Urban monitoring of antimicrobial resistance during a COVID-19 surge through wastewater surveillance

Algae bioremediation of swine and domestic wastewater promotes a reduction of coliforms and antibiotic-resistant bacteria

The microbiological load that wastewater may contain is an important factor to consider in wastewater treatment to avoid water bodies contamination and has taken on great relevance due to the possible presence of antibiotic-resistant bacteria. This study investigates the feasibility of bacteria control by phycoremediation treatment using Scenedesmus sp. in two types of wastewater (domestic and swine wastewater). It was determined the cell growth of microalgae culture, and the reduction of total coliforms and enterobacteria load throughout ten days of experiment. In addition, the removal of antibiotic-resistant bacteria was performed using five different antibiotics commonly used in clinical diagnosis: Ampicillin Tetracycline, Ciprofloxacin, Sulfamethoxazole, and Ceftriaxone. The results shown a significant decrease in total coliforms and enterobacteria in the phycoremediation process, it was removed up to 98 % of total coliforms [ from (8.7 ± 2.31) × 104 to (1.6 ± 0.17) × 103 CFU mL -1] in swine wastewater and 99 % in domestic wastewater [(3.6 ± 0.31) × 105 to (2 ± 0.05) × 103 CFU mL -1]. Significant reduction in the case of sulfamethoxazole-resistant bacteria by microalgae in swine wastewater from [(1.47 ± 0.05) × 105 to (5.3 ± 0.57) × 103 ] and domestic wastewater [(4.9 ± 0.15) × 104 to (2.9 ± 0.36) × 103]. These findings demonstrate the versatility and effectiveness of the phycoremediation system since the general microbial control to most specific of antibiotic-resistant bacteria in wastewater, demonstrating its great potential to reduce the risk of public health issues in urban and rural areas.

Hospital air sampling enables surveillance of respiratory virus infections and genomes

There is an urgent need for early detection and comprehensive surveillance of respiratory pathogens. Environmental surveillance may be key to timely responses for newly emerging pathogens and infections that are unreported or underreported. Here, we employed air sampling in a large urban hospital. Air samples (n = 358) were collected weekly at five locations, including two in the emergency department, two in hospital common areas and one in a storage room, for two respiratory virus seasons (November 2022 to June 2024). Air samples were tested for eight respiratory pathogens by qPCR, including RNA and DNA viruses and a bacterium. Air samples had an average of four detected pathogens per sample and 97 % samples contained SARS-CoV-2. Air sample pathogen positivity and quantity were strongly correlated with clinical surveillance for four seasonal respiratory pathogens: influenza A and B, respiratory syncytial virus, and human metapneumovirus. Targeted amplicon sequencing of SARS-CoV-2 showed that lineages detected in air samples reflected those in contemporaneous regional clinical specimens. Metagenomic sequencing with viral capture enrichment detected myriad human pathogens, including respiratory-associated viruses with recovery of full viral genomes. Detection of viral pathogens correlated well between virus capture sequencing and qPCR. Overall, this suggests air sampling can be an agile and effective tool for pathogen early warning, surveillance and genome characterization.

High-throughput qPCR profiling of antimicrobial resistance genes and bacterial loads in wastewater and receiving environments

Wastewater treatment plants (WWTPs) are hot spots for the acquisition and spread of antimicrobial resistance (AMR). This regional-based study quantified antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), and bacteria in hospital and community-derived wastewater and receiving environments, using high-throughput qPCR (HT-qPCR). This is the first study to apply Resistomap's Antibiotic Resistance Gene Index (ARGI) as a standardised metric to find the overall AMR level across different WWTPs. ARGI of WWTPs ranged from 2.0 to 2.3, indicating higher relative ARG levels than the mean European ARGI of 2.0, but lower than the global mean of 2.4. The highest diversity and abundance of ARGs were observed in untreated hospital and community wastewater. The reduction of total ARGs during wastewater treatment (0.2-2 logs) and bacteria (0.3-1.5 logs) varied spatio-temporally across the WWTPs. Despite a decrease in ARG and bacterial abundance in treated effluents, substantial loads were still released into receiving environments. Notably, ARG levels in coastal sediments were comparable to those in untreated wastewater, and most ARGs were shared between wastewater and receiving environments, highlighting the impact of wastewater discharge on these ecosystems. Sewage outfall exposure increased ARGs in shellfish, emphasising risks to shellfish hygiene. This study provides evidence to inform policymaking, emphasising advanced wastewater treatment methods and combined sewer overflow (CSO) management to mitigate ARG release, protecting water users and the food chain.

