and vanB abundance from each site was compared with clinically identified infections, vancomycin prescribing and hemodialysis services. RESULTS:The abundance of vanA was up to 1085-fold higher (p < 0.0001, Mann-Whitney) and vanB up to 32-fold higher (p < 0.01, Mann-Whitney) in adult hospitals compared to an aggregate municipal signal and exhibited significantly greater variation. Strong correlations between each method of fecal normalization and raw-measured vanA and vanB were observed, and no normalization method proved superior (Spearman's r = 0.50-0.96, p < 0.0001). vanA abundance was strongly correlated with hemodialysis provision (Spearman's r = 0.8357, p < 0.0001) but not vancomycin prescribing. CONCLUSIONS:Wastewater-based surveillance is a comprehensive tool capable of longitudinal real-time hospital surveillance for VRE with the potential to transform the ability of infection control and antimicrobial stewardship programs to dynamically track, understand, and mitigate nosocomial antimicrobial-resistant pathogens.

ABSTRACT Hydraulically fractured shale reservoirs have facilitated studies of unexplored niches in the continental deep biosphere. In high-salinity North American shale systems, members of the genus Halanaerobium seem to be ubiquitous. Polymers like guar gum used as gelling agents in hydraulic fracturing fluids are known to be fermentable substrates, but metabolic pathways encoding these processes have not been characterized. To explore this, produced water samples from the Permian Basin were incubated both at 30°C to simulate above-ground storage conditions and at 60°C to simulate subsurface reservoir conditions. Guar metabolism coincided with Halanaerobium growing only at 30°C, revealing genes for polymer biodegradation through the mixed-acid fermentation pathway in different metagenome-assembled genomes (MAGs). Whereas thiosulfate reduction to sulfide is often invoked to explain the dominance of Halanaerobium in these settings, genes for thiosulfate metabolism were lacking in Halanaerobium genomes with high estimated completeness. Sulfide production was observed in 60°C incubations, with corresponding enrichment of Desulfohalobium and Desulfovibrionaceae that possess complete pathways for coupling mannose and acetate oxidation to sulfate reduction. These findings outline how production of fermentation intermediates (mannose and acetate) by Halanaerobium in topside settings can result in reservoir souring when these metabolites are introduced into the subsurface through produced water reuse. IMPORTANCE Hydraulically fractured shale oil reservoirs are ideal for studying extremophiles such as thermohalophiles. During hydraulic fracturing, reservoir production water is stored in surface ponds prior to reuse. Microorganisms in these systems therefore need to withstand various environmental changes such as the swing between warm downhole oil reservoir temperatures and cooler surface conditions. While most studies on hydraulically fractured oil reservoirs mimic the environmental conditions found in oil wells, this study follows this water cycle during fracking and the associated microbial metabolic potential during topside-produced water storage and subsurface oil reservoir conditions. Of particular interest are members of the genus Halanaerobium that have been reported to reduce thiosulfate contributing to souring of oil reservoirs. Here, we show that some Halanaerobium strains were unable to grow at hotter temperatures reflective of oil reservoir conditions and lack genes for thiosulfate reduction, despite the proposed importance of this metabolism in other studies. Rather, it is likely that these organisms metabolize complex organics in fracking fluids at lower temperatures, thereby generating substrates that support reservoir souring by thermophilic sulfate-reducing bacteria at higher temperatures. In this way, Halanaerobium promotes souring indirectly by feeding sulfate-reducing microorganisms fermentation products (e.g., acetate and hydrogen) rather than via direct sulfidogenesis via thiosulfate reduction. Therefore, the novelty of this research is not within the detection of known oil reservoir colonizing bacteria but rather in the relationship between bacteria and the indirect involvement of Halanaerobium , promoting souring throughout the produced water reuse cycle.

