A review of wastewater-based epidemiology for antimicrobial resistance surveillance
0Abstract
Antimicrobial resistance (AMR) is recognized as one of the most serious threats to public health. Unparalleled population growth and accelerated rates of AMR emergence and dissemination have resulted in both novel resistance in pathogenic organisms and the re-appearance of infections that were formerly under control. Consequently, this has led to an increased quantity of infectious diseases. One of the main drivers of antimicrobial overuse is inappropriate prescribing in human and veterinary medicine. The ability to rapidly survey the spread of antimicrobial resistance within human populations is key for its prevention, intervention, and control. However, many constraints are present for current clinical surveillance systems and their capacity to determine AMR dynamics in the microbiome of healthy individuals as well as in clinical pathogens causing infections. Wastewater-based epidemiology (WBE) is an emergent technique that has the capacity to act as a supplementary measure for current infectious disease surveillance systems and as an early warning system for infectious disease outbreaks. The development of disease outbreaks to the community level can be monitored in real time through the analysis of population pooled wastewater. This review provides an introduction to using wastewater-based epidemiology to monitor AMR bacteria, as well as an overview of wastewater-based epidemiology and its components.
Keywords
INTRODUCTION: ANTIMCROBIAL RESISTANCE
Antimicrobial resistance (AMR) is a global, overlooked pandemic[1]. From common infections such as urinary tract infections, sepsis, and sexually transmitted infections, high rates of AMR have been observed worldwide, indicating that the treatments we have are becoming ineffective[2-4]. AMR is a concern because once existing efficacious antimicrobials are exhausted, common infections and medical procedures may become lethal[2,5].
Epidemiological surveillance networks in Europe have documented that AMR bacteria have become much more prevalent during the past decade[6].
According to statistical models, there were a predicted 4.95 million deaths associated with bacterial AMR in 2019, including 1.27 million deaths directly caused by bacterial AMR[1]. The impact of AMR on death tolls, the economy, and the burden on healthcare systems will be catastrophic unless action is taken to mitigate this risk[7,8].
Antimicrobials can be defined as any drug or compound that exhibits antimicrobial activity - such that they impair the growth of microbial life forms (e.g., bacteria, viruses, fungi, protozoa, etc.)[4]. The discovery and use of antimicrobials have decreased the burden of infectious disease and allowed the innovation of complex medical procedures and surgeries in humans and animals.
AMR occurs when a microbe gains the ability to survive in the presence of an antimicrobial compound[4]. Evidence also shows that sub-lethal concentrations of antimicrobials can also favor resistant bacteria, which grow faster than susceptible bacteria at low antimicrobial concentrations[9]. AMR has occurred naturally over time in environmental bacteria exposed to antimicrobials produced by microorganisms, but human use and misuse of antimicrobials have accelerated AMR evolution. This review will focus on AMR bacteria.
Some bacteria are intrinsically resistant to specific antimicrobials, or resistance can be acquired through mutation of specific genes or through horizontal gene transfer (HGT) of mobile genes via transformation, transduction, or conjugation[10,11]. Molecular mechanisms of resistance to antimicrobials usually involve compound metabolism, target site alterations, or reduced cell membrane permeability/increased cell efflux[11,12]. There are many molecular mechanisms that use the aforementioned strategies to resist antimicrobial compounds and they are described in detail elsewhere[10-14]. Research in the discovery of novel resistance mechanisms is ongoing and is beneficial in directing research in the discovery of new antimicrobials[15].
Antimicrobial misuse and overuse in human and animal medicine and crop production are key drivers in the evolution of AMR[7,16,17]. AMR in humans is connected to AMR in animals and the environment because humans can be infected by pathogens found in their microbiomes[18] and resistance genes can also pass between microbe species via HGT[19]. AMR bacteria and genetic determinants are found in humans, food, animals, and the environment and can be transferred freely between these components. The following sections illustrate why AMR is a global concern with impacts on humans, animals, plants, and the environment.
The environment as a reservoir of antimicrobial resistance
Once antimicrobials are consumed by humans or animals, they are excreted into the environment either as parent molecules, metabolites, or a combination of both[20]. These chemicals often end up in wastewater treatment facilities and potentially contaminate groundwater, rivers, lakes, and agricultural land[17,21]. This is of concern because once these chemicals are released into the environment, they have the potential to select for AMR[21-26]. People and animals may be exposed to microbial pathogens and AMR bacteria through recreational activities in contaminated water, drinking contaminated water, eating contaminated foods, or inhaling aerosols[17,21]. Importantly, AMR has been shown to evolve at sub-inhibitory concentration levels of antimicrobials[9,23,27,28]. For instance, it has been shown that environmentally relevant levels of heavy metals can select for antimicrobial resistance[29]. This is of particular concern because locations with even low concentrations of antimicrobial compounds could select for AMR in the environment[9].
The natural environment is a known reservoir of AMR E, which has been found in freshwater lake environments[30], on plastics[31], in sewers[32] and wastewater[33,34], in soil[35], and in groundwater[36]. Furthermore, animals themselves can act as reservoirs of resistance[19,37,38]. Areas where pharmaceutical[33,37,39], agricultural[40], municipal[41], and hospital waste[42] enter the environment and freshwater are of particular concern as they have increased AMR prevalence and provide routes for human exposure and transmission [Figure 1].
Figure 1. The environment as a reservoir for antimicrobial resistance. Created in BioRender.com[138].
In brief, the acquisition of AMR bacteria in the environment is caused by three key mechanisms that may occur in combination[43]: (1) HGT of resistance genes between different bacterial species; (2) genetic mutation and recombination; and (3) selection pressure caused by antibiotics or other substances such as biocides or heavy metals, which may induce or accelerate the rates of (1) and (2)[44].
