Predicting clinical resistance prevalence using sewage metagenomic data

Developing wastewater-based surveillance schemes for multiple pathogens: The WastPan project in Finland

Wastewater comprises multiple pathogens and offers a potential for wastewater-based surveillance (WBS) to track the prevalence of communicable diseases. The Finnish WastPan project aimed to establish wastewater-based pandemic preparedness for multiple pathogens (viruses, bacteria, parasites, fungi), including antimicrobial resistance (AMR). This article outlines WastPan's experiences in this project, including the criteria for target selection, sampling locations, frequency, analysis methods, and results communication. Target selection relied on epidemiological and microbiological evidence and practical feasibility. Within the WastPan framework, wastewater samples were collected between 2021 and 2023 from 10 wastewater treatment plants (WWTPs) covering 40 % of Finland's population. WWTP selection was validated for reported cases of Extended Spectrum Beta-lactamase-producing bacterial pathogens (Escherichia coli and Klebsiella pneumoniae) from the National Infectious Disease Register. The workflow included 24-h composite influent samples, with one fraction for culture-based analysis (bacteria and fungi) and the rest of the sample was reserved for molecular analysis (viruses, bacteria, antibiotic resistance genes, and parasites). The reproducibility of the monitoring workflow was assessed for SARS-CoV-2 through inter-laboratory comparisons using the N2 and N1 assays. Identical protocols were applied to same-day samples, yielding similar positivity trends in the two laboratories, but the N2 assay achieved a significantly higher detection rate (Laboratory 1: 91.5 %; Laboratory 2: 87.4 %) than the N1 assay (76.6 %) monitored only in Laboratory 2 (McNemar, p < 0.001 Lab 1, = 0.006 Lab 2). This result indicates that the selection of monitoring primers and assays may impact monitoring sensitivity in WBS. Overall, the current study recommends that the selection of sampling frequencies and population coverage of the monitoring should be based on pathogen-specific epidemiological characteristics. For example, pathogens that are stable over time may need less frequent annual sampling, while those that are occurring across regions may require reduced sample coverage. Here, WastPan successfully piloted WBS for monitoring multiple pathogens, highlighting the significance of one-litre community composite wastewater samples for assessing community health. The infrastructure established for COVID-19 WBS is valuable for monitoring various pathogens. The prioritization of the monitoring targets optimizes resource utilization. In the future legislative support in target selection, coverage determination, and sustained funding for WBS is recomended.

High-throughput microfluidic quantitative PCR system for the simultaneous detection of antibiotic resistance genes and bacterial and viral pathogens in wastewater

Comprehensive data on bacterial and viral pathogens of diarrhea and studies applying culture-independent methods for examining antibiotic resistance in wastewater are lacking. This study aimed to simultaneously quantify antibiotic resistance genes (ARGs), class 1 integron-integrase (int1), bacterial and viral pathogens of diarrhea, 16S rRNA, and other indicators using a high-throughput quantitative PCR (HT-qPCR) system. Thirty-six grab wastewater samples from a wastewater treatment plant in Japan, collected three times a month between August 2022 and July 2023, were centrifuged, followed by nucleic acid extraction, reverse transcription, and HT-qPCR. Fourteen targets were included, and HT-qPCR was performed on the Biomark X9™ System (Standard BioTools). For all qPCR assays, R2 was ≥0.978 and the efficiencies ranged from 90.5% to 117.7%, exhibiting high performance. Of the 36 samples, 20 (56%) were positive for Norovirus genogroup II (NoV-GII), whereas Salmonella spp. and Campylobacter jejuni were detected in 24 (67%) and Campylobacter coli in 13 (36%) samples, with mean concentrations ranging from 3.2 ± 0.8 to 4.7 ± 0.3 log10 copies/L. NoV-GII detection ratios and concentrations were higher in winter and spring. None of the pathogens of diarrhea correlated with acute gastroenteritis cases, except for NoV-GII, suggesting the need for data on specific bacterial infections to validate bacterial wastewater-based epidemiology (WBE). All samples tested positive for sul1, int1, and blaCTX-M, irrespective of season. The less explored blaNDM-1 showed a wide prevalence (>83%) and consistent abundance ranging from 4.3 ± 1.0 to 4.9 ± 0.2 log10 copies/L in all seasons. sul1 was the predominant ARG, whereas absolute abundances of 16S rRNA, int1, and blaCTX-M varied seasonally. int1 was significantly correlated with blaCTX-M in autumn and spring, whereas it showed no correlation with blaNDM-1, questioning the applicability of int1 as a sole indicator of overall resistance determinants. This study exhibited that the HT-qPCR system is pivotal for WBE.

Integron-associated genes are reliable indicators of antibiotic resistance in wastewater despite treatment- and seasonality-driven fluctuations

The present study aims to characterize the bacterial community, resistome and integron abundance of a municipal wastewater treatment plant (WWTP) over the course of 12 months and evaluate the year-long performance of integron-related genes as potential indicators of antibiotic resistance mechanisms in influents and effluents. For that, total DNA was extracted and subjected to 16S rRNA-targeted metabarcoding, high-throughput (HT) qPCR (48 targets) and standard qPCR (5 targets). Targets included integrase genes, antibiotic resistance genes (ARGs) and putative pathogenic groups. A total of 16 physicochemical parameters determined in the wastewater samples were also considered. Results revealed that the WWTP treatment significantly impacted the bacterial community, as well as the content in ARGs and integrase genes. Indeed, there was a relative enrichment from influent to effluent of 13 pathogenic groups (e.g., Legionella and Mycobacterium) and genes conferring resistance to sulphonamides, aminoglycosides and disinfectants. Effluent samples (n = 25) also presented seasonal differences, with an increase of the total ARGs' concentration in summer, and differences between winter and summer on relative abundance of sulphonamide and disinfectant resistance mechanisms. From the eight putative integron-related genes selected, all were positively correlated with the total ARGs' content in wastewater and the relative abundance of resistance to most of the specific antibiotic classes. The genes intI1, blaGES and qacE∆1 were the most strongly correlated with the total concentration of ARGs. Genes blaGES and blaVIM, were better correlated to resistance to beta-lactams, aminoglycosides and tetracyclines. This study supports the use of integron-related genes as powerful indicators of antibiotic resistance in wastewater, being robust despite the variability caused by wastewater treatment and seasonality.

Characterisation of microbial communities and quantification of antibiotic resistance genes in Italian wastewater treatment plants using 16S rRNA sequencing and digital PCR

The spread of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in humans, animals and environment is a growing threat to public health. Wastewater treatment plants (WWTPs) are crucial in mitigating the risk of environmental contamination by effectively removing contaminants before discharge. However, the persistence of ARB and ARGs even after treatment is a challenge for the management of water system. To comprehensively assess antimicrobial resistance dynamics, we conducted a one-year monitoring study in three WWTPs in central Italy, both influents and effluents. We used seasonal sampling to analyze microbial communities by 16S rRNA, as well as to determine the prevalence and behaviour of major ARGs (sul1, tetA, blaTEM, blaOXA-48, blaCTX-M-1 group, blaKPC) and the class 1 Integron (int1). Predominant genera included in order: Arcobacter, Acinetobacter, Flavobacterium, Pseudarcobacter, Bacteroides, Aeromonas, Trichococcus, Cloacibacterium, Pseudomonas and Streptococcus. A higher diversity of bacterial communities was observed in the effluents compared to the influents. Within these communities, we also identified bacteria that may be associated with antibiotic resistance and pose a significant threat to human health. The mean concentrations (in gene copies per liter, gc/L) of ARGs and int1 in untreated wastewater (absolute abundance) were as follows: sul1 (4.1 × 109), tetA (5.2 × 108), blaTEM (1.1 × 108), blaOXA-48 (2.1 × 107), blaCTX-M-1 group (1.1 × 107), blaKPC (9.4 × 105), and int1 (5.5 × 109). The mean values in treated effluents showed reductions ranging from one to three log. However, after normalizing to the 16S rRNA gene (relative abundance), it was observed that in 37.5 % (42/112) of measurements, the relative abundance of ARGs increased in effluents compared to influents. Furthermore, correlations were identified between ARGs and bacterial genera including priority pathogens. This study improves our understanding of the dynamics of ARGs and provides insights to develop more effective strategies to reduce their spread, protecting public health and preserving the future efficacy of antibiotics.