Ammonia-oxidizing activity and microbial structure of ammonia-oxidizing bacteria, ammonia-oxidizing archaea and complete ammonia oxidizers in biofilm systems with different salinities

Ammonia-oxidizing activity of different ammonia-oxidizing microorganisms (AOMs), such as ammonia-oxidizing bacteria (AOB), ammonia-oxidizing archaea (AOA), and complete ammonia oxidizers (comammoxs), were investigated by adding the inhibitors such as 2-phenyl-4,4,5,5-tetramethylimidazoline-1-oxyl 3-oxide, octyne, and KCLO3 in biofilm systems with different salinities. It was found that the ammonia-oxidizing activity of all AOMs gradually decreased with increasing salinity. The ammonia-oxidizing activity of AOB was consistently higher than those of AOA and comammox at different salinities. Moreover, nitrite-oxidizing bacteria (NOB) were more sensitive to changes in salinity than AOMs. Metagenomic analysis revealed that nitrifiers were detected at high level, with the AOB Nitrosomonas sp. comprising 24.9 % and the NOB Nitrospira sp. comprising 47.2 % of all nitrifiers. The main functional genes involved in the nitrification reaction were amoABC, hao, and nxrAB. This study demonstrates that higher abundance of functional microorganisms and genes is related to the ammonia-oxidizing activity and ammonia removal contribution rate.

Wastewater surveillance reveals patterns of antibiotic resistance across the United States

Antibiotic resistance is a growing public health threat, with over 2.8 million antibiotic-resistant infections and 35,000 attributable deaths annually in the U.S. This is an underestimate, as it is based on people who seek medical attention. Here, we sought to use wastewater monitoring to assess community-level antibiotic resistance. This study quantifies concentrations of antibiotic resistance genes (ARGs) by digital droplet PCR in wastewater solids obtained from 163 wastewater treatment plants across the United States. We measure 11 ARGs that confer resistance to beta-lactams (CMY, CTX-M, KPC, NDM, mecA, OXA-48, TEM, VIM), colistin (mcr-1), tetracycline (tetW), and vancomycin (vanA). The Northeast and South have higher overall ARG concentrations compared to the West and Midwest. We pair these data with national data sets including antibiotic use, social vulnerability, size of animal agriculture operations, density of healthcare facilities, and presence of airports to investigate potential drivers of resistance. We also generate predictive maps of ARG concentrations for every county in the United States. We show social vulnerability indicators (overcrowding, housing burden, and access to health insurance) and indicators of international travel are associated with increased ARG concentrations in wastewater, while antibiotic usage is only weakly positively correlated. Our results provide a national baseline of ARG concentrations and highlight the complexity of factors driving spread of antibiotic resistance.

Biofilms as potential reservoirs of antimicrobial resistance in vulnerable settings

Antimicrobial resistance is a major global health threat, characterized by the ability of microorganisms to withstand the effects of antimicrobial agents. Biofilms, as unique microbial communities, significantly contribute to this threat. They provide a protective environment for pathogens, facilitate horizontal gene transfer, and create an ideal setting for the persistence and evolution of resistant bacteria. This issue can be particularly important in low-income settings and vulnerable communities, such as formal and informal refugee and migrant camps. These settings usually have limited access to healthcare resources and appropriate treatments, contributing to the selective pressure that promotes the survival and proliferation of resistant bacteria. Thus, biofilms formed in wastewater in these areas can play a critical role in spreading antimicrobial resistance or acting as hidden reservoirs for future outbreaks. While emerging efforts focus on detecting antibiotic resistance genes and planktonic bacteria in wastewater, biofilms may be a source of under-appreciated antimicrobial resistance, creating a significant gap in our understanding of resistance dynamics in wastewater systems. Incorporating biofilm surveillance into wastewater monitoring strategies in vulnerable settings can help develop a more comprehensive understanding of resistance transmission and more effective intervention measures in these settings.