Many studies have made headway in determining the prevalence of AMR bacteria in the environment[34,43,45-49]. Most notably, a systematic review in 2015 found that AMR bacteria were detected in all (66/66) of the “contamination” sources (wastewater and manure) included in the review[21]. The review also included molecular evidence supporting AMR transmission from wastewater to the environment[21]. This paper illustrated that AMR bacteria are ubiquitous in the environment, and the authors emphasize that measuring the extent of AMR in the environment is important for the innovation of intervention strategies to limit the spread of AMR in the environment[21]. While ubiquitous, not all AMR is of equal concern, as different resistance genes confer resistance to different classes of antibiotics with differing clinical importance. In addition, some resistance genes, mobile genetic elements carrying multiple resistance genes, and AMR clinical pathogens have an “anthropogenic signature”, meaning they have been selected for in humans or animal microbiomes, and therefore pose a more immediate transmission and infection risk (relative to resistance harbored by most environmental bacteria).
The long-term effects of the dissemination of antimicrobials in the environment are still unfolding, and the effect on the natural environment and the emergence of AMR in human and animal pathogens remains unclear. What is evident, however, is that the release of antimicrobials, AMR bacteria and the potential for subsequent evolution of AMR in various microbes can have serious consequences for both human and animal health.
Antimicrobial resistance surveillance
The first step in mitigating the problem of AMR is to examine its risk to human[50] and animal health and understand its drivers before creating public health policy to contain it. Comprehensive integrated AMR surveillance is needed to create evidence-based policy. The development of regional, national, and global collaborative surveillance networks is important in determining the risk AMR poses[51,52]. AMR surveillance can include recording antimicrobial prescribing in humans, infection rates in humans, antimicrobial use in agriculture, antimicrobial compound concentrations in the environment, and AMR microorganisms and/or gene concentrations in the environment, amongst others. Informed by these variables, integrated surveillance programs can advise and inform policy on multiple drivers of AMR across the One Health continuum, including, but not limited to, antimicrobial use in medicine, livestock and crop production, infection control in hospitals, biosecurity on farms, and waste management.
Surveillance of antimicrobials and AMR in the environment is critical for public health authorities to evaluate the risk that AMR poses in different parts of the world, and to distinguish specific local drivers and risk factors. Policies can then be implemented, informed by evidence from surveillance and the latest research findings[51-56]. Theoretically, if certain resistance mechanisms (such as fluoroquinolone resistance) are high in a specific human population determined by wastewater surveillance, for example, public health authorities could warn against using the corresponding antimicrobial and suggest the use of an alternative[51]. Surveillance programs are important for evaluating risk and advising a course of action to mitigate further harm[4].
For example, in Australia, the Antimicrobial Use and Resistance in Australia (AURA) surveillance system retrieves and reports on data from hospitals, aged care facilities, and the community. AURA focuses on human health and uses data from five other national programs to create a report of patterns and trends of AMR across Australia[57]. The AURA program provides insight and suggestions for improving hospital care, aged care, and infection control. Notably, the sources AURA uses are mainly prescription data and not measurements of AMR in the environment or agriculture. Prescription data alone is not wholly reliable - as this does not mean the drugs are being taken or account for inappropriate disposal.
Another example of a surveillance system is the Global Antimicrobial Resistance and Use Surveillance System (GLASS)[58]. GLASS is a system put in place by the World Health Organization (WHO) and is the first international collaborative scheme to standardize AMR surveillance. GLASS implements a standard approach to the collection, analysis, interpretation, and sharing of data by countries and actively supports this by building and monitoring the status of existing and new surveillance systems. GLASS promotes a shift from traditional surveillance systems based solely on laboratory data to a system that encompasses epidemiological, clinical, and population-level data. GLASS has progressively incorporated data from surveillance of AMR in humans, such as the monitoring of resistance genes and the use of antimicrobial medicines, including AMR in the food chain and in the environment.
Comprehensive surveillance data is needed to inform evidence-based policy on AMR that can reduce the burden of AMR. Although many nations provide annual reports on prescription use and monitor resistant bacteria, national surveillance efforts are different across countries such that most incidence and prevalence data cannot be connected to epidemiological data[16,51,59]. Tacconelli and co-authors wrote a concise summary of the importance of surveillance systems across the world and emphasized the need to improve national AMR surveillance systems by including data from food, livestock, and the environment - in order to create a better narrative of the risk AMR imposes[51]. This integrated approach is called the One Health approach, and it has been promoted by organizations such as the WHO in their global action plan for combatting AMR[51,52,59-61].
An example of a surveillance system that has taken steps towards this One Health approach is the Canadian Integrated Program on Antimicrobial Resistance Surveillance (CIPARS). CIPARS is a national surveillance program which is maintained by the Public Health Agency of Canada's Centre for Food-borne, Environmental and Zoonotic Infectious Diseases and National Microbiology Laboratory in association with federal, provincial, and private industry partners. CIPARS collects and analyzes trends in antimicrobial use and AMR, in particular bacteria from humans, animals, and retail meat across Canada. The bacteria under scrutiny are known as enteric bacteria, and they can be passed between animals and humans. The program started by combining data on AMR from animal samples collected in abattoirs with data on AMR from sick animals and humans[59]. The CIPARS system has increased its level of complexity over time by adding collection points along the animal rearing system[49,59,62]. The program then added other types of data, such as AMR in farm samples and in retail meat samples, and data on the use of antimicrobials both in animal production and human health[59]. Another program that uses the One Health approach to surveillance is the National Antimicrobial Resistance Monitoring System for Enteric Bacteria (NARMS) implemented in the United States[63].