Monitoring ESBL-Escherichia coli in Swiss wastewater between November 2021 and November 2022: insights into population carriage

Antimicrobial resistance (AMR) poses a global health threat, causing millions of deaths annually, with expectations of increased impact in the future. Wastewater surveillance offers a cost-effective, non-invasive tool to understand AMR carriage trends within a population. We monitored extended-spectrum β-lactamase producing Escherichia coli (ESBL-E. coli) weekly in influent wastewater from six wastewater treatment plants (WWTPs) in Switzerland (November 2021 to November 2022) to investigate spatio-temporal variations, explore correlations with environmental variables, develop a predictive model for ESBL-E. coli carriage in the community, and detect the most prevalent ESBL-genes. We cultured total and ESBL-E. coli in 300 wastewater samples to quantify daily loads and percentage of ESBL-E. coli. Additionally, we screened 234 ESBL-E. coli isolates using molecular methods for the presence of 18 ESBL-gene families. We found a population-weighted mean percentage of ESBL-E. coli of 1.9% (95% confidence interval: 1.8-2%) across all sites and weeks, which can inform ESBL-E. coli carriage. Concentrations of ESBL-E. coli varied across WWTPs and time, with higher values observed in WWTPs serving larger populations. Recent precipitations (previous 24/96 h) showed no significant association with ESBL-E. coli, while temperature occasionally had a moderate impact (P < 0.05, correlation coefficients approximately 0.40) in some locations. We identified blaCTX-M-1, blaCTX-M-9, and blaTEM as the predominant ESBL-gene families. Our study demonstrates that wastewater-based surveillance of culturable ESBL-E. coli provides insights into AMR trends in Switzerland and may also inform resistance. These findings establish a foundation for long term, nationally established monitoring protocols and provide information that may help inform targeted public health interventions.

IMPORTANCE

Antimicrobial resistance (AMR) is a global health threat and is commonly monitored in clinical settings, given its association with the risk of antimicrobial-resistant infections. Nevertheless, tracking AMR within a community proves challenging due to the substantial sample size required for a representative population, along with high associated costs and privacy concerns. By investigating high resolution temporal and geographic trends in extended-spectrum beta-lactamase producing Escherichia coli in wastewater, we provide an alternative approach to monitor AMR dynamics, distinct from the conventional clinical settings focus. Through this approach, we develop a mechanistic model, shedding light on the relationship between wastewater indicators and AMR carriage in the population. This perspective contributes valuable insights into trends of AMR carriage, emphasizing the importance of wastewater surveillance in informing effective public health interventions.

Antibiotic resistance monitoring in wastewater in the Nordic countries: A systematic review

The Nordic countries (Denmark, Finland, Iceland, Norway, and Sweden) have effectively kept lower antibiotic-resistant bacterial (ARB) pathogen rates than many other countries. However, in recent years, these five countries have encountered a rise in ARB cases and challenges in treating infections due to the growing prevalence of ARB pathogens. Wastewater-based surveillance (WBS) is a valuable supplement to clinical methods for ARB surveillance, but there is a lack of comprehensive understanding of WBS application for ARB in the Nordic countries. This review aims to compile the latest state-of-the-art developments in WBS for ARB monitoring in the Nordic countries and compare them with clinical surveillance practices. After reviewing 1480 papers from the primary search, 54 were found relevant, and 15 additional WBS-related papers were included. Among 69 studies analyzed, 42 dedicated clinical epidemiology, while 27 focused on wastewater monitoring. The PRISMA review of the literature revealed that Nordic countries focus on four major WBS objectives of ARB: assessing ARB in the human population, identifying ARB evading wastewater treatment, quantifying removal rates, and evaluating potential ARB evolution during the treatment process. In both clinical and wastewater contexts, the most studied targets were pathogens producing carbapenemase and extended-spectrum beta-lactamase (ESBL), primarily Escherichia coli and Klebsiella spp. However, vancomycin-resistant Enterococcus (VRE) and methicillin-resistant Staphylococcus aureus (MRSA) have received more attention in clinical epidemiology than in wastewater studies, probably due to their lower detection rates in wastewater. Clinical surveillance has mostly used culturing, antibiotic susceptibility testing, and genotyping, but WBS employed PCR-based and metagenomics alongside culture-based techniques. Imported cases resulting from international travel and hospitalization abroad appear to have frequently contributed to the rise in ARB pathogen cases in these countries. The many similarities between the Nordic countries (e.g., knowledge exchange practices, antibiotic usage patterns, and the current ARB landscape) could facilitate collaborative efforts in developing and implementing WBS for ARB in population-level screening.

Evaluation of culture conditions for sewage-based surveillance of antibiotic resistance in Klebsiella pneumoniae

BACKGROUND

Recent studies have shown promise in predicting clinical antibiotic resistance rates from sewage data. Few have focused on Klebsiella pneumoniae, despite its virulence and importance as carrier of antibiotic resistance. Several media have been suggested for the isolation of K. pneumoniae from complex samples. However, comprehensive evaluations of culture protocols for isolation of K. pneumoniae from sewage are lacking.

METHODS

Here, influent samples from a major Swedish sewage treatment plant were used to evaluate ten culture conditions in parallel: cultivation on Brilliant green containing Inositol-Nitrate-Deoxycholate agar (BIND), Bruce agar, Klebsiella ChromoSelect Selective agar®, MacConkey-Inositol-Carbenicillin, or Simmons Citrate Agar with Inositol (SCAI) incubated at either 37°C or 42°C for 44 h. The culture conditions were compared based on colony counts of presumed K. pneumoniae and identification precision assessed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

RESULTS

The sensitivity was lowest for BIND, whereas it was similar for the other media irrespective of incubation temperature. For four media, a better precision was observed after incubation at 42°C compared to 37°C, to a large extent explained by a lower frequency of captured Klebsiella oxytoca. SCAI incubated at 42°C showed the highest precision (84.4%). By combining this protocol with subsequent antibiotic resistance screening of collected isolates, low resistance rates in sewage K. pneumoniae were revealed, potentially reflecting the local resistance landscape.

CONCLUSION

When combined with downstream analyses, SCAI incubated at 42°C could be a valuable culture protocol for sewage-based studies on various aspects of K. pneumoniae epidemiology including antibiotic resistance prevalence.