Understanding factors associated with antimicrobial resistance (AMR) distribution across populations is a necessary step in planning mitigation measures. While associations between AMR and socioeconomic-status (SES), including employment and education have been increasingly recognized in low- and middle-income settings, connections are less clear in high-income countries where SES remains an important influence on other health outcomes. We explored the relationship between SES and AMR in Calgary, Canada using spatially-resolved wastewater-based surveillance of resistomes detected by metagenomics across eight socio-economically diverse urban neighborhoods. Resistomes were established by shotgun-sequencing of wastewater pellets, and qPCR of targeted-AMR genes. SES status was established using 2021 Canadian census data. Conducting this comparison during the height of COVID-related international travel restrictions (Dec. 2020–Oct. 2021) allowed the hypotheses linking SES and AMR to be assessed with limited confounding. These were compared with sewage metagenomes from 244 cities around the world, linked with Human Development Index (HDI). Wastewater metagenomes from Calgary’s socioeconomically diverse neighborhoods exhibit highly similar resistomes, with no quantitative differences (p > 0.05), low Bray-Curtis dissimilarity, and no significant correlations with SES. By comparison, dissimilarity is observed between globally-sourced resistomes (p < 0.05), underscoring the homogeneity of resistomes in Calgary’s sub-populations. The analysis of globally-sourced resistomes alongside Calgary’s resistome further reveals lower AMR burden in Calgary relative to other cities around the world. This is particularly pronounced for the most clinically-relevant AMR genes (e.g., beta-lactamases, macrolide-lincosamide-streptogramin). This work showcases the effectiveness of inclusive and comprehensive wastewater-based surveillance for exploring the interplay between SES and AMR. Antimicrobial resistance (AMR) occurs when antimicrobial treatments fail to work and microbes continue to grow. This is a result of microbes acquiring AMR genes. Antimicrobial resistance (AMR) is an increasing public health threat. Some studies have suggested an association between AMR and socioeconomic factors. The amount of AMR can be monitored by investigating the presence of specific genes indicative of AMR in wastewater. To explore this within a high-income country with publicly funded health care, we collected wastewater from eight socioeconomically diverse neighborhoods across a large Canadian city. Conducted over eleven months during COVID-19-related travel restrictions, we did not observe an association between socioeconomic status of residents and the amount or types of AMR genes in wastewater. We also compared AMR genes from wastewater from cities across the globe, where we observed the presence of AMR genes significantly differed along established socio-economic parameters. Overall, our findings revealed the relationship between AMR genes and socioeconomic factors is dynamic, and context dependent. Lee et al. investigate whether the wastewater resistome of a large Canadian city associates with socioeconomic status (SES) of resident populations using a granular, neighborhood-based approach. No correlation is seen, in contrast with data from other cities around the world, where AMR genes disproportionately concentrate in cities with lower SES.

Abstract Background Hepatitis A virus (HAV) incident infection in Canada is rarely diagnosed (i.e. incidence of 3.6-10 cases/100,000 persons) (PMID: 18159360). Infections generally relate to imported contaminated food-products, or travelers returning from endemic countries. Due to its fecal-oral spread, possible underdiagnosis, and often-cryptic presentation, HAV is an ideal candidate for wastewater (WW)-based surveillance, a tool increasingly utilized to monitor infectious diseases globally.Figure 1.Longitudinal monitoring of HAV RNA wastewater abundance across eight Alberta municipalities over a 4-month period. Methods 24-hour composite WW was collected weekly from eight geographically disparate, and socioeconomically diverse municipal WW treatment plants in Alberta from August to December 2023. After short-term cold storage, WW was centrifuged, and RNA from the raw pellet was extracted using Qiagen’s RNeasy PowerFecal pro. HAV levels were quantified by RT-qPCR of the vp1 gene. 2021 Canadian census data was used to define population demographics for each participating site. Results HAV was detected in 18/117 (15.4%) WW samples and 5/8 (62.5%) municipalities over the 4-month period (Figure 1). RNA abundance in HAV positive WW samples was a median of 3.4 copies/mL (IQR 0.44 - 6.97). Larger population size (p=0.007), and greater density (p=0.001) were associated with increased likelihood of HAV WW detection, whereas social and economic demographics of populations within sewershed catchments did not associate with likelihood of HAV detection (Table 1). Conclusion HAV RNA is rarely detected in the wastewater of Alberta. Detection was more frequently observed in larger municipalities, which is consistent with non-endemic, imported disease. WW surveillance can potentially be adapted to monitor HAV in the context of outbreaks to reduce secondary transmission, and safeguard public health. Disclosures Mark Swain, MD MSc, Abbott: Advisor/Consultant|Advanz: Advisor/Consultant|Gilead, BMS, CymaBay, Intercept, Genfit, Pfizer, Novartis, Astra Zeneca, GSK, Celgene, Novo Nordisk, Axcella Health Inc., Merck, Galectin Therapeutics: Grant/Research Support|GSK: Advisor/Consultant|Ipsen: Advisor/Consultant|Novo Nordisk: Advisor/Consultant Carla Coffin, MD MSc, Altimmune: Grant/Research Support|Gilead: Grant/Research Support|GSK: Grant/Research Support|Janssen: Grant/Research Support Steven J. Drews, PhD FCCM D(ABMM), Abbott: Grant/Research Support|Danaher: Honoraria|Roche: Advisor/Consultant|Roche: Grant/Research Support