CARPDM: cost-effective antibiotic resistome profiling of metagenomic samples using targeted enrichment

Better interrogation of antimicrobial resistance requires new approaches to detect the associated genes in metagenomic samples. Targeted enrichment is an ideal method for their sequencing and characterization. However, no open-source, up-to-date hybridization probe set targeting antimicrobial resistance genes exists. Here, we describe the Comprehensive Antibiotic Resistance Probe Design Machine (CARPDM), a probe design software package made to run alongside all future Comprehensive Antibiotic Resistance Database releases. To test its efficacy, we have created and tested two separate probe sets: allCARD, which enriches all genes encoded in the Comprehensive Antibiotic Resistance Database's protein homolog models (n = 4,661), and clinicalCARD, which focuses on a clinically relevant subset of resistance genes (n = 323). We demonstrate that allCARD increases the number of reads mapping to resistance genes by up to 594-fold. clinicalCARD performs similarly when clinically relevant genes are present, increasing the number of resistance-gene mapping reads by up to 598-fold. In parallel with this development, we have established a protocol to synthesize any probe set in-house, saving up to 350 dollars per reaction. Together, these probe sets, their associated design program CARPDM, and the protocol for in-house synthesis will democratize metagenomic resistome analyses, allowing researchers access to a cost-effective and efficient means to explore the antibiotic resistome.IMPORTANCEAntimicrobial resistance threatens to undermine all modern medicine and is driven by the spread of antimicrobial resistance genes among pathogens, environments, patients, and animals. DNA sequencing of complex samples, such as wastewater, shows considerable promise for tracking these genes and making risk assessments. However, these methods suffer from high costs and low detection limits, plus a requirement for frequent redesign due to the constantly evolving diversity of resistance genes. Building upon our Comprehensive Antibiotic Resistance Database, our research provides software for on-demand renewal, based on the latest knowledge of resistance gene diversity, of our novel bait-capture hybridization platform that simultaneously reduces cost and increases detection levels for DNA sequencing of complex samples. The significance of our research is in the development of new software tools, reagent synthesis protocols, and hybridization enrichment protocols to provide affordable, high-resolution metagenomics DNA sequencing, which we test using environmental and wastewater samples.

Phenotypic Antibiotic Resistance Patterns of Escherichia coli Isolates from Clinical UTI Samples and Municipal Wastewater in a Grenadian Community

Antimicrobial resistance (AMR) is a growing global health threat. This study investigated antibiotic resistance in E. coli isolates from municipal wastewater (86 isolates) and clinical urinary tract infection (UTI) cases (34 isolates) in a Grenadian community, using data from January 2022 to October 2023. Antibiogram data, assessed per WHO guidelines for Critically Important antimicrobials (CIA), showed the highest resistance levels in both clinical and wastewater samples for ampicillin, followed by amoxicillin/clavulanic acid and nalidixic acid, all classified as Critically Important. Similar resistance was observed for sulfamethoxazole-trimethoprim (highly important) in both groups, with nitrofurantoin showing resistance in the important category. According to the WHO AWaRe classification, ampicillin (ACCESS group) had the highest resistance, while nitrofurantoin had the lowest across all samples. The WATCH group antibiotics, cefuroxime and cefoxitin, showed comparable resistance levels, whereas aztreonam from the RESERVE group (tested only in wastewater) was 100% sensitive. Multiple Antibiotic Resistance (MAR) index analysis revealed that 7% of wastewater and 38.2% of clinical samples had MAR values over 0.2, indicating prior antibiotic exposure in clinical isolates. These parallel patterns in wastewater and clinical samples highlight wastewater monitoring as a valuable tool for AMR surveillance, supporting antibiotic stewardship through ongoing environmental and clinical assessment.