The Global Sewage Surveillance Project (GSSP) uses wastewater (sewage) samples from 102 countries to monitor the prevalence of infectious disease and AMR and has published data using samples from around the world[64]. Human disease surveillance is often impeded due to ethical problems with the sensitivity of data from clinical samples and healthy individuals. Wastewater has been suggested as an alternative for population-based surveillance and the anonymous nature of wastewater avoids many ethical concerns[65]. The rapid development in metagenome analyses offers the potential to rapidly detect emerging pathogens and related antimicrobial resistance genes. Since monitoring of pathogens and AMR in wastewater can provide timely information on pathogens of concern, the information can be used to assist policy managers with information on prevention strategies. This is one of the first coordinated global efforts to use wastewater in AMR surveillance, but others have also used wastewater to monitor AMR[34,46,48] on a smaller scale. Another notable program is the National Wastewater Surveillance System program in the United States, which was developed to track the presence of SARS-CoV-2 across the country[66] .
Using more types of data (such as information provided by wastewater analysis and data on antimicrobial usage and AMR in agriculture) in the surveillance of AMR will help create a more informed narrative on the prevalence and magnitude of AMR around the world. Using these data, policy makers can create evidence-based decisions on antimicrobial use and practice. Once work is done on standardizing data collection and reporting globally, data can be generated by an integrated One Health AMR surveillance system[5,51,52,54,59]. However, standardizing data collection is an ambitious goal for AMR surveillance because there are so many kinds of methods used for sample preparation and data collection.
WASTEWATER-BASED EPIDEMIOLOGY: A BRIEF INTRODUCTION
Wastewater-based epidemiology (WBE) is an approach that can be used to give comprehensive health information at a population or community level. WBE has become a growing field of scientific research, as wastewater contains the collective urine and faeces of whole communities and therefore contains a wealth of epidemiological information about chemical exposure, lifestyle, infectious disease, and wellbeing[55,67-69]. This approach can provide a ton of information on spatial and temporal consumption and serves as an intelligence tool for authorities[55,69]. This tool can be used to link exposure to environmental contaminants to health outcomes[69].
It is based on the concept that markers of chemicals and biologicals we consume or are exposed to (such as chemical compounds and biological microorganisms, defined as biomarkers) are excreted into wastewater either in their original or in a modified form (chemical metabolites)[68] [Figure 2]. In a recent review, Choi and colleagues described WBE as the normalization of analyte influent concentration to per capita mass loads using the daily flow and wastewater treatment plant (WWTP) population size[70]. WBE includes the extraction, detection, and analysis of chemical and/or biological markers in wastewater. This was first described by Daughton in 2001[71] and later tested by Zuccato[72]. Methods to improve WBE are continuously improving and evolving.
Figure 2. Wastewater-based epidemiology workflow. Created in BioRender.com[138].
A huge number of global, long-term WBE monitoring initiatives have been created worldwide, such as in Europe[64,73], Australia[68,74-76], and the USA[77]. In the early days of WBE, research was focused on illicit drug consumption, including heroin, cocaine, and amphetamines[72,78,79], but has since diversified to include a large range of other factors such as alcohol[80-82], tobacco[76], SARS-CoV-2 (aetiological agent of
WBE is also used by surveillance systems previously mentioned to monitor AMR, such as the GSSP mentioned previously[88]. Monitoring the presence and prevalence of AMR bacteria and resistance genes in wastewater can provide information about the level of antimicrobial use and the emergence and spread of AMR within a local human population. By analyzing wastewater samples from different sources, such as hospitals, nursing homes, and communities, researchers can identify hotspots for AMR bacteria and genes. This information can then be used to inform public health strategies, including the development of targeted interventions to reduce antimicrobial use and prevent the spread of AMR infections. The role of WBE in policy making for the management of AMR is still emerging[89]. Surveillance systems such as NWSS will play a key role in preventing the spread of AMR by allowing the monitoring of trends and the identification of hotspots for resistance.
WBE can be used to monitor the use of antimicrobial agents in different populations and settings. For example, a study in South Africa found higher per capita antimicrobial usage in informal settlements than in sewerage connected communities[90]. Another study measured spatiotemporal trends in concentrations of antibiotics in Eastern China[91]. WBE can also monitor resistant pathogen distribution in the community. In one study, wastewater testing revealed geospatial-temporal trends of AMR pathogens in Australia[92]. WBE can be used to monitor the use of antimicrobials and AMR pathogens in different populations and settings.
Key considerations for WBE
Appropriate wastewater biomarkers
Biomarkers are compounds in wastewater that can be targets for WBE. Biomarkers can be sorted by their role as biomarkers of exposure, biomarkers of effect, biological biomarkers (e.g., metabolites, hormones), or by the disease they may suggest (e.g., cardiovascular biomarkers, obesity biomarkers)[69]. Examples of potential biomarkers include illicit drugs, alcohol, tobacco, UV filters, caffeine, pesticides, RNA, DNA, and antimicrobials. The choosing of a biomarker is a difficult task, as it needs to satisfy criteria as outlined in previous studies[69].
From a WBE perspective, a usable biomarker must be stable enough to allow for its measurement in WW - laboratory scale sewer reactor experiments can evaluate this[93]. Population biomarkers need to have low variance in the daily excretion. The selection of biomarkers can largely affect the results of the research and should be considered carefully.
A large number of resistance genes and antimicrobials can be potentially used as biomarkers, but not all are of equal importance[70]. Some guidelines for the selection of genes for wastewater-based monitoring were proposed by Lhat et al. and Berendonk et al., who suggested the following: (i) clinically relevant genes posing a risk to human health; (ii) genes found in mobile elements, thus demonstrating potential for transfer; (iii) genes conferring resistance to high consumption antibiotics; (iv) genes conferring resistance to antibiotics that have been in use for a long time (e.g., tetracycline, sulfonamides); and (v) genes conferring resistance to newer, clinically relevant antibiotics such as the extended-spectrum beta-lactams[94,95]. Notably, sulfonamide and tetracycline resistance genes are among the most common resistance genes studied in wastewater, as both sulfonamide and tetracycline antimicrobials have been in use for a long time and cause resistance via multiple mechanisms[70].