Combating antibiotic resistance using wastewater surveillance: Significance, applications, challenges, and future directions

The global increase of antibiotic resistance (AR) and resistant infections call for effective surveillance methods for understanding and mitigating (re)-emerging public health risks. Wastewater surveillance (WS) of antibiotic resistance is an emerging, but currently under-utilized decision-support tool in public health systems. Recent years have witnessed an increase in evidence linking antibiotic resistance in wastewaters to that of the community. To date, very few comprehensive reviews exist on the application of WS to understand AR and resistant infections in population. Current and emerging AR detection methods, and their merits and limitations are discussed. Wastewater surveillance has several merits relative to individual testing, including; (1) low per capita testing cost, (2) high spatial coverage, (3) low requirement for diagnostic equipment, and (4) detection of health threats ahead of real outbreaks. The applications of WS as an early warning system and decision support tool to understand and mitigate AR are discussed. Wastewater surveillance could be a tool of choice in low-income settings lacking resources and diagnostic facilities for individual testing. To demonstrate the utility of WS, empirical evidence from field case studies is presented. However, constraints still exist, including; (1) lack of standardized protocols, (2) the clinical utility and sensitivity of WS-based data, (3) uncertainties in relating WS data to pathogenic and virulent bacteria, and (4) whether or not AR in stools and ultimately wastewater represent the complete human resistome. Finally, further prospects are presented, include knowledge gaps on; (1) development of low-cost biosensors for AR, (2) development of WS protocols (sampling, processing, interpretation), (3) further pilot scale studies to understand the opportunities and limits of WS, and (4) development of computer-based analytical tools to facilitate rapid data collection, visualization and interpretation. Therefore, the present paper discusses the principles, opportunities, and constraints of wastewater surveillance applications to understand AR and safeguard public health.

Simultaneous stream assessment of antibiotics, bacteria, antibiotic resistant bacteria, and antibiotic resistance genes in an agricultural region of the United States

Antimicrobial resistance (AMR) is now recognized as a leading global threat to human health. Nevertheless, there currently is a limited understanding of the environment's role in the spread of AMR and antibiotic resistance genes (ARGs). In 2019, the U.S. Geological Survey conducted the first statewide assessment of antibiotic resistant bacteria (ARB) and ARGs in surface water and bed sediment collected from 34 stream locations across Iowa. Environmental samples were analyzed for a suite of 29 antibiotics and plated on selective media for 15 types of bacteria growth; DNA was extracted from culture growth and used in downstream polymerase chain reaction (PCR) assays for the detection of 24 ARGs. ARGs encoding resistance to antibiotics of clinical importance to human health and disease prevention were prioritized as their presence in stream systems has the potential for environmental significance. Total coliforms, Escherichia coli (E. coli), and staphylococci were nearly ubiquitous in both stream water and stream bed sediment samples, with enterococci present in 97 % of water samples, and Salmonella spp. growth present in 94 % and 67 % of water and bed sediment samples. Bacteria enumerations indicate that high bacteria loads are common in Iowa's streams, with 23 (68 %) streams exceeding state guidelines for primary contact for E. coli in recreational waters and 6 (18 %) streams exceeding the secondary contact advisory level. Although antibiotic-resistant E. coli growth was detected from 40 % of water samples, vancomycin-resistant enterococci (VRE) and penicillinase-resistant Staphylococcus aureus (MRSA) colony growth was detected from nearly all water samples. A total of 14 different ARGs were detected from viable bacteria cells from 30 Iowa streams (88 %, n = 34). Study results provide the first baseline understanding of the prevalence of ARB and ARGs throughout Iowa's waterways and health risk potential for humans, wildlife, and livestock using these waterways for drinking, irrigating, or recreating.

Advancing pathogen genomics in resource-limited settings

Genomic sequencing has emerged as a powerful tool to enhance early pathogen detection and characterization with implications for public health and clinical decision making. Although widely available in developed countries, the application of pathogen genomics among low-resource, high-disease burden settings remains at an early stage. In these contexts, tailored approaches for integrating pathogen genomics within infectious disease control programs will be essential to optimize cost efficiency and public health impact. We propose a framework for embedding pathogen genomics within national surveillance plans across a spectrum of surveillance and laboratory capacities. We adopt a public health approach to genomics and examine its application to high-priority diseases relevant in resource-limited settings. For each grouping, we assess the value proposition for genomics to inform public health and clinical decision-making, alongside its contribution toward research and development of novel diagnostics, therapeutics, and vaccines.

Exploiting genomics for antimicrobial resistance surveillance at One Health interfaces

The intersection of human, animal, and ecosystem health at One Health interfaces is recognised as being of key importance in the evolution and spread of antimicrobial resistance (AMR) and represents an important, and yet rarely realised opportunity to undertake vital AMR surveillance. A working group of international experts in pathogen genomics, AMR, and One Health convened to take part in a workshop series and online consultation focused on the opportunities and challenges facing genomic AMR surveillance in a range of settings. Here we outline the working group's discussion of the potential utility, advantages of, and barriers to, the implementation of genomic AMR surveillance at One Health interfaces and propose a series of recommendations for addressing these challenges. Embedding AMR surveillance at One Health interfaces will require the development of clear beneficial use cases, especially in low-income and middle-income countries. Evidence of directionality, risks to human and animal health, and potential trade implications were also identified by the working group as key issues. Addressing these challenges will be vital to enable genomic surveillance technology to reach its full potential for assessing the risk of transmission of AMR between the environment, animals, and humans at One Health interfaces.

Association of health, nutrition, and socioeconomic variables with global antimicrobial resistance: a modelling study

BACKGROUND

Although antimicrobial use is a key selector for antimicrobial resistance, recent studies have suggested that the ecological context in which antimicrobials are used might provide important factors for the prediction of the emergence and spread of antimicrobial resistance.

METHODS

We used 1547 variables from the World Bank dataset consisting of socioeconomic, developmental, health, and nutritional indicators; data from a global sewage-based study on antimicrobial resistance (abundance of antimicrobial resistance genes [ARGs]); and data on antimicrobial usage computed from the ECDC database and the IQVIA database. We characterised and built models predicting the global resistome at an antimicrobial class level. We used a generalised linear mixed-effects model to estimate the association between antimicrobial usage and ARG abundance in the sewage samples; a multivariate random forest model to build predictive models for each antimicrobial resistance class and to select the most important variables for ARG abundance; logistic regression models to test the association between the predicted country-level antimicrobial resistance abundance and the country-level proportion of clinical resistant bacterial isolates; finite mixture models to investigate geographical heterogeneities in the abundance of ARGs; and multivariate finite mixture models with covariates to investigate the effect of heterogeneity in the association between the most important variables and the observed ARG abundance across the different country subgroups. We compared our predictions with available clinical phenotypic data from the SENTRY Antimicrobial Surveillance Program from eight antimicrobial classes and 12 genera from 56 countries.

FINDINGS

Using antimicrobial use data from between Jan 1, 2016, and Dec 31, 2019, we found that antimicrobial usage was not significantly associated with the global ARG abundance in sewage (p=0·72; incidence rate ratio 1·02 [95% CI 0·92-1·13]), whereas country-specific World Bank's variables explained a large amount of variation. The importance of the World Bank variables differed between antimicrobial classes and countries. Generally, the estimated global ARG abundance was positively associated with the prevalence of clinical phenotypic resistance, with a strong association for bacterial groups in the human gut. The associations between bacterial groups and ARG abundance were positive and significantly different from zero for the aminoglycosides (three of the four of the taxa tested), β-lactam (all the six microbial groups), fluoroquinolones (seven of nine of the microbial groups), glycopeptide (one microbial group tested), folate pathway antagonists (four of five microbial groups), and tetracycline (two of nine microbial groups).

INTERPRETATION

Metagenomic analysis of sewage is a robust approach for the surveillance of antimicrobial resistance in pathogens, especially for bacterial groups associated with the human gut. Additional studies on the associations between important socioeconomic, nutritional, and health factors and antimicrobial resistance should consider the variation in these associations between countries and antimicrobial classes.