Targeted Enrichment Sequencing Utilizing a Respiratory Pathogen Panel for Genomic Wastewater-Based Viral Epidemiology in Uruguay

Human respiratory and enteric viruses are responsible for substantial morbidity and mortality worldwide. Wastewater-based epidemiology utilizing next-generation sequencing serves as an effective tool for monitoring viral circulation dynamics at the community level. However, these complex environmental samples are often laden with other microorganisms and host genomic material, which can hinder the sensitivity of viral detection. To address this limitation, targeted enrichment sequencing is emerging as a preferred strategy, facilitating the acquisition of a more comprehensive understanding of specific pathogens. In this study, we evaluated the performance of a targeted enrichment sequencing panel for 42 excreted respiratory viruses (including Picornaviridae, Adenoviridae, Coronaviridae, Paramyxoviridae, Orthomyxoviridae, Orthoherpesviridae, Pneumoviridae, and Parvoviridae families), known as the Respiratory Pathogen ID/AMR enrichment panel (RPIP), coupled with Explify bioinformatics analysis in 3 sewage samples from Uruguay. RPIP panel successfully identified sequences from frequently circulating viruses, along with some that had not been documented previously. We identified and characterized various viruses, including human Enterovirus (Coxsackievirus A1 and A19), Influenza A-H1N1, and full-length sequences of SARS-CoV-2. Additionally, several other viral pathogens were detected, such as human Bocavirus, human Parechovirus, Enterovirus A71, and Enterovirus D68; however, for these viruses further analysis was limited due to the small genomic regions or low-read coverage obtained. While the RPIP panel necessitates substantial sequencing depth and may introduce bias towards the more predominant strains present in the samples, this approach suggests its viability as a genomic epidemiological tool for assessing respiratory and enteric viruses in wastewater.

Comparative analysis of qPCR and metagenomics for detecting antimicrobial resistance in wastewater: a case study

OBJECTIVE

The World Health Organization (WHO) has declared antimicrobial resistance (AMR) as one of the top threats to global public health. While AMR surveillance of human clinical isolates is well-established in many countries, the increasing threat of AMR has intensified efforts to detect antibiotic resistance genes (ARGs) accurately and sensitively in environmental samples, wastewater, animals, and food. Using five ARGs and the 16S rRNA gene, we compared quantitative PCR (qPCR) and metagenomic sequencing (MGS), two commonly used methods to uncover the wastewater resistome. We compared both methods by evaluating ARG detection through a municipal wastewater treatment chain.

RESULTS

Our results demonstrate that qPCR was more sensitive than MGS, particularly in diluted samples with low ARG concentrations such as oxidation pond water. However, MGS was potentially more specific and has less risk of off-target binding in concentrated samples such as raw sewage. MGS analysis revealed multiple subtypes of each gene which could not be distinguished by qPCR; these subtypes varied across different sample types. Our findings affect the conclusions that can be drawn when comparing different sample types, particularly in terms of inferring removal rates or origins of genes. We conclude that both methods appear suitable to profile the resistome of wastewater and other environmental samples, depending on the research question and type of sample.

Wastewater surveillance for antibiotic resistance genes during the late 2020 SARS-CoV-2 peak in two different populations

The United States Centers for Disease Control and Prevention reported a rise in resistant infections after the coronavirus disease 2019 (COVID-19) pandemic started. How and if the pandemic contributed to antibiotic resistance in the larger population is not well understood. Wastewater treatment plants are good locations for environmental surveillance because they can sample entire populations. This study aimed to validate methods used for COVID-19 wastewater surveillance for bacterial targets and to understand how rising COVID-19 cases from October 2020 to February 2021 in Portugal (PT) and King County, Washington contributed to antibiotic resistance genes in wastewater. Primary influent wastewater was collected from two treatment plants in King County and five treatment plants in PT, and hospital effluent was collected from three hospitals in PT. Genomic extracts were tested with the quantitative polymerase chain reaction for antibiotic resistance genes conferring resistance against antibiotics under threat. Random-effect models were fit for log-transformed gene abundances to assess temporal trends. All samples collected tested positive for multiple resistance genes. During the sampling period, mecA statistically significantly increased in King County and PT. No statistical evidence exists of correlation between samples collected in the same Portuguese metro area.