Many new methods are currently being developed for the quantification of antimicrobials in wastewater, such as direct injection liquid chromatography-tandem mass spectrometry[96].
Wastewater sample collection
Wastewater samples are usually collected from municipal WWTPs as they serve communities located in specific sewerage catchment areas. Hence, wastewater from said community can be considered as its pooled excreta. Samples typically collected from WWTPs include wastewater influent and effluent as well as biosolids. Wastewater influent, which is collected at the inlet of the WWTP, can be analyzed to determine chemical or biological biomarkers that are excreted or discharged into the wastewater pipeline. This kind of sample can be used to determine community consumption of, or exposure to, a substance. Wastewater effluent samples, on the other hand, are samples that are collected at the exit of the WWTP after the wastewater has been treated. This type of sample is commonly used to estimate removal efficiencies during the treatment process and to monitor chemicals that are being discharged into the environment. Effluent can contain biological contaminants that can cause harm to humans and the environment. Therefore, it is important to analyze the risks associated with inadequate treatment and to understand the consequencesof poor removal efficiencies.
In order to accurately determine the per capita amount of biomarkers from a sample, representative samples are collected over a specified time frame using autosamplers that collect high-frequency flow (preferred) or time-proportional wastewater influent samples[68,97]. Per capita daily consumption of a parent compound in each catchment is calculated using Equation (1)[72]
where Ci is the concentration of a given drug residue, i, measured in wastewater samples, F is the total wastewater volume during the sampling period, P is the number of people in the catchment, Ri is the ratio of molar mass of parent drug to its metabolite, and Ei is the average excretion rate of a drug residue, i[72].
Different methods can be employed to collect wastewater samples, including continuous and discrete sampling modes[97]. The most representative sampling mode is the continuous flow-proportional mode. In this mode, a side stream of wastewater enters that autosampler at a rate proportional to the flow rate of wastewater in the WWTP. Variants of this mode are described in detail in a review[97]. The least representative sampling technique is called the grab sample, as it only represents the amount of analyte present at a single time point[97]. An alternative sampling mode involves the use of passive samplers, which can absorb chemicals over a longer period, often days or weeks.
Once the wastewater is collected, a preservative such as hydrochloric acid may be added in order to minimize microbial degradation before samples are stored for chemical analyses - however, suitable preservation techniques can be biomarker-specific[70,98]. If the sample is being collected for genomic or culturing analysis, then it is mixed with 20% v/v of sterile glycerol and stored at -80 C in order to preserve the microbial community in the sample[46].
Wastewater analysis
Wastewater can be analyzed for AMR using several methods including analytical chemistry and molecular biology techniques[54]. When an appropriate chemical biomarker has been selected, samples can be analyzed to determine analyte concentrations. This often consists of a sample pre-treatment step, including filtration to remove solids in the sample matrix, as well as solid phase extraction (SPE) for clean-up and concentration of target analytes in a sample. Once the sample is “cleaned up”, it can undergo chemical analysis, typically performed using quantitative analysis with liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS).
If a sample is being analyzed using molecular microbiological methods, the sample will be filtered for solids and prepared via an appropriate DNA library preparation technique as described elsewhere[46]. These steps can vary depending on the specific technology being used (e.g., qPCR and/or metagenome sequencing) and the type of sample being analyzed, but some common steps include:
Sample collection and filtration: Depending on the source of the sample, it may need to be collected and filtered to remove any large solids or debris that could interfere with downstream processing.
DNA extraction: The DNA in the sample needs to be extracted and purified so that it can be used for downstream approaches, such as metagenome sequencing. Different extraction methods may be used depending on the type of sample and the sequencing technology being used and are discussed in depth in other studies[99-101]. DNA quality and quantity is verified before progressing to:
Library preparation: The DNA needs to be fragmented and amplified using PCR to create a library of DNA fragments that can be sequenced. Different library preparation methods may be used depending on the sequencing technology being used.
Sequencing: The prepared library is then loaded onto the sequencing instrument, and the DNA fragments are sequenced using the appropriate sequencing chemistry.
After sequencing is complete, the resulting data is typically analyzed using specialized software and bioinformatics tools to identify and classify the different microbial species present in the sample, as well as their relative abundances, and functional capacity (such as relative abundance of AMR genes). Bioinformatics is needed for processing reads prior to assigning taxonomic rank in order to sort small species differences. This information can be used to gain insights into the microbial community structure and function, as well as potential links to human health or environmental processes.
Alternatively, samples may undergo qPCR to amplify specific gene targets of interest, or viable culturable bacteria in samples can be cultured[54].
Limitations and uncertainties of WBE
WBE has its own limitations that scientists and policy advisors need to be mindful of when describing data. Chemicals are not consistently stable under sewer conditions, as they may degrade or transform before entering the WWTP[102,103]. Therefore, sewer stability experiments may be required if data is not available in the literature. Another stability concern would be in-sample degradation, which can happen due to other compounds and microbes in the sample matrix. There are other concerns regarding the sampling mode (as explained above) and the flow measurement of the WWTP[68,104]. Furthermore, the matrix of wastewater can contain PCR inhibitors that can impair the accuracy of molecular-based methods of detecting AMR genes and microbes, particularly for qPCR[55]. In addition, population sizes in a catchment can normally only be estimated[105,106]. To apply Equation 1 (above) and determine the consumption of a compound, excretion data for the biomarker is needed. This information is not always readily available.
Even though WBE has its own set of limitations and uncertainties, technological advancements have improved the field. For example, one study advocates for biosensing techniques as a promising surveillance alternative. Another study showed improvement in qPCR methods for the detection of macrolide and tetracycline resistance[107]. Improvements in epidemiological modelling have also improved the field[108]. Although WBE has its own set of limitations and uncertainties, technological advancements in the field of biosensors[109], qPCR detection methods[107], and epidemiological modelling[108] have improved the field.