FUNDING

EU Horizon 2020 and Novo Nordisk Foundation.

Comparison of metagenomic and targeted methods for sequencing human pathogenic viruses from wastewater

Wastewater-based epidemiology is a powerful tool for monitoring the emergence and spread of viral pathogens at the population scale. Typical polymerase chain reaction (PCR)-based methods of quantitative and genomic monitoring of viruses in wastewater provide high sensitivity and specificity. However, these methods are limited to the surveillance of target viruses in a single assay and require prior knowledge of the target genome(s). Metagenomic sequencing methods may represent a target-agnostic approach to viral wastewater monitoring, allowing for the detection of a broad range of target viruses, including potentially novel and emerging pathogens. In this study, targeted and untargeted metagenomic sequencing methods were compared with tiled-PCR sequencing for the detection and genotyping of viral pathogens in wastewater samples. Deep shotgun metagenomic sequencing was unable to generate sufficient genome coverage of human pathogenic viruses for robust genomic epidemiology, with samples dominated by bacteria. Hybrid-capture enrichment of shotgun libraries for respiratory viruses led to significant increases in genome coverage for a range of targets. Tiled-PCR sequencing led to further improvements in genome coverage compared to hybrid capture for severe acute respiratory syndrome coronavirus 2, enterovirus D68, norovirus GII, and human adenovirus F41 in wastewater samples. In conclusion, untargeted shotgun sequencing was unsuitable for genomic monitoring of the low virus concentrations in wastewater samples analyzed in this study. Hybrid-capture enrichment represented a viable method for simultaneous genomic epidemiology of a range of viral pathogens, while tiled-PCR sequencing provided the optimal genome coverage for individual viruses with the minimum sequencing depth. IMPORTANCE Most public health initiatives that monitor viruses in wastewater have utilized quantitative polymerase chain reaction (PCR) and whole genome PCR sequencing, mirroring techniques used for viral epidemiology in individuals. These techniques require prior knowledge of the target viral genome and are limited to monitoring individual or small groups of viruses. Metagenomic sequencing may offer an alternative strategy for monitoring a broad spectrum of viruses in wastewater, including novel and emerging pathogens. In this study, while amplicon sequencing gave high viral genome coverage, untargeted shotgun sequencing of total nucleic acid samples was unable to detect human pathogenic viruses with enough sensitivity for use in genomic epidemiology. Enrichment of shotgun libraries for respiratory viruses using hybrid-capture technology provided genotypic information on a range of viruses simultaneously, indicating strong potential for wastewater surveillance. This type of targeted metagenomics could be used for monitoring diverse targets, such as pathogens or antimicrobial resistance genes, in environmental samples.

Wastewater-Based Surveillance of Antimicrobial Resistance in Niger: An Exploratory Study

Wastewater-based surveillance is increasingly recognized as an important approach to monitoring population-level antimicrobial resistance (AMR). In this exploratory study, we examined the use of metagenomics to evaluate AMR using untreated wastewater samples routinely collected by the Niger national polio surveillance program. Forty-eight stored samples from two seasons each year over 4 years (2016-2019) in three regions were selected for inclusion in this study and processed using unbiased DNA deep sequencing. Normalized number of reads of genetic determinants for different antibiotic classes were compared over time, by season, and by location. Correlations in resistance were examined among classes. Changes in reads per million per year were demonstrated for several classes, including decreases over time in resistance determinants for phenicols (-3.3, 95% CI: -8.7 to -0.1, P = 0.029) and increases over time for aminocoumarins (3.8, 95% CI: 0.0 to 11.4, P = 0.043), fluoroquinolones (6.8, 95% CI: 0.0 to 20.5, P = 0.048), and beta-lactams (0.85, 95% CI: 0.1 to 1.7, P = 0.006). Sulfonamide resistance was higher in the post-rainy season compared with the dry season (5.2-fold change, 95% CI: 3.4 to 7.9, P < 0.001). No differences were detected when comparing other classes by season or by site for any antibiotic class. Positive correlations were identified in genetic determinants of resistance among several antibiotic classes. These results demonstrate the potential utility of leveraging existing wastewater sample collection in this setting for AMR surveillance.

Social demographics determinants for resistome and microbiome variation of a multiethnic community in Southern Malaysia

The prevalence of antibiotic-resistant bacteria in Southeast Asia is a significant concern, yet there is limited research on the gut resistome and its correlation with lifestyle and environmental factors in the region. This study aimed to profile the gut resistome of 200 individuals in Malaysia using shotgun metagenomic sequencing and investigate its association with questionnaire data comprising demographic and lifestyle variables. A total of 1038 antibiotic resistance genes from 26 classes were detected with a mean carriage rate of 1.74 ± 1.18 gene copies per cell per person. Correlation analysis identified 14 environmental factors, including hygiene habits, health parameters, and intestinal colonization, that were significantly associated with the resistome (adjusted multivariate PERMANOVA, p < 0.05). Notably, individuals with positive yeast cultures exhibited a reduced copy number of 15 antibiotic resistance genes. Network analysis highlighted Escherichia coli as a major resistome network hub, with a positive correlation to 36 antibiotic-resistance genes. Our findings suggest that E. coli may play a pivotal role in shaping the resistome dynamics in Segamat, Malaysia, and its abundance is strongly associated with the community's health and lifestyle habits. Furthermore, the presence of yeast appears to be associated with the suppression of antibiotic-resistance genes.

Global within-species phylogenetics of sewage microbes suggest that local adaptation shapes geographical bacterial clustering

Most investigations of geographical within-species differences are limited to focusing on a single species. Here, we investigate global differences for multiple bacterial species using a dataset of 757 metagenomics sewage samples from 101 countries worldwide. The within-species variations were determined by performing genome reconstructions, and the analyses were expanded by gene focused approaches. Applying these methods, we recovered 3353 near complete (NC) metagenome assembled genomes (MAGs) encompassing 1439 different MAG species and found that within-species genomic variation was in 36% of the investigated species (12/33) coherent with regional separation. Additionally, we found that variation of organelle genes correlated less with geography compared to metabolic and membrane genes, suggesting that the global differences of these species are caused by regional environmental selection rather than dissemination limitations. From the combination of the large and globally distributed dataset and in-depth analysis, we present a wide investigation of global within-species phylogeny of sewage bacteria. The global differences found here emphasize the need for worldwide data sets when making global conclusions.