Environmental Surveillance of Flood Control Infrastructure Impacted by Unsheltered Individuals Leads to the Detection of SARS-CoV-2 and Novel Mutations in the Spike Gene

In the United States, the growing number of people experiencing homelessness has become a socioeconomic crisis with public health ramifications, recently exacerbated by the COVID-19 pandemic. We hypothesized that the environmental surveillance of flood control infrastructure may be an effective approach to understand the prevalence of infectious disease. From December 2021 through July 2022, we tested for SARS-CoV-2 RNA from two flood control channels known to be impacted by unsheltered individuals residing in upstream tunnels. Using qPCR, we detected SARS-CoV-2 RNA in these environmental water samples when significant COVID-19 outbreaks were occurring in the surrounding community. We also performed whole genome sequencing to identify SARS-CoV-2 lineages. Variant compositions were consistent with those of geographically and temporally matched municipal wastewater samples and clinical specimens. However, we also detected 10 of 22 mutations specific to the Alpha variant in the environmental water samples collected during January 2022-one year after the Alpha infection peak. We also identified mutations in the spike gene that have never been identified in published reports. Our findings demonstrate that environmental surveillance of flood control infrastructure may be an effective tool to understand public health conditions among unsheltered individuals-a vulnerable population that is underrepresented in clinical surveillance data.

Early Detection of Novel SARS-CoV-2 Variants from Urban and Rural Wastewater through Genome Sequencing and Machine Learning

Genome sequencing from wastewater has emerged as an accurate and cost-effective tool for identifying SARS-CoV-2 variants. However, existing methods for analyzing wastewater sequencing data are not designed to detect novel variants that have not been characterized in humans. Here, we present an unsupervised learning approach that clusters co-varying and time-evolving mutation patterns leading to the identification of SARS-CoV-2 variants. To build our model, we sequenced 3,659 wastewater samples collected over a span of more than two years from urban and rural locations in Southern Nevada. We then developed a multivariate independent component analysis (ICA)-based pipeline to transform mutation frequencies into independent sources with co-varying and time-evolving patterns and compared variant predictions to >5,000 SARS-CoV-2 clinical genomes isolated from Nevadans. Using the source patterns as data-driven reference "barcodes", we demonstrated the model's accuracy by successfully detecting the Delta variant in late 2021, Omicron variants in 2022, and emerging recombinant XBB variants in 2023. Our approach revealed the spatial and temporal dynamics of variants in both urban and rural regions; achieved earlier detection of most variants compared to other computational tools; and uncovered unique co-varying mutation patterns not associated with any known variant. The multivariate nature of our pipeline boosts statistical power and can support accurate and early detection of SARS-CoV-2 variants. This feature offers a unique opportunity for novel variant and pathogen detection, even in the absence of clinical testing.

Global insight into the occurrence, treatment technologies and ecological risk of emerging contaminants in sanitary sewers: Effects of the SARS-CoV-2 coronavirus pandemic

The SARS-CoV-2 pandemic caused changes in the consumption of prescribed/non-prescribed drugs and the population's habits, influencing the detection and concentration of emerging contaminants (ECs) in sanitary sewage and harming environmental and health risks. Therefore, the present work sought to discuss current literature data on the effects of the "COVID-19 pandemic factor" on the quality of raw sewage produced over a five-year period (2018-2019: pre-pandemic; 2020-2022: during the pandemic) and biological, physical, chemical and hybrid treatment technologies, influencing factors in the removal of ECs and potential ecological risks (RQs). Seven hundred thirty-one publications correlating sewage and COVID-19 were identified: 184 pre-pandemic and 547 during the pandemic. Eight classes and 37 ECs were detected in sewage between 2018 and 2022, with the "COVID-19 pandemic factor" promoting an increase in estrogens (+31,775 %), antibiotics (+19,544 %), antiepileptics and antipsychotics (+722 %), pesticides (+200 %), analgesics, anti-inflammatories and anticoagulants (+173 %), and stimulant medications (+157 %) in sanitary sewage. Among the treatment systems, aerated reactors integrated into biomembranes removed >90 % of cephalexin, clarithromycin, ibuprofen, estrone, and 17β-estradiol. The absorption, adsorption, and biodegradation mechanisms of planted wetland systems contributed to better cost-benefit in reducing the polluting load of sewage ECs in the COVID-19 pandemic, individually or integrated into the WWTP. The COVID-19 pandemic factor increased the potential ecological risks (RQs) for aquatic organisms by 40 %, with emphasis on clarithromycin and sulfamethoxazole, which changed from negligible risk and low risk to (very) high risk and caffeine with RQ > 2500. Therefore, it is possible to suggest that the COVID-19 pandemic intensified physiological, metabolic, and physical changes to different organisms in aquatic biota by ECs during 2020 and 2022.