A benefit of WBE is that since WW samples are pooled samples from a community, the anonymity of the individual person is largely maintained. However, in some cases, it is necessary to manage the privacy of location data to prevent the stigmatization of certain groups of people. The ethical considerations of WBE for pharmaceuticals and drugs have been discussed elsewhere[65,110,111]. Generally, populations over > 10,000 are large enough to give anonymity and there is little risk in characterizing smaller societal groups[55].
Using metagenomic data comes with the risk that the individual person can be identified using archived wastewater samples due to the fact that metagenomic sequencing sequences all the DNA in the sample. However, it is unlikely that the data would actually be traced back to the individual as researchers are focused on mapping human pathogens in wastewater[112,113]
Wastewater-based epidemiology and antimicrobial resistance
WBE poses a unique and innovative way of monitoring AMR in a community because it would allow for the simultaneous measurement of antimicrobial compound concentrations and AMR microbes in a community. Table 1 highlights key methodologies for detecting AMR and antimicrobials using WBE and references several studies that have detected antimicrobials and AMR organisms in wastewater in different capacities[46,48,64,67,114-116] . The table highlights the use of LC-MS, sequencing technologies, and culture-based methods for detecting AMR and antimicrobials and illustrates the pros and cons of each method. Combining the aforementioned detection methods with WBE methods[74] can also provide useful and unique information on socioeconomic determinants and temporal trends in the use of antimicrobials. Since the usage of some antimicrobials is seasonal, there are potentially compelling opportunities for trends to be established via wastewater analysis. Some studies have shown periodic patterns for several antibiotics, including clarithromycin, erythromycin, and ciprofloxacin, with more use observed in winter[117,118]. In addition, in locations where prescription information is not made available or antimicrobial medications can be bought over the counter easily, WBE can be a way of monitoring antimicrobial use in a community.
Methodology of using WBE to quantify AMR
| Methodology | Description | Pros. | Cons. | Refs. |
| LC-MS | Liquid chromatography coupled with mass spectrometry (LC-MS) to elucidate antimicrobial compound concentrations in wastewater | By combining HPLC and MS, the strengths of both techniques can be used | Initial costs, requires skilled personnel to set up | [139] |
| Sequencing using Illumina | Next-generation sequencing of microbial genetic material in a wastewater sample using the Illumina platform | Technology used widely, lowest cost, wide range of instruments, lowest error rates | Long sequence runs, shortest read lengths, no real-time data access | [140,141] |
| Sequencing using Oxford Nanopore technology | Next-generation sequencing of microbial genetic material in a wastewater sample using the Oxford Nanopore platform | Fast sequencing, longest confirmed reads, small instrument footprint, lowest instrument and consumable cost, real-time data access | Highest error rate of all the platforms | [142] |
| Sequencing using Ion Torrent technology | Next-generation sequencing of microbial genetic material in a wastewater sample using the Ion Torrent platform | Fast run time, comparatively cheap, long reads possible | High error rate, lower overall data output | [143] |
| Sequencing using Pacific Biosciences technology | Next-generation sequencing of microbial genetic material in a wastewater sample using the Pacific Biosciences platform | Fast sequencing runs, long reads, real-time measurement of base incorporation | Largest instrument footprint, lower output per run, higher error rates | [144,145] |
| Metagenomic sequencing | Can be defined as the sequencing of all genomes in a sample | Gathers information on all genomes in a sample, discovery of novel organisms, no a priori data needed | DNA of environmental microorganisms cannot be extracted completely, the sequencing may miss low-abundance microorganisms, there is no “gold standard” for bioinformatic software | [146,147] |
| Sequencing using 16S region of microbial DNA | Sequencing of the microbial 16S rRNA region of genetic material using a sequencing technology | Targets and reads a region of the 16S rRNA gene which is found in all Bacteria and Archaea, relatively cheap, lots of computational pipelines available | Can only identify organisms that have a 16S rRNA gene, multicopy variation of the 16S rRNA gene, 16S rRNA gene variable regions cannot typically resolve species | [148,149] |
| Whole-genome sequencing | Sequencing of the genetic material of a single organism using sequencing technology. Can identify all the genes in a genome including ARGs, and contribute to high-resolution genome assembly and identification of bacterial species/strains | Provides a high-resolution, base-by-base view of the genome, lots of computational pipelines available | The processing involves a few extra steps compared to 16S rRNA sequencing, is more expensive, computationally intense | [46,150] |
| Measuring resistance genes using qPCR | Using quantitative polymerase chain reaction (qPCR) to quantify the quantity of AMR genes | Comparatively cheap (compared to most sequencing technologies), fast method of measuring resistance genes | Need prior sequence data of the specific target gene of interest, needs standard curve analysis, susceptible to impurities present in the sample | [151,152] |
| Using droplet digital PCR | Using droplet digital PCR (ddPCR) to quantify the quantity of AMR genes | Accurate absolute quantification of pathogens, less contamination, no need for standard curves, more resilient to inhibitory substances | Clinical application of ddPCR is still not popular, there are fewer references available | [144,153,154] |
| Culture-based methods | With or without antibiotic or selective media, allows for the identification of specific taxa | Relatively quick, cheap | Lacks resolution (number of taxa studied), ignores "unculturable" bacteria, low sensitivity (compared to molecular methods), works best for bacteria that replicate efficiently in rich media within | [54] |
As mentioned before, WBE has been excellent at monitoring drug usage and has the potential to assist in the surveillance of AMR[46,51,55]. Using and developing a standardized methodology for characterizing AMR in wastewater will be important in quantifying AMR prevalence in communities. Recent literature on detecting antimicrobial residues in wastewater using analytical chemistry[119-121] is similar to how WBE has already been used to detect other drugs (as described in the previous section).