High-resolution characterization of short-term temporal variability in the taxonomic and resistome composition of wastewater influent

Wastewater-based epidemiology (WBE) for population-level surveillance of antimicrobial resistance (AMR) is gaining significant traction, but the impact of wastewater sampling methods on results is unclear. In this study, we characterized taxonomic and resistome differences between single-timepoint-grab and 24 h composites of wastewater influent from a large UK-based wastewater treatment work [WWTW (population equivalent: 223 435)]. We autosampled hourly influent grab samples (n=72) over three consecutive weekdays, and prepared additional 24 h composites (n=3) from respective grabs. For taxonomic profiling, metagenomic DNA was extracted from all samples and 16S rRNA gene sequencing was performed. One composite and six grabs from day 1 underwent metagenomic sequencing for metagenomic dissimilarity estimation and resistome profiling. Taxonomic abundances of phyla varied significantly across hourly grab samples but followed a repeating diurnal pattern for all 3 days. Hierarchical clustering grouped grab samples into four time periods dissimilar in both 16S rRNA gene-based profiles and metagenomic distances. 24H-composites resembled mean daily phyla abundances and showed low variability of taxonomic profiles. Of the 122 AMR gene families (AGFs) identified across all day 1 samples, single grab samples identified a median of six (IQR: 5-8) AGFs not seen in the composite. However, 36/36 of these hits were at lateral coverage <0.5 (median: 0.19; interquartile range: 0.16-0.22) and potential false positives. Conversely, the 24H-composite identified three AGFs not seen in any grab with higher lateral coverage (0.82; 0.55-0.84). Additionally, several clinically significant human AGFs (bla _VIM, bla _IMP, bla _KPC) were intermittently or completely missed by grab sampling but captured by the 24 h composite. Wastewater influent undergoes significant taxonomic and resistome changes on short timescales potentially affecting interpretation of results based on sampling strategy. Grab samples are more convenient and potentially capture low-prevalence/transient targets but are less comprehensive and temporally variable. Therefore, we recommend 24H-composite sampling where feasible. Further validation and optimization of WBE methods is vital for its development into a robust AMR surveillance approach.

Metagenomic Analysis of the Abundance and Composition of Antibiotic Resistance Genes in Hospital Wastewater in Benin, Burkina Faso, and Finland

Antibiotic resistance is a global threat to human health, with the most severe effect in low- and middle-income countries. We explored the presence of antibiotic resistance genes (ARGs) in the hospital wastewater (HWW) of nine hospitals in Benin and Burkina Faso, two low-income countries in West Africa, with shotgun metagenomic sequencing. For comparison, we also studied six hospitals in Finland. The highest sum of the relative abundance of ARGs in the 68 HWW samples was detected in Benin and the lowest in Finland. HWW resistomes and mobilomes in Benin and Burkina Faso resembled each other more than those in Finland. Many carbapenemase genes were detected at various abundances, especially in HWW from Burkina Faso and Finland. The bla_GES genes, the most widespread carbapenemase gene in the Beninese HWW, were also found in water intended for hand washing and in a puddle at a hospital yard in Benin. mcr genes were detected in the HWW of all three countries, with mcr-5 being the most common mcr gene. These and other mcr genes were observed in very high relative abundances, even in treated wastewater in Burkina Faso and a street gutter in Benin. The results highlight the importance of wastewater treatment, with particular attention to HWW. IMPORTANCE The global emergence and increased spread of antibiotic resistance threaten the effectiveness of antibiotics and, thus, the health of the entire population. Therefore, understanding the resistomes in different geographical locations is crucial in the global fight against the antibiotic resistance crisis. However, this information is scarce in many low- and middle-income countries (LMICs), such as those in West Africa. In this study, we describe the resistomes of hospital wastewater in Benin and Burkina Faso and, as a comparison, Finland. Our results help to understand the hitherto unrevealed resistance in Beninese and Burkinabe hospitals. Furthermore, the results emphasize the importance of wastewater management infrastructure design to minimize exposure events between humans, HWW, and the environment, preventing the circulation of resistant bacteria and ARGs between humans (hospitals and community) and the environment.

Indian sewage microbiome has unique community characteristics and potential for population-level disease predictions

Sewage wastewater pollutes water and poses a public health issue but it could also prove useful in certain research domains. Sewage is a complex niche relevant for research concerning 'one-health', human health, pollution and antibiotic resistance. Indian gut microbiome is also understudied due to sampling constraints and sewage could be used to explore it. Ostensibly, Indian sewage needs to be studied and here, we performed a cross-sectional pan-India sewage sampling to generate the first comprehensive Indian sewage microbiome. Indian sewage showed predominance of Burkholderiaceae, Rhodocyclaceae, Veillonellaceae, Prevotellaceae, etc. and has high representation of gut microbes. The identified gut microbes have overrepresentation of Veillonellaceae, Rikenellaceae, Streptococcaceae, and Bacillaceae. Imputed metagenomics of sewage microbiome indicated dominance of transport, motility, peptidases, amino acid metabolism, and antibiotic resistance genes. Microbiome-disease associations drawn using simple decision tree and random forest analysis identified specific microbes as potential predictors of diabetes and obesity in a city. Altogether, we generated the first Indian sewage microbiome and our non-invasive, high-throughput workflow could be emulated for future research, wastewater-based epidemiology and designing policies concerning public health.

A proposed framework for the identification of indicator genes for monitoring antibiotic resistance in wastewater: Insights from metagenomic sequencing

Antibiotic resistance is one of the greatest threats to global human and animal health of our time. Municipal wastewater has been identified as a hotspot of antibiotic resistance contamination to water bodies. However, there are numerous potential antibiotic resistant pathogens and their associated antibiotic resistance genes (ARGs), making it difficult to implement routine monitoring that addresses the breadth of the problem. The objective of this study was to identify candidate indicator ARGs for monitoring antibiotic resistance in wastewater and receiving water bodies. We developed a framework to identify indicator ARGs that incorporated clinical relevance, abundance in wastewater, geographic ubiquity, environmental relevance, ARG mobility, associations with mobile genetic elements, and the availability of quantitative analytical methods. To identify indicator ARGs, published metagenomic sequencing data from 191 wastewater samples originating from 64 countries across the world were obtained from online public repositories. Through ARG annotation and network analysis, this framework revealed 56 candidate indicator ARGs distributed across four modules of strongly correlated ARGs, with one ARG from each module (oqxA, ermB, sul1, and mexE) proposed as a minimally redundant monitoring target. The results of this study provide the basis for antibiotic resistance surveillance and monitoring framework in wastewater and contaminated waterways.

Wastewater surveillance of antibiotic-resistant bacterial pathogens: A systematic review

Infectious diseases caused by antibiotic-resistant bacterial (ARB) pathogens are a serious threat to human and animal health. The active surveillance of ARB using an integrated one-health approach can help to reduce the emergence and spread of ARB, reduce the associated economic impact, and guide antimicrobial stewardship programs. Wastewater surveillance (WWS) of ARB provides composite samples for a total population, with easy access to the mixed community microbiome. This concept is emerging rapidly, but the clinical utility, sensitivity, and uniformity of WWS of ARB remain poorly understood especially in relation to clinical evidence in sewershed communities. Here, we systematically searched the literature to identify studies that have compared findings from WWS of ARB and antibiotic resistance genes (ARG) with clinical evidence in parallel, thereby evaluating how likely WWS of ARB and ARG can relate to the clinical cases in communities. Initially, 2,235 articles were obtained using the primary search keywords, and 1,219 articles remained after de-duplication. Among these, 35 articles fulfilled the search criteria, and an additional 13 relevant articles were included by searching references in the primary literature. Among the 48 included papers, 34 studies used a culture-based method, followed by 11 metagenomics, and three PCR-based methods. A total of 28 out of 48 included studies were conducted at the single sewershed level, eight studies involved several countries, seven studies were conducted at national or regional scales, and five at hospital levels. Our review revealed that the performance of WWS of ARB pathogens has been evaluated more frequently for Escherichia coli, Enterococcus spp., and other members of the family Enterobacteriaceae, but has not been uniformly tested for all ARB pathogens. Many wastewater-based ARB studies comparing the findings with clinical evidence were conducted to evaluate the public health risk but not to relate with clinical evidence and to evaluate the performance of WWS of ARB. Indeed, relating WWS of ARB with clinical evidence in a sewershed is not straightforward, as the source of ARB in wastewater cannot be only from symptomatic human individuals but can also be from asymptomatic carriers as well as from animal sources. Further, the varying fates of each bacterial species and ARG within the sewerage make the aim of connecting WWS of ARB with clinical evidence more complicated. Therefore, future studies evaluating the performance of many AMR pathogens and their genes for WWS one by one can make the process simpler and the interpretation of results easier.