Oxygen mediated mobilization and co-occurrence of antibiotic resistance in lab-scale bioreactor using metagenomic binning

Sub-lethal levels of antibiotic stimulate bacteria to generate reactive oxygen species (ROS) that promotes emergence and spread of antibiotic resistance mediated by mobile genetic elements (MGEs). Nevertheless, the influence of dissolved oxygen (DO) levels on mobility of antibiotic resistance genes (ARGs) in response to ROS-induced stress remains elusive. Thus, the study employs metagenomic assembly and binning approaches to decipher mobility potential and co-occurrence frequency of ARGs and MGEs under hyperoxic (5.5-7 mgL- 1), normoxic (2.5-4 mgL- 1), and hypoxic (0.5-1 mgL- 1) conditions in lab-scale bioreactor for 6 months. Among 163 high-quality metagenome-assembled genomes (MAGs) recovered from 13 metagenomes, 42 MAGs harboured multiple ARGs and were assigned to priority pathogen group. Total ARG count increased by 4.3 and 2.5% in hyperoxic and normoxic, but decreased by 0.53% in hypoxic conditions after 150 days. On contrary, MGE count increased by 7.3-1.3% in all the DO levels, with only two ARGs showed positive correlation with MGEs in hypoxic compared to 20 ARGs under hyperoxic conditions. Opportunistic pathogens (Escherichia, Klebsiella, Clostridium, and Proteus) were detected as potential hosts of ARGs wherein co-localisation of critical ARG gene cassette (sul1, dfr1,adeF, and qacC) were identified in class 1 integron/Tn1 family transposons. Thus, enhanced co-occurrence frequency of ARGs with MGEs in pathogens suggested promotion of ARGs mobility under oxidative stress. The study offers valuable insights into ARG dissemination and hosts dynamics that is essential for controlling oxygen-related stress for mitigating MGEs and ARGs in the environment.

Wastewater surveillance for bacterial targets: current challenges and future goals

Wastewater-based epidemiology (WBE) expanded rapidly in response to the COVID-19 pandemic. As the public health emergency has ended, researchers and practitioners are looking to shift the focus of existing wastewater surveillance programs to other targets, including bacteria. Bacterial targets may pose some unique challenges for WBE applications. To explore the current state of the field, the National Science Foundation-funded Research Coordination Network (RCN) on Wastewater Based Epidemiology for SARS-CoV-2 and Emerging Public Health Threats held a workshop in April 2023 to discuss the challenges and needs for wastewater bacterial surveillance. The targets and methods used in existing programs were diverse, with twelve different targets and nine different methods listed. Discussions during the workshop highlighted the challenges in adapting existing programs and identified research gaps in four key areas: choosing new targets, relating bacterial wastewater data to human disease incidence and prevalence, developing methods, and normalizing results. To help with these challenges and research gaps, the authors identified steps the larger community can take to improve bacteria wastewater surveillance. This includes developing data reporting standards and method optimization and validation for bacterial programs. Additionally, more work is needed to understand shedding patterns for potential bacterial targets to better relate wastewater data to human infections. Wastewater surveillance for bacteria can help provide insight into the underlying prevalence in communities, but much work is needed to establish these methods.IMPORTANCEWastewater surveillance was a useful tool to elucidate the burden and spread of SARS-CoV-2 during the pandemic. Public health officials and researchers are interested in expanding these surveillance programs to include bacterial targets, but many questions remain. The NSF-funded Research Coordination Network for Wastewater Surveillance of SARS-CoV-2 and Emerging Public Health Threats held a workshop to identify barriers and research gaps to implementing bacterial wastewater surveillance programs.