Wastewater-based surveillance (WBS) for SARS-CoV-2 was a key strategy for epidemiological modelling and informing COVID-19 health policy during the pandemic. We assessed the capacity and performance of SARS-CoV-2 WBS in public schools. Of seventeen schools screened for participation, only four had plumbing systems that were amenable to comprehensive monitoring. From December 2020 to March 2021 composite wastewater collected twice-weekly from these four schools was compared with three municipal wastewater treatment plants (WWTPs) for SARS-CoV-2 RNA by RTqPCR and fecal biomarkers. Schools had lower rates of successful sample collection relative to WWTPs (64/79 vs. 66/66, p < 0.001). In a time of low COVID-19 activity, 13/64 of school samples were positive for SARS-CoV-2, versus 66/66 for WWTP (p < 0.0001). SARS-CoV-2 RNA in school wastewater was associated with, and often preceded, clinically confirmed COVID-19 cases among students, but showed no correlation with overall rates of student absenteeism. Levels of both SARS-CoV-2 RNA and fecal biomarkers were markedly lower in school wastewater relative to WWTPs. This work demonstrated that WBS for SARS-CoV-2 in schools can be a leading indicator of clinical disease but is technically challenging. The lower fecal biomarker levels from schools suggests children may avoid defecation at school which may further adversely impact school-based WBS for fecal-shed targets.

Rigorous method development and validation to detect and quantify SARS-CoV-2 RNA in wastewater has led to important advances in community disease surveillance using quantitative molecular biology tools. Despite this progress, agreement on standardized workflows for this important public health objective has been elusive. Multiple studies have compared different protocols but have been limited by short periods of observation or low numbers of test sites. Here we compare results from two parallel workflows for wastewater processing and quantifying SARS-CoV-2 gene targets from five wastewater treatment plants in three large cities in Alberta, Canada for up to 29-months. In total 1,482 wastewater samples were processed using either affinity columns followed by RT-qPCR with DNA-based standards or using ultrafiltration followed by RT-qPCR with RNA-based standards. Results from either workflow correlated well with each other, and with 5-day rolling averages of clinically diagnosed COVID-19 cases (i.e., in the early part of the 29-month study period when clinical testing was performed routinely). This highlights that different workflows both effectively and reliably monitored SARS-CoV-2 trends in wastewater. Parallel quantification of pepper mild mottle virus genomes and normalization were inconsistent between the two workflows, suggesting that normalization strategies may require adjustment for different wastewater processing protocols. Freezing wastewater samples diminished measured SARS-CoV-2 RNA levels significantly, whereas short term sample storage at +4°C gave consistent results. Overall, this work demonstrates that different workflows can deliver similarly effective wastewater-based surveillance for community COVID-19 burden. As this emerging technology is used more routinely, investigators should prioritize consistent application of a given workflow to a high-quality standard over time, whereas focusing on all testing programs adopting identical workflows and methods may be unnecessary.