Demonstrating a Comprehensive Wastewater-Based Surveillance Approach That Differentiates Globally Sourced Resistomes

Wastewater-based surveillance (WBS) for disease monitoring is highly promising but requires consistent methodologies that incorporate predetermined objectives, targets, and metrics. Herein, we describe a comprehensive metagenomics-based approach for global surveillance of antibiotic resistance in sewage that enables assessment of 1) which antibiotic resistance genes (ARGs) are shared across regions/communities; 2) which ARGs are discriminatory; and 3) factors associated with overall trends in ARGs, such as antibiotic concentrations. Across an internationally sourced transect of sewage samples collected using a centralized, standardized protocol, ARG relative abundances (16S rRNA gene-normalized) were highest in Hong Kong and India and lowest in Sweden and Switzerland, reflecting national policy, measured antibiotic concentrations, and metal resistance genes. Asian versus European/US resistomes were distinct, with macrolide-lincosamide-streptogramin, phenicol, quinolone, and tetracycline versus multidrug resistance ARGs being discriminatory, respectively. Regional trends in measured antibiotic concentrations differed from trends expected from public sales data. This could reflect unaccounted uses, captured only by the WBS approach. If properly benchmarked, antibiotic WBS might complement public sales and consumption statistics in the future. The WBS approach defined herein demonstrates multisite comparability and sensitivity to local/regional factors.

A curated data resource of 214K metagenomes for characterization of the global antimicrobial resistome

The growing threat of antimicrobial resistance (AMR) calls for new epidemiological surveillance methods, as well as a deeper understanding of how antimicrobial resistance genes (ARGs) have been transmitted around the world. The large pool of sequencing data available in public repositories provides an excellent resource for monitoring the temporal and spatial dissemination of AMR in different ecological settings. However, only a limited number of research groups globally have the computational resources to analyze such data. We retrieved 442 Tbp of sequencing reads from 214,095 metagenomic samples from the European Nucleotide Archive (ENA) and aligned them using a uniform approach against ARGs and 16S/18S rRNA genes. Here, we present the results of this extensive computational analysis and share the counts of reads aligned. Over 6.76∙10⁸ read fragments were assigned to ARGs and 3.21∙10⁹ to rRNA genes, where we observed distinct differences in both the abundance of ARGs and the link between microbiome and resistome compositions across various sampling types. This collection is another step towards establishing global surveillance of AMR and can serve as a resource for further research into the environmental spread and dynamic changes of ARGs.

The growing threat of antimicrobial resistance (AMR) calls for new epidemiological surveillance methods and a deeper understanding of how resistance genes are transmitted around the world. This study presents a large-scale remapping of sequencing reads of publicly available metagenomic datasets that can be used to monitor the global prevalence of AMR genes.

Microbiome Analysis for Wastewater Surveillance during COVID-19

Wastewater surveillance (WS), when coupled with advanced molecular techniques, offers near real-time monitoring of community-wide transmission of SARS-CoV-2 and allows assessing and mitigating COVID-19 outbreaks, by evaluating the total microbial assemblage in a community. Composite wastewater samples (24 h) were collected weekly from a manhole between December 2020 and November 2021 in Maryland, USA. RT-qPCR results showed concentrations of SARS-CoV-2 RNA recovered from wastewater samples reflected incidence of COVID-19 cases. When a drastic increase in COVID-19 was detected in February 2021, samples were selected for microbiome analysis (DNA metagenomics, RNA metatranscriptomics, and targeted SARS-CoV-2 sequencing). Targeted SARS-CoV-2 sequencing allowed for detection of important genetic mutations, such as spike: K417N, D614G, P681H, T716I, S982A, and D1118H, commonly associated with increased cell entry and reinfection. Microbiome analysis (DNA and RNA) provided important insight with respect to human health-related factors, including detection of pathogens and their virulence/antibiotic resistance genes. Specific microbial species comprising the wastewater microbiome correlated with incidence of SARS-CoV-2 RNA, suggesting potential association with SARS-CoV-2 infection. Climatic conditions, namely, temperature, were related to incidence of COVID-19 and detection of SARS-CoV-2 in wastewater, having been monitored as part of an environmental risk score assessment carried out in this study. In summary, the wastewater microbiome provides useful public health information, and hence, a valuable tool to proactively detect and characterize pathogenic agents circulating in a community. In effect, metagenomics of wastewater can serve as an early warning system for communicable diseases, by providing a larger source of information for health departments and public officials. IMPORTANCE Traditionally, testing for COVID-19 is done by detecting SARS-CoV-2 in samples collected from nasal swabs and/or saliva. However, SARS-CoV-2 can also be detected in feces of infected individuals. Therefore, wastewater samples can be used to test all individuals of a community contributing to the sewage collection system, i.e., the infrastructure, such as gravity pipes, manholes, tanks, lift stations, control structures, and force mains, that collects used water from residential and commercial sources and conveys the flow to a wastewater treatment plant. Here, we profile community wastewater collected from a manhole, detect presence of SARS-CoV-2, identify genetic mutations of SARS-CoV-2, and perform COVID-19 risk score assessment of the study area. Using metagenomics analysis, we also detect other microorganisms (bacteria, fungi, protists, and viruses) present in the samples. Results show that by analyzing all microorganisms present in wastewater, pathogens circulating in a community can provide an early warning for contagious diseases.

Wastewater Surveillance Detected Carbapenemase Enzymes in Clinically Relevant Gram-Negative Bacteria in Helsinki, Finland; 2011–2012

Antimicrobial resistance profiling of pathogens helps to identify the emergence of rare or new resistance threats and prioritize possible actions to be taken against them. The analysis of wastewater (WW) can reveal the circulation of antimicrobial-resistant bacteria (ARB) and antimicrobial resistance genes (ARG) among the catchment communities. Here, we analyzed WW influent samples to determine the prevalence of carbapenemase genes-carrying Gram-negative bacteria (Carba-GNB) in Helsinki, Finland. This study set important historical reference points from the very early stage of the carbapenemase era, during the period 2011–2012. A total of 405 bacterial isolates grown on CHROMagarKPC (n = 195) and CHROMagarESBL (n = 210) from WW influent samples were collected between October 2011 and August 2012 and were analyzed. The bacterial DNA from the isolates was extracted, and the prevalence of carbapenemases genes blaKPC, blaNDM, blaGES, blaOXA-48, blaIMP, blaIMI, and blaVIM were screened with multiplexed PCR. All carbapenemase-positive isolates were identified taxonomically to species or genus level with matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). The nucleic acid extraction was successful for 399 isolates, of which 59 (14.8%) were found to carry carbapenemase genes. A total of 89.8% of the carbapenemase positive isolates (53 out of 59) were obtained from CHROMagarKPC plates and only 10.2% (six out of 59) were obtained from CHROMagar ESBL plates. Among the Carba-GNB isolates, 86.4% were blaGES (51 out of 59), 10.2% were blaKPC (six out of 59), and 3.4% were blaVIM (two out of 59). The most common carba-gene, blaGES, was carried by 10 different bacterial species, including Aeromonas spp., Enterobacter spp., and Kluyvera spp.; the blaKPC gene was carried by Escherichia coli, Klebsiella pneumoniae, and Kluyvera cryocescens; and the blaVIM gene was carried by Aeromonas hydrophila/caviae and Citrobacter amalonaticus. This study emphasizes that wastewater surveillance (WWS) can be an additional tool for monitoring antimicrobial resistance (AMR) at the population level.