Another potential biological marker in wastewater for AMR is DNA fragments from bacteria. Most recently, wastewater has been used to monitor the prevalence and spread of COVID-19 (SARS-CoV-2 virus) in Australia[83] and around the world[122,123]. Government health authorities such as Queensland Health have been able to use this data to inform public health policy with respect to the pandemic[124]. Next-generation sequencing of wastewater samples can provide much data on the microbial communities in samples, including identification of the wide range of pathogens and resistance genes present. Analysis of the resistance genes present has been shown to provide key information on novel pathogens, as well as re-emerging infectious diseases and AMR[19,88,125]. Several studies have been published recently on the surveillance of AMR using WBE[46,64,126-128].
While standardization of protocols for sequencing remains a challenge, the improvements in technology combined with decreasing sequencing costs have the potential to improve both pathogen and resistance surveillance in wastewater.
WBE for AMR research: addressing gaps in the literature
Most surveillance systems for AMR focus on key pathogens and use passive laboratory reporting[46,51]. Integrating the power of WBE with the clinical and veterinary surveillance of AMR will help create a more informed understanding of the prevalence and diversity of AMR in microbial populations[5,19,51,55,129].
WBE could aid in providing this population-wide information on the prevalence of AMR in human populations. A large range of antimicrobial resistance genes (ARGs) have been studied and analyzed in wastewater, typically through qPCR[34,41,128,130-133]. Only a couple of studies have analyzed relationships between the levels of antimicrobials and the abundance of ARGs in wastewater. There have been some correlations observed between antimicrobials and respective resistance gene levels that have been antimicrobial dependent[48,133,134-136]. For example, Elder et al. observed positive correlations between fluoroquinolones and qnrS quantity between different locations (r = 0.997, P < 0.004)[137]. Further research is needed to elucidate the relationship (if any) between antimicrobial concentrations and AMR in wastewater. These data can be used to inform on the prevalence and risk of AMR in human populations, wastewater, and receiving environments.
CONCLUSIONS
WBE has emerged as a promising tool for the surveillance of AMR in human populations. WWTPs are a major point of entry for antimicrobial agents and resistant bacteria into the environment, making them an ideal site for monitoring AMR introduced to aquatic environments. This review discusses the potential of WBE for AMR surveillance, as well as the challenges and limitations associated with this approach. We highlight the importance of selecting appropriate sampling strategies and analytical methods to ensure the accuracy and reliability of the data.
One of the main advantages of WBE is that it provides a population-wide snapshot and trend analysis of AMR trends, as it captures wastewater from multiple sources, including households, hospitals, and industrial sites. This can help to identify hotspots of AMR and inform targeted interventions. WBE can help identify the changepoints in target concentrations after targeted interventions. This review also discusses the potential of WBE for monitoring the use of antimicrobial agents in different populations and settings. By analyzing the concentration and distribution of specific antimicrobials and their metabolites in wastewater, it is possible to estimate the consumption of these drugs in the population. We would like to highlight the importance of collecting and archiving representative samples now so that we can establish baseline data retrospectively, particularly as the costs of analyses are decreasing and the accuracy and scope of analyses are only improving.
However, there are also several challenges associated with WBE, such as variability in wastewater composition, dilution effects, and the presence of confounding factors such as environmental stressors, co-selective pressures, and developing models for fecal/urine shedding. In addition, the interpretation of WBE data requires a deep understanding of the local context and the factors that may influence AMR trends.
Overall, this review highlights the potential of WBE for AMR surveillance and calls for further research to optimize sampling and analytical methods, develop standardized protocols, and validate the data against clinical and environmental data.
DECLARATIONS
Acknowledgments
The Queensland Alliance for Environmental Health Sciences, The University of Queensland gratefully acknowledges the financial support of the Queensland Department of Health.
Authors’ contributions
Prepared the manuscript: Clarke LM
Conceptualized: Clarke LM, O’Brien JW, Murray AK, Gaze WH, Thomas KV
Availability of data and materials
Not applicable.
Financial support and sponsorship
Clarke LM was supported by the QUEX Institute (University of Exeter, UK and the University of Queensland, Australia). O’Brien JW is the recipient of an NHMRC Emerging Leadership Fellowship (EL1 2009209). The Queensland Alliance for Environmental Health Sciences (QAEHS), Murray AK is supported by a UKRI NERC Industrial Innovation Research Fellowship (NE/R01373X/1). Gaze WH is supported by a NERC Knowledge Exchange Fellowship (NE/V019279/1).
Conflicts of interest
All authors declared that there are no conflicts of interest.
Ethical approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Copyright
© The Author(s) 2024.
REFERENCES
1. Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 2022;399:629-55.
2. O’ Neill J. Tackling drug-resistant infections globally: final report and reccomendations. 2016. Available from: https://amr-review.org/sites/default/files/160518_Final%20paper_with%20cover.pdf [Last accessed on 23 Feb 2024].
3. Prestinaci F, Pezzotti P, Pantosti A. Antimicrobial resistance: a global multifaceted phenomenon. Pathog Glob Health 2015;109:309-18.
4. WHO. Antimicrobial resistance: global report on surveillance. 2014. Available from: https://www.who.int/publications/i/item/9789241564748 [Last accessed on 23 Feb 2024].
5. Goff DA, Kullar R, Goldstein EJC, et al. A global call from five countries to collaborate in antibiotic stewardship: united we succeed, divided we might fail. Lancet Infect Dis 2017;17:e56-63.
6. Antimicrobial resistance in the EU/EEA (EARS-Net) - annual epidemiological report for 2021. Available from: https://www.ecdc.europa.eu/en/publications-data/surveillance-antimicrobial-resistance-europe-2021 [Last accessed on 23 Feb 2024].