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.

Mapping the spread and mobility of antibiotic resistance in wastewater due to COVID-19 surge

Large amounts of antibiotics have been discharged into wastewater during the COVID-19 pandemic due to overuse and misuse of antibiotics to treat patients. Wastewater-based surveillance can be used as an early warning for antibiotic resistance (AR) emergence. The present study analyzed municipal wastewater corresponding to the major pandemic waves (WW1, WW2, and WW3) in India along with hospital wastewater (Ho) taken as a benchmark for AR. Commonly prescribed antibiotics during a pandemic, azithromycin and cefixime residues, were found in the range of 2.1-2.6 μg/L in Ho and WW2. Total residual antibiotic concentration was less in WW2; however, the total antibiotic resistance gene (ARG) count was 1065.6 ppm compared to 85.2 ppm in Ho. Metagenome and RT-qPCR analysis indicated a positive correlation between antibiotics and non-corresponding ARGs (blaOXA, aadA, cat, aph3, and ere), where 7.2-7.5% was carried by plasmid in the bacterial community of WW1 and WW2. Moreover, as the abundance of the dfrA and int1 genes varied most among municipal wastewater, they can be suggested as AR markers for the pandemic. The common pathogens Streptococcus, Escherichia, Shigella, and Aeromonas were putative ARG hosts in metagenome-assembled genomes. The ARG profile and antibiotic levels varied between municipal wastewaters but were fairly similar for WW2 and Ho, suggesting the impact of the pandemic in shaping the resistome pattern. The study provides insights into the resistome dynamic, AR markers, and host-ARG association in wastewater during the COVID-19 surge. Continued surveillance and identification of intervention points for AR beyond the pandemic are essential to curbing the environmental spread of ARGs in the near future.

Wastewater treatment plants: The missing link in global One-Health surveillance and management of antibiotic resistance

INTRODUCTION

As a global public health crisis, antibiotic resistance (AR) should be monitored and managed under the One-Health concept according to the World Health Organization (WHO), considering the interconnection between humans, animals, and the environment. But this approach often remains focused on human health and rarely on the environment and its compartments, especially wastewater as the main AR receptor. Wastewater treatment plants (WWTPs) not only are not designed for reliving AR but also provide appropriate conditions for enhancing AR through different mechanisms.

METHODS

By reviewing the research-based statistics on the inclusion of WWTPs in the One-Health/AR program crisis, this paper highlights the importance of paying attention to these hotspots, at first. Also, the importance and technical roadmap for the application of WWTPs in both surveillance and management of AR were provided. The current position of these facilities was also evaluated using strengths, weaknesses, opportunities, and threats (SWOT) analysis. In the end, the concluding knowledge gaps and research needs for future investigations were presented.

RESULTS

Despite the fact that wastewater matrices are the hotspot for AR dissemination, WWTPs appear under-represented in One-Health/AR literature. So, of the 414434 articles retrieved for One-Health only 1.5% (n = 6321) focused on AR and about 0.04% (n = 158) on WWTPs. The potential of WWTPs inclusion in AR surveillance has been confirmed by several studies, however, when it comes to its inclusion for management of AR, more evidence should be presented, which confirmed by SWOT results.

DISCUSSION

As such, WWTPs simultaneously provide opportunities for AR surveillance as it is assumed that this medium can reflect the reality of the corresponding society, and for managing unexpected crises which could impact the public. Nonetheless, there are still numerous considerations to change WWTPs role from Achilles' heel to Ajax' shield, including strengthening the research-based knowledge and conducting both surveillance and management strategies of AR under One-Health concept (One-Health/AR) in a clear straightforward framework.