Population-based variations of a core resistome revealed by urban sewage metagenome surveillance

Sewage-based surveillance is widely employed to understand the occurrence and distribution of antimicrobial resistance (AMR) in urban community. However, there are limited studies which investigated the sewage of different sources within community. The present study used metagenomics to decipher the AMR profiles in five sources: local residence's source, animal source, migrant workers' source, clinical source , and urban wastewater treatment plant influent. A core resistome of ARGs was found across all samples, accounting for 81.4%-93.3% of the abundance of total resistome with only 17.3% diversity, irrespective of the sewage sources. Clinically relevant ARGs were identified in the core resistome across all wastewater sources. This included genes conferring resistance to beta-lactams as biomarkers of hospital sewage. The pet center wastewater showed a high abundance of genes encoding resistance to tetracycline, which is a commonly used veterinary antibiotic. The resistome profile of sewage from the migrant workers' dormitories showed a slight variation to that of the local residential population, suggesting possible differences in the human gut resistome of the foreign/migrant population, with biomarkers of genes encoding resistance to fosfomycin, fosmidomycin, kasugamycin, MLS, and polymyxin. The co-localization of ARGs and plasmid, MGEs and integrative and conjugative elements (ICEs) could explain variations in the core resistome, presumably a result of high antibiotic selection pressure. Further analysis showed a specific host-associated resistance pattern, in which core hosts mediated the core resistome profile. The core BMRGs were also co-localized with MGEs/ICEs and carried by core potential bacterial hosts. Local healthy population carried the lowest ARG load (copy number discharged by each person per day) but contributed the highest ARG burden (copy number discharged by the population). This study elucidates population-based variations of a core resistome, and further provides important insights into source tracking and management of AMR in urban environments.

International Travel as a Risk Factor for Carriage of Extended-Spectrum β-Lactamase-Producing Escherichia coli in a Large Sample of European Individuals—The AWARE Study

Antibiotic resistance (AR) is currently a major threat to global health, calling for a One Health approach to be properly understood, monitored, tackled, and managed. Potential risk factors for AR are often studied in specific high-risk populations, but are still poorly understood in the general population. Our aim was to explore, describe, and characterize potential risk factors for carriage of Extended-Spectrum Beta-Lactamase-resistant Escherichia coli (ESBL-EC) in a large sample of European individuals aged between 16 and 67 years recruited from the general population in Southern Germany, the Netherlands, and Romania. Questionnaire and stool sample collection for this cross-sectional study took place from September 2018 to March 2020. Selected cultures of participants’ stool samples were analyzed for detection of ESBL-EC. A total of 1183 participants were included in the analyses: 333 from Germany, 689 from the Netherlands, and 161 from Romania. Travels to Northern Africa (adjusted Odds Ratio, aOR 4.03, 95% Confidence Interval, CI 1.67–9.68), Sub-Saharan Africa (aOR 4.60, 95% CI 1.60–13.26), and Asia (aOR 4.08, 95% CI 1.97–8.43) were identified as independent risk factors for carriage of ESBL-EC. Therefore, travel to these regions should continue to be routinely asked about by clinical practitioners as possible risk factors when considering antibiotic therapy.

Systematic review of wastewater surveillance of antimicrobial resistance in human populations

OBJECTIVES

We systematically reviewed studies using wastewater for AMR surveillance in human populations, to determine: (i) evidence of concordance between wastewater-human AMR prevalence estimates, and (ii) methodological approaches which optimised identifying such an association, and which could be recommended as standard. We used Lin's concordance correlation coefficient (CCC) to quantify concordance between AMR prevalence estimates in wastewater and human compartments (where CCC = 1 reflects perfect concordance), and logistic regression to identify study features (e.g. sampling methods) associated with high agreement studies (defined as >70% of within-study wastewater-human AMR prevalence comparisons within ±10%).

RESULTS

Of 8,867 records and 441 full-text methods reviewed, 33 studies were included. AMR prevalence data was extractable from 24 studies conducting phenotypic-only (n = 7), genotypic-only (n = 1) or combined (n = 16) AMR detection. Overall concordance of wastewater-human AMR prevalence estimates was reasonably high for both phenotypic (CCC = 0.85 [95% CI 0.8-0.89]) and genotypic approaches (CCC = 0.88 (95% CI 0.84-0.9)) despite diverse study designs, bacterial species investigated and phenotypic/genotypic targets. No significant relationships between methodological approaches and high agreement studies were identified using logistic regression; however, this was limited by inconsistent reporting of study features, significant heterogeneity in approaches and limited sample size. Based on a secondary, descriptive synthesis, studies conducting composite sampling of wastewater influent, longitudinal sampling >12 months, and time-/location-matched sampling of wastewater and human compartments generally had higher agreement.

CONCLUSION

Wastewater-based surveillance of AMR appears promising, with high overall concordance between wastewater and human AMR prevalence estimates in studies irrespective of heterogenous approaches. However, our review suggests future work would benefit from: time-/location-matched sampling of wastewater and human populations, composite sampling of influent, and sampling >12 months for longitudinal studies. Further research and clear and consistent reporting of study methods is required to identify optimal practice.

Antibiotic resistance genes of emerging concern in municipal and hospital wastewater from a major Swedish city

The spread of antibiotic resistance among bacterial pathogens is to a large extent mediated by mobile antibiotic resistance genes (ARGs). The prevalence and geographic distribution of several newly discovered ARGs, as well as some clinically important ARGs conferring resistance to last resort antibiotics, are largely unknown. Targeted analysis of wastewater samples could allow estimations of carriage in the population connected to the sewers as well as release to the environment. Here we quantified ARGs conferring resistance to linezolid (optrA and cfr(A)) and colistin (mcr-1, -2, -3, -4 and -5) and the recently discovered gar (aminoglycoside ARG) and sul4 (sulphonamide ARG) in raw hospital and municipal wastewater as well as treated municipal wastewater during five years in a low antibiotic resistance prevalence setting (Gothenburg, Sweden). Additionally, variations in bacterial composition of the wastewaters characterized by 16S rRNA sequencing were related to the variations of the ARGs in an attempt to reveal if the presence of known or suspected bacterial host taxa could explain the presence of the ARGs in wastewater. The mcr-1, mcr-3, mcr-4, mcr-5, sul4 and gar genes were detected regularly in all types of wastewater samples while optrA and cfr(A) were detected only in hospital wastewater. The most abundant genes were mcr-3 and mcr-5, especially in municipal wastewater. The detection of optrA was restricted to a peak during one year. Most of the ARGs correlated with taxa previously described as bacterial hosts and associated with humans. Although some of the tentative hosts may include bacteria also thriving in wastewater environments, detection of the ARGs in the wastewaters could reflect their presence in the gut flora of the contributing populations. If so, they could already today or in the near future hinder treatment of bacterial infections in a setting where they currently are rarely targeted/detected during clinical surveillance.

Monitoring and Early Warning Analysis of the Epidemic Situation of Escherichia coli Based on Big Data Technology and Cloud Computing

The purpose of this study is to analyze the molecular epidemiological characteristics and resistance mechanisms of Escherichia coli. The study established a big data cloud computing prediction model for the epidemic mechanism of the pathogen. The study establishes the early warning, control parameters, and mathematical model of Escherichia coli infectious disease and monitors the molecular sequence of the pathogen based on discrete indicators. A nonlinear mathematical model equation was used to establish the epidemic trend model of Escherichia coli. The study shows that the use of the model can control the relative error at about 5%. The experiment proves the effectiveness of the combined model.