Abstract Background Shigatoxin-producing E. coli (STEC) are responsible for significant human morbidity, with the potential to cause severe food-borne illness and outbreaks. STEC incidence varies between communities and peaks in summer months (PMID 31652648). Leveraging a SARS-CoV-2 WBS program, we sought to explore genomic targets for STEC WBS.Figure 1.Prevalence of STEC differs across Alberta’s municipal sewer sheds Methods Composite-24h wastewater (WW) was collected from geographically disparate, and socioeconomically diverse Alberta communities (n=5) at the level of municipal WW treatment plants. From 04/2022-03/2024, monthly WW underwent pelleting and DNA extraction by Qiagen DNeasy PowerSoil Pro kit. WW extracts were assessed for four potential genomic STEC targets: Shiga Toxin 1 (stx1), Shiga Toxin 2 (stx2), Intimin (eae) and LPS O antigen gene specific for O157 (rfbEO157), by multiplex PCR. Each target was normalized by 16S rRNA-for total bacterial burden. WW STEC targets were assessed for correlation using Spearman’s and compared between communities and seasonality (July-Sept vs Jan-Mar) by Mann-Whitney U-test.Figure 2.STEC WBS demonstrates seasonal prevalence trends Results Of 111 WW samples assessed, 108 (97%) were positive for all targets, and individual targets were identified in 110 (99%) stx1; 110 (99%;) stx2; 111 (100%) eae, and 110 (99%) rfbEO157. Gene abundance for each STEC target exhibited strong correlations across sites (stx1 vs stx2, r=0.802, p< 0.0001; stx2 vs rfbEO157, r=0.634, p< 0.0001; eae vs rfbEO157, r =0.551, p< 0.0001; stx2 vs. eae r=0.542, p< 0.0001). WW measured STEC gene targets exhibited significant differences between municipalities (Figure 1) with strong seasonal trends (Figure 2). Conclusion WBS for STEC yielded patterns consistent with established patterns of disease (PMID 31652648). All four STEC genomic targets demonstrated significant correlation across sewersheds. STEC WBS may represent a novel tool to understand and monitor population-level activity and prevent disease. Disclosures All Authors: No reported disclosures

Antimicrobial resistance is an accelerating threat to global health. Wastewater-based surveillance (WBS) enables objective, inclusive, and comprehensive assessments of population-level antimicrobial resistance; however, it is limited in its ability to detect rare antibiotic resistance genes (ARG). We compared traditional high-depth metagenomic sequencing of raw wastewater with lower-depth sequencing following semi-selective culture enrichment for gram negatives for rare ARGs in wastewater from two tertiary-care hospitals and two nearby urban neighborhoods. In total, 26 antibiotic resistance gene types (1,225 subtypes) were identified, with beta-lactamase genes being the most prevalent. Resistomes differed between raw and culture-enriched wastewater metagenomes and clustered based on sample type (hospitals versus neighborhoods). Hospital wastewater had higher diversity and a greater abundance of ARGs relative to both raw and culture-enriched neighborhood wastewater metagenomes. Lower coverage sequencing following culture enrichment proved superior to deeper sequencing for identifying rare, clinically relevant targets, including carbapenemase genes. In particular, enrichment with meropenem proved the most sensitive to identifying clinically relevant genes and enabled significant cost savings. ARG WBS has enormous potential for augmenting hospital-based infection prevention and control and antimicrobial stewardship programs.IMPORTANCEAntimicrobial resistance (AMR) poses a considerable burden to healthcare systems and contributes to increased morbidity and mortality. This is expected to further increase with time. AMR surveillance programs are key to understanding and controlling this progressive threat. Wastewater-based surveillance (WBS) is an emerging tool that can be adapted to this end. This study explores the role of metagenomic analysis of WBS with/and without culture enrichment to detect rare antibiotic resistance genes (ARG) of clinically important pathogens across a range of scales. We were able to demonstrate that the resistome of hospitals significantly differs from communities having a greater abundance, and more heterogeneous ARGs. Culture enrichment, particularly with meropenem, improved the detection of clinically relevant ARGs even at lower sequencing depths. WBS is an important tool with the capacity to augment hospital-based infection control and antimicrobial stewardship programs, providing real-time, cost-effective information on the population within.

