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

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.