Spatiotemporal dynamics of the resistome and virulome of riverine microbiomes disturbed by a mining mud tsunami

Aquatic ecosystems are highly vulnerable to anthropogenic activities. However, it remains unclear how the microbiome responds to press disturbance events in these ecosystems. We examined the impact of the world's largest mining disaster (Brazil, 2015) on sediment microbiomes in two disturbed rivers compared to an undisturbed river during 390 days post-disturbance. The diversity and structure of the virulome and microbiome, and of antibiotic and metal resistomes, consistently differed between the disturbed and undisturbed rivers, particularly at day 7 post-disturbance. 684 different ARGs were predicted, 38% were exclusive to the disturbed rivers. Critical antibiotic resistance genes (ARGs), e.g., mcr and ereA2, were significantly more common in the disturbed microbiomes. 401 different ARGs were associated with mobile genetic elements (MGEs), 95% occurred in the disturbed rivers. While plasmids were the most common MGEs with a broad spectrum of ARGs, spanning 16 antibiotic classes, integrative conjugative elements (ICEs) and integrons disseminated ARGs associated with aminoglycoside and tetracycline, and aminoglycoside and beta-lactam, respectively. A significant increase in the relative abundance of class 1 integrons, ICEs, and pathogens was identified at day 7 in the disturbed microbiomes, 72-, 14- and 3- fold higher, respectively, compared with the undisturbed river. Mobile ARGs associated with ESKAPEE group pathogens, while metal resistance genes and virulence factor genes in nonpathogenic hosts predominated in all microbiomes. Network analysis showed highly interconnected ARGs in the disturbed communities, including genes targeting antibiotics of last resort. Interactions between copper and beta-lactam/aminoglycoside/macrolide resistance genes, mostly mobile and critical, were also uncovered. We conclude that the mud tsunami resulted in resistome expansion, enrichment of pathogens, and increases in promiscuous and mobile ARGs. From a One Health perspective, mining companies need to move toward more environmentally friendly and socially responsible mining practices to reduce risks associated with pathogens and critical and mobile ARGs.

Antibiotic resistance in the environment

Antibiotic resistance is a global health challenge, involving the transfer of bacteria and genes between humans, animals and the environment. Although multiple barriers restrict the flow of both bacteria and genes, pathogens recurrently acquire new resistance factors from other species, thereby reducing our ability to prevent and treat bacterial infections. Evolutionary events that lead to the emergence of new resistance factors in pathogens are rare and challenging to predict, but may be associated with vast ramifications. Transmission events of already widespread resistant strains are, on the other hand, common, quantifiable and more predictable, but the consequences of each event are limited. Quantifying the pathways and identifying the drivers of and bottlenecks for environmental evolution and transmission of antibiotic resistance are key components to understand and manage the resistance crisis as a whole. In this Review, we present our current understanding of the roles of the environment, including antibiotic pollution, in resistance evolution, in transmission and as a mere reflection of the regional antibiotic resistance situation in the clinic. We provide a perspective on current evidence, describe risk scenarios, discuss methods for surveillance and the assessment of potential drivers, and finally identify some actions to mitigate risks.

Undervalued Pseudo-nifH Sequences in Public Databases Distort Metagenomic Insights into Biological Nitrogen Fixers

Nitrogen fixation, a distinct process incorporating the inactive atmospheric nitrogen into the active biological processes, has been a major topic in biological and geochemical studies. Currently, insights into diversity and distribution of nitrogen-fixing microbes are dependent upon homology-based analyses of nitrogenase genes, especially the nifH gene, which are broadly conserved in nitrogen-fixing microbes. Here, we report the pitfall of using nifH as a marker of microbial nitrogen fixation. We exhaustively analyzed genomes in RefSeq (231,908 genomes) and KEGG (6,509 genomes) and cooccurrence and gene order patterns of nitrogenase genes (including nifH) therein. Up to 20% of nifH-harboring genomes lacked nifD and nifK, which encode essential subunits of nitrogenase, within 10 coding sequences upstream or downstream of nifH or on the same genome. According to a phenotypic database of prokaryotes, no species and strains harboring only nifH possess nitrogen-fixing activities, which shows that these nifH genes are "pseudo"-nifH genes. Pseudo-nifH sequences mainly belong to anaerobic microbes, including members of the class Clostridia and methanogens. We also detected many pseudo-nifH reads from metagenomic sequences of anaerobic environments such as animal guts, wastewater, paddy soils, and sediments. In some samples, pseudo-nifH overwhelmed the number of "true" nifH reads by 50% or 10 times. Because of the high sequence similarity between pseudo- and true-nifH, pronounced amounts of nifH-like reads were not confidently classified. Overall, our results encourage reconsideration of the conventional use of nifH for detecting nitrogen-fixing microbes, while suggesting that nifD or nifK would be a more reliable marker. IMPORTANCE Nitrogen-fixing microbes affect biogeochemical cycling, agricultural productivity, and microbial ecosystems, and their distributions have been investigated intensively using genomic and metagenomic sequencing. Currently, insights into nitrogen fixers in the environment have been acquired by homology searches against nitrogenase genes, particularly the nifH gene, in public databases. Here, we report that public databases include a significant amount of incorrectly annotated nifH sequences (pseudo-nifH). We exhaustively investigated the genomic structures of nifH-harboring genomes and found hundreds of pseudo-nifH sequences in RefSeq and KEGG. Over half of these pseudo-nifH sequences belonged to members of the class Clostridia, which is supposed to be a prominent nitrogen-fixing clade. We also found that the abundance of nitrogen fixers in metagenomes could be overestimated by 1.5 to >10 times due to pseudo-nifH recorded in public databases. Our results encourage reconsideration of the prevalent use of nifH as a marker of nitrogen-fixing microbes.

How Do the Players Play? A Post-Genomic Analysis Paradigm to Understand Aquatic Ecosystem Processes

Culture-independent and meta-omics sequencing methods have shed considerable light on the so-called “microbial dark matter” of Earth’s environmental microbiome, improving our understanding of phylogeny, the tree of life, and the vast functional diversity of microorganisms. This influx of sequence data has led to refined and reimagined hypotheses about the role and importance of microbial biomass, that paradoxically, sequencing approaches alone are unable to effectively test. Post-genomic approaches such as metabolomics are providing more sensitive and insightful data to unravel the fundamental operations and intricacies of microbial communities within aquatic systems. We assert that the implementation of integrated post-genomic approaches, specifically metabolomics and metatranscriptomics, is the new frontier of environmental microbiology and ecology, expanding conventional assessments toward a holistic systems biology understanding. Progressing beyond siloed phylogenetic assessments and cataloging of metabolites, toward integrated analysis of expression (metatranscriptomics) and activity (metabolomics) is the most effective approach to provide true insight into microbial contributions toward local and global ecosystem functions. This data in turn creates opportunity for improved regulatory guidelines, biomarker discovery and better integration of modeling frameworks. To that end, critical aquatic environmental issues related to climate change, such as ocean warming and acidification, contamination mitigation, and macro-organism health have reasonable opportunity of being addressed through such an integrative approach. Lastly, we argue that the “post-genomics” paradigm is well served to proactively address the systemic technical issues experienced throughout the genomics revolution and focus on collaborative assessment of field-wide experimental standards of sampling, bioinformatics and statistical treatments.