7. Allcock S, Young EH, Holmes M, et al. Antimicrobial resistance in human populations: challenges and opportunities. Glob Health Epidemiol Genom 2017;2:e4.
8. O’Neill J. Antimicrobial resistance: tackling a crisis for the health and wealth of nations. 2014. Available from: https://amr-review.org/sites/default/files/AMR%20Review%20Paper%20-%20Tackling%20a%20crisis%20for%20the%20health%20and%20wealth%20of%20nations_1.pdf [Last accessed on 23 Feb 2024].
9. Gullberg E, Cao S, Berg OG, et al. Selection of resistant bacteria at very low antibiotic concentrations. PLoS Pathog 2011;7:e1002158.
10. Christaki E, Marcou M, Tofarides A. Antimicrobial resistance in bacteria: mechanisms, evolution, and persistence. J Mol Evol 2020;88:26-40.
11. Bleuven C, Landry CR. Molecular and cellular bases of adaptation to a changing environment in microorganisms. Proc Biol Sci 2016;283:20161458.
13. Blair JM, Webber MA, Baylay AJ, Ogbolu DO, Piddock LJ. Molecular mechanisms of antibiotic resistance. Nat Rev Microbiol 2015;13:42-51.
14. Savageau MA. Escherichia coli habitats, cell types, and molecular mechanisms of gene control. Am Nat 1983;122:732-44.
15. Laxminarayan R, Duse A, Wattal C, et al. Antibiotic resistance-the need for global solutions. Lancet Infect Dis 2013;13:1057-98.
16. Larsson DGJ, Andremont A, Bengtsson-Palme J, et al. Critical knowledge gaps and research needs related to the environmental dimensions of antibiotic resistance. Environ Int 2018;117:132-8.
17. Singer AC, Shaw H, Rhodes V, Hart A. Review of antimicrobial resistance in the environment and its relevance to environmental regulators. Front Microbiol 2016;7:1728.
18. Hiltunen T, Virta M, Laine AL. Antibiotic resistance in the wild: an eco-evolutionary perspective. Philos Trans R Soc Lond B Biol Sci 2017;372:20160039.
19. Woolhouse M, Ward M, van Bunnik B, Farrar J. Antimicrobial resistance in humans, livestock and the wider environment. Philos Trans R Soc Lond B Biol Sci 2015;370:20140083.
20. von Wintersdorff CJH, Penders J, van Niekerk JM, et al. Dissemination of antimicrobial resistance in microbial ecosystems through horizontal gene transfer. Front Microbiol 2016;7:173.
21. Huijbers PM, Blaak H, de Jong MC, Graat EA, Vandenbroucke-Grauls CM, de Roda Husman AM. Role of the environment in the transmission of antimicrobial resistance to humans: a review. Environ Sci Technol 2015;49:11993-2004.
22. Ben W, Wang J, Cao R, Yang M, Zhang Y, Qiang Z. Distribution of antibiotic resistance in the effluents of ten municipal wastewater treatment plants in China and the effect of treatment processes. Chemosphere 2017;172:392-8.
23. Stanton IC, Murray AK, Zhang L, Snape J, Gaze WH. Evolution of antibiotic resistance at low antibiotic concentrations including selection below the minimal selective concentration. Commun Biol 2020;3:467.
24. Kraemer SA, Ramachandran A, Perron GG. Antibiotic pollution in the environment: from microbial ecology to public policy. Microorganisms 2019;7:180.
25. Leonard AF, Zhang L, Balfour AJ, Garside R, Gaze WH. Human recreational exposure to antibiotic resistant bacteria in coastal bathing waters. Environ Int 2015;82:92-100.
26. O’Neill J. Antimicrobials in agriculture and the environment: reducing unnecessary use and waste. 2015. Available from: https://amr-review.org/sites/default/files/Antimicrobials%20in%20agriculture%20and%20the%20environment%20-%20Reducing%20unnecessary%20use%20and%20waste.pdf [Last accessed on 23 Feb 2024].
27. Murray AK, Zhang L, Yin X, et al. Novel insights into selection for antibiotic resistance in complex microbial communities. mBio 2018;9:e00969-18.
28. Westhoff S, van Leeuwe TM, Qachach O, Zhang Z, van Wezel GP, Rozen DE. The evolution of no-cost resistance at sub-MIC concentrations of streptomycin in Streptomyces coelicolor. ISME J 2017;11:1168-78.
29. Zhang Y, Gu AZ, Cen T, et al. Sub-inhibitory concentrations of heavy metals facilitate the horizontal transfer of plasmid-mediated antibiotic resistance genes in water environment. Environ Pollut 2018;237:74-82.
30. Kraemer SA, Barbosa da Costa N, Oliva A, Huot Y, Walsh DA. A resistome survey across hundreds of freshwater bacterial communities reveals the impacts of veterinary and human antibiotics use. Front Microbiol 2022;13:995418.
31. Yang Y, Liu G, Song W, et al. Plastics in the marine environment are reservoirs for antibiotic and metal resistance genes. Environ Int 2019;123:79-86.
32. Auguet O, Pijuan M, Borrego CM, et al. Sewers as potential reservoirs of antibiotic resistance. Sci Total Environ 2017;605-6:1047-54.
33. Carraro E, Bonetta S, Bertino C, Lorenzi E, Bonetta S, Gilli G. Hospital effluents management: chemical, physical, microbiological risks and legislation in different countries. J Environ Manag 2016;168:185-99.
34. Fouz N, Pangesti KNA, Yasir M, et al. The contribution of wastewater to the transmission of antimicrobial resistance in the environment: implications of mass gathering settings. Trop Med Infect Dis 2020;5:33.
35. Forsberg KJ, Reyes A, Wang B, Selleck EM, Sommer MO, Dantas G. The shared antibiotic resistome of soil bacteria and human pathogens. Science 2012;337:1107-11.