COVID-19's environmental impacts: Challenges and implications for the future

Strict measures have curbed the spread of COVID-19, but waste generation and movement limitations have had an unintended impact on the environment over the past 3 years (2020-2022). Many studies have summarized the observed and potential environmental impacts associated with COVID-19, however, only a few have quantified and compared the effects of these unintended environmental impacts; moreover, whether COVID-19 policy stringency had the same effects on the main environmental topic (i.e., CO2 emissions) across the 3 years remains unclear. To answer these questions, we conducted a systematic review of the recent literature and analyzed the main findings. We found that the positive environmental effects of COVID-19 have received more attention than the negative ones (50.6 % versus 35.7 %), especially in emissions reduction (34 % of total literature). Medical waste (14.5 %) received the highest attention among the negative impacts. Although global emission reduction, especially in terms of CO2, has received significant attention, the positive impacts were temporary and only detected in 2020. Strict COVID-19 policies had a more profound and significant effect on CO2 emissions in the aviation sector than in the power and industry sectors. For example, compared with 2019, international aviation related CO2 emissions dropped by 59 %, 49 %, and 25 % in 2020, 2021, and 2022, respectively, while industry related ones dropped by only 3.16 % in 2020. According to our developed evaluation matrix, medical wastes and their associated effects, including the persistent pollution caused by antibiotic resistance genes, heavy metals and microplastics, are the main challenges post the pandemic, especially in China and India, which may counteract the temporary environmental benefits of COVID-19. Overall, the presented results demonstrate methods to quantify the environmental effects of COVID-19 and provide directions for policymakers to develop measures to address the associated environmental issues in the post-COVID-19 world.

Wastewater sequencing reveals community and variant dynamics of the collective human virome

Wastewater is a discarded human by-product, but its analysis may help us understand the health of populations. Epidemiologists first analyzed wastewater to track outbreaks of poliovirus decades ago, but so-called wastewater-based epidemiology was reinvigorated to monitor SARS-CoV-2 levels while bypassing the difficulties and pit falls of individual testing. Current approaches overlook the activity of most human viruses and preclude a deeper understanding of human virome community dynamics. Here, we conduct a comprehensive sequencing-based analysis of 363 longitudinal wastewater samples from ten distinct sites in two major cities. Critical to detection is the use of a viral probe capture set targeting thousands of viral species or variants. Over 450 distinct pathogenic viruses from 28 viral families are observed, most of which have never been detected in such samples. Sequencing reads of established pathogens and emerging viruses correlate to clinical data sets of SARS-CoV-2, influenza virus, and monkeypox viruses, outlining the public health utility of this approach. Viral communities are tightly organized by space and time. Finally, the most abundant human viruses yield sequence variant information consistent with regional spread and evolution. We reveal the viral landscape of human wastewater and its potential to improve our understanding of outbreaks, transmission, and its effects on overall population health.

Identification and genome sequencing of an influenza H3N2 variant in wastewater from elementary schools during a surge of influenza A cases in Las Vegas, Nevada

Real-time surveillance of infectious diseases at schools or in communities is often hampered by delays in reporting due to resource limitations and infrastructure issues. By incorporating quantitative PCR and genome sequencing, wastewater surveillance has been an effective complement to public health surveillance at the community and building-scale for pathogens such as poliovirus, SARS-CoV-2, and even the monkeypox virus. In this study, we asked whether wastewater surveillance programs at elementary schools could be leveraged to detect RNA from influenza viruses shed in wastewater. We monitored for influenza A and B viral RNA in wastewater from six elementary schools from January to May 2022. Quantitative PCR led to the identification of influenza A viral RNA at three schools, which coincided with the lifting of COVID-19 restrictions and a surge in influenza A infections in Las Vegas, Nevada, USA. We performed genome sequencing of wastewater RNA, leading to the identification of a 2021-2022 vaccine-resistant influenza A (H3N2) 3C.2a1b.2a.2 subclade. We next tested wastewater samples from a treatment plant that serviced the elementary schools, but we were unable to detect the presence of influenza A/B RNA. Together, our results demonstrate the utility of near-source wastewater surveillance for the detection of local influenza transmission in schools, which has the potential to be investigated further with paired school-level influenza incidence data.

Wastewater surveillance for public health

Wastewater contains information on pathogen spread, evolution, and outbreak risk.