Hydrocarbon seepage in marine sediments exerts selective pressure on benthic microbiomes. Accordingly, microbial community composition in these sediments can reflect the presence of hydrocarbons, with specific groups being more prolific in association with seepage. Here, we tested machine learning models with large 16S rRNA gene amplicon data sets derived from marine sediments in deep-sea hydrocarbon prospective areas of the Eastern Gulf of Mexico and NW Atlantic Scotian Slope. Utilizing H2O's AutoML machine learning platform, it was determined that Gradient Boosting Machines performed best for creating 16S rRNA-based models that successfully predict the presence of hydrocarbons. Feature importance scores from the models revealed that in Gulf of Mexico samples, members of the Aminicenantia class (within the Acidobacteriota phylum) and the Sulfurovum genus (within the Campylobacterota phylum) were most diagnostic for the presence of low molecular weight hydrocarbon gases. The Campylobacterota lineage was also important in Scotian Slope sediments, along with sequences affiliated with the class-level JS1 group (within the Caldatribacteriota phylum) for determining hydrocarbon-positive sites. Testing these models in geographically distant seafloor basins showed that the microbial communities between basins varied sufficiently to prevent consistently accurate reciprocal predictions. However, models trained on a combined data set and filtered for important features performed substantially better, supporting the feasibility of generalized models under stringent feature selection. These results highlight the potential of seabed microbial taxonomy-based hydrocarbon seep site prediction when paired with refined sampling and consistent geochemical characterization. IMPORTANCE:Our study showcases an important use of bioinformatics in an interdisciplinary context, by combining hydrocarbon geochemistry and microbial biodiversity DNA sequencing profiles. We trained and compared different machine learning models on 16S rRNA-based bacterial taxonomy data using 377 DNA sequencing libraries from marine surface sediments in two different hydrocarbon prospective marine basins from different parts of the global ocean to predict the hydrocarbon status of sediment samples. Of all algorithms tested, Gradient Boosting Machines worked best for this objective. Feature importance scores from the models highlighted that in Gulf of Mexico samples, members of the Aminicenantales order and Campylobacterota lineages were most diagnostic for the presence of low molecular weight hydrocarbon gases. The Campylobacterota lineage was also important in NW Atlantic Scotian Slope sediments, along with sequences affiliated with the class-level group JS1 (within the Caldatribacteriota phylum) for determining hydrocarbon-positive sites, though several features appeared to be basin-specific. Importantly, models had a high prediction accuracy when predicting samples from the same basin but were less effective in predicting the hydrocarbon status in reciprocal basin testing, pointing to the ecological differences in hydrocarbon-driven environmental selection in different parts of the ocean. However, combined models using a refined set of predictive features improved cross-basin performance, highlighting the feasibility of a broader application. Results highlight the exciting potential of microbial taxonomy-based machine learning models in predicting broader ecological, oceanographic, and geological phenomena at the biosphere-geosphere interface.

Nitrate addition for mitigating sulfide production in oil field systems has been studied in laboratory settings and in some subsurface oil reservoirs. To promote water recycling and reuse associated with oil reservoirs produced by hydraulic fracturing, high-salinity produced waters are temporarily stored in surface ponds prior to subsequent reinjection into the subsurface. In this study, nitrate was added directly to a storage pond to prevent sulfide accumulation. DNA sequencing of pond water over a 4-week period revealed a decrease in the proportion of sulfate-reducing microorganisms following nitrate application. Sulfate levels remained stable during this period, whereas nitrate and nitrite fluctuated in the days following the nitrate addition. Metagenome-assembled genomes (MAGs) reconstructed from the pond water microbiome highlighted different organisms with genes for organoheterotrophic and lithoheterotrophic nitrate reduction, whereas genes associated with sulfide production via sulfate or thiosulfate reduction were barely detected. Within those MAGs, genes for acetate metabolism were observed, consistent with acetate decreasing substantially in the pond water in the presence of nitrate. After nitrate was consumed an increase in relative abundance of putative autotrophic microorganisms was observed (e.g. Arhodomonas, Guyparkeria, and Psychroflexus), corresponding to a drop in total inorganic carbon measurements in the storage pond. This trial offers an overview on microbial processes taking place in storage pond environments in response to nitrate addition.

Abstract Background Hospitals represent ideal locations for developing wastewater (WW) surveillance for antibiotics (Abx), owing to the high frequency of Abx use and robust record-keeping. To investigate this technology as a potentially useful stewardship tool, we compared the concentration of several Abx in WW from tertiary care hospitals to their corresponding levels in the surrounding municipality. Concentration of 4 common antimicrobials found in hospital wastewater effluent over a 4-month period Concentrations of 4 antimicrobials measured in WW from 3 tertiary care hospitals and the corresponding wastewater treatment plant (WWTP) in the same city. Displayed statistics represent the results of a Wilcoxon test, antimicrobials labelled “*” are commonly administered intravenously. Methods WW was collected bi-weekly from three tertiary care hospitals (two adult and one pediatric, with 600, 650 and 135 inpatient beds, respectively) and the associated municipal WW treatment plant (serving a population of ∼1,000,000) between February and May of 2024. Aliquots of WW were filtered and run directly on a liquid-chromatography paired triple quadrupole mass spectrometer (LC-QQQ) to quantify specific Abx (azithromycin, doxycycline, ciprofloxacin, levofloxacin, metronidazole, cefazolin, ceftriaxone, piperacillin, tazobactam, meropenem, vancomycin, and sulfamethoxazole). Spiked and replicate samples were randomly included to validate analyte recovery and reproducibility. Agilent MassHunter software (Version 10.1, 2019) was utilized to process and export raw data to R. Box plots and Wilcoxon tests were utilized to compare the concentration of each Abx. Results Validation experiments confirmed that filtering WW samples and directly running them on LC-QQQ yields reproducible and reliable results. Serial monitoring revealed that WW from hospitals generally exhibited a broader range of Abx concentrations than was observed city-wide (Figure 1). This variability was particularly evident among the most used Abx, consistent with the changing treatment needs of highly dynamic hospital populations. In addition to this, ceftriaxone, an IV-administered antimicrobial was found at significantly higher concentrations in all hospital sites when compared to the municipal WW treatment plant. Conclusion Validating Abx monitoring in WW from a range of scales will enable this approach to be applied across diverse environments as a tool to mitigate Abx resistance. This approach will be strengthened as it is integrated with clinical metadata and metagenomic assessment of antimicrobial resistance genes from the same samples. Disclosures All Authors: No reported disclosures

ObjectiveAlberta's largest research universities collaborated to expand COVID-19 wastewater monitoring throughout the province to regularly provide evidence of SARS-CoV-2 burden in municipalities representing 3.2 million people. ApproachSampling was conducted at 26 wastewater treatment plants and facilities across the province. The project quantified SARS-CoV-2 genomic material in wastewater to reveal population-level trends of COVID-19 cases. This inclusive and comprehensive strategy captures everyone who contributes to wastewater, including those not clinically diagnosed. Researchers collected wastewater samples in municipalities three times a week. Additional sentinel monitoring was undertaken in neighbourhoods, hospitals, long-term care facilities, worksites, shelters, and schools. Results were shared on the public COVID Data Tracker website (https://covid-tracker.chi-csm.ca/). Data was additionally linked with hospital outcomes, workforce absenteeism and outbreak information. ResultsWastewater-based surveillance (WBS) for SARS-CoV-2 genomic RNA associates very strongly with clinically diagnosed cases and health resource utilization, providing a ≥6-day leading indicator. WBS can effectively be performed across a range of geographic scales (from cities to individual facilities), ensuring actionable data that is relevant to end-users. We have published how outbreaks across a range of high-risk facilities can be monitored and predicted with WBS and can also be used to model COVID-19-associated workforce absenteeism. Emails and website interactions suggested widespread citizen engagement using data for evidence-based decisions. ConclusionWBS is a valuable tool for identifying potential outbreaks and tailoring response measures at the policy level and by individual citizens. We've created customizable real-time data-sharing tools catering to both the public (enhanced data transparency) and government (actionable insights).