36. Andrade L, Kelly M, Hynds P, Weatherill J, Majury A, O’Dwyer J. Groundwater resources as a global reservoir for antimicrobial-resistant bacteria. Water Res 2020;170:115360.
37. Ramirez AJ, Brain RA, Usenko S, et al. Occurrence of pharmaceuticals and personal care products in fish: results of a national pilot study in the United States. Environ Toxicol Chem 2009;28:2587-97.
38. Rovira P, McAllister T, Lakin SM, et al. Characterization of the microbial resistome in conventional and “raised without antibiotics” beef and dairy production systems. Front Microbiol 2019;10:1980.
39. Doron A, Broom A. The spectre of superbugs: waste, structural violence and antimicrobial resistance in India. Worldwide Waste 2019;2:7.
40. Call DR, Matthews L, Subbiah M, Liu J. Do antibiotic residues in soils play a role in amplification and transmission of antibiotic resistant bacteria in cattle populations? Front Microbiol 2013;4:193.
41. Amos GC, Hawkey PM, Gaze WH, Wellington EM. Waste water effluent contributes to the dissemination of CTX-M-15 in the natural environment. J Antimicrob Chemother 2014;69:1785-91.
42. Andy IE, Okpo EA. Occurrence and antibiogram of bacteria isolated from effluent and waste dump site soil of selected hospitals in Calabar Metropolis, Nigeria. Microbiol Res J Int 2018;25:1-9.
43. Westphal-Settele K, Konradi S, Balzer F, Schönfeld J, Schmithausen R. Die umwelt als reservoir für antibiotikaresistenzen. Ein wachsendes problem für die öffentliche gesundheit? The environment as a reservoir for antimicrobial resistance: a growing problem for public health? Bundesgesundheitsbla 2018;61:533-42.
44. Martinez JL. Environmental pollution by antibiotics and by antibiotic resistance determinants. Environ Pollut 2009;157:2893-902.
45. Berendsen BJ, Wegh RS, Memelink J, Zuidema T, Stolker LA. The analysis of animal faeces as a tool to monitor antibiotic usage. Talanta 2015;132:258-68.
46. Hendriksen RS, Munk P, Njage P, et al. Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage. Nat Commun 2019;10:1124.
47. Meyer E, Gastmeier P, Deja M, Schwab F. Antibiotic consumption and resistance: data from Europe and Germany. Int J Med Microbiol 2013;303:388-95.
48. Novo A, André S, Viana P, Nunes OC, Manaia CM. Antibiotic resistance, antimicrobial residues and bacterial community composition in urban wastewater. Water Res 2013;47:1875-87.
49. Health Agency of Canada; in participation with the Canadian Food Inspection Agency; Canadian Institutes of Health Research; Health Canada; Agriculture and Agri-Food Canada. Antimicrobial resistance and use in Canada: a federal framework for action. Can Commun Dis Rep 2014;40:S-2.
50. Larsson DGJ, Gaze WH, Laxminarayan R, Topp E. AMR, one health and the environment. Nat Microbiol 2023;8:754-5.
51. Tacconelli E, Sifakis F, Harbarth S, et al. EPI-Net COMBACTE-MAGNET Group. Surveillance for control of antimicrobial resistance. Lancet Infect Dis 2018;18:e99-106.
52. World Health Organization. Global action plan on antimicrobial resistance. 2016; pp. 1-28. Available from: https://www.who.int/publications/i/item/9789241509763 [Last accessed on 23 Feb 2024].
53. Grundmann H, Klugman KP, Walsh T, et al. A framework for global surveillance of antibiotic resistance. Drug Resist Updat 2011;14:79-87.
54. Gaze WH, Leonard A. Stanton I. et al. Towards developing an international environmental AMR surveillance strategy. 2022. Available from: https://www.researchgate.net/publication/363647959_Towards_developing_an_international_environmental_AMR_surveillance_strategy [Last accessed on 23 Feb 2024].
55. Sims N, Kasprzyk-Hordern B. Future perspectives of wastewater-based epidemiology: monitoring infectious disease spread and resistance to the community level. Environ Int 2020;139:105689.
56. Seale AC, Hutchison C, Fernandes S, et al. Supporting surveillance capacity for antimicrobial resistance: laboratory capacity strengthening for drug resistant infections in low and middle income countries. Wellcome Open Res 2017;2:91.
57. AURA 2019: third Australian report on antimicrobial use and resistance in human health. Available from: https://www.safetyandquality.gov.au/aura-2019 [Last accessed on 23 Feb 2024].
58. World Health Organization. Global antimicrobial resistance and use surveillance system (GLASS) report: 2022. Available from: https://www.who.int/publications/i/item/9789240062702 [Last accessed on 23 Feb 2024].
59. Aenishaenslin C, Häsler B, Ravel A, Parmley J, Stärk K, Buckeridge D. Evidence needed for antimicrobial resistance surveillance systems. Bull World Health Organ 2019;97:283-9.
60. Durso LM, Cook KL. One health and antibiotic resistance in agroecosystems. Ecohealth 2019;16:414-9.
61. Webb HE, Angulo FJ, Granier SA, Scott HM, Loneragan GH. Illustrative examples of probable transfer of resistance determinants from food animals to humans: streptothricins, glycopeptides, and colistin. F1000Res 2017;6:1805.
62. Canada PHAo. Canadian integrated program for antimicrobial resistance surveillance (CIPARS). Annual report 2008. Available from: https://www.phac-aspc.gc.ca/cipars-picra/2008/pdf/cipars-picra-2008-eng.pdf [Last accessed on 23 Feb 2024].
63. Centers for Disease Control and Prevention. National antimicrobial resistance monitoring system for enteric bacteria (NARMS). 2023. Available from: https://www.cdc.gov/narms/index.html [Last accessed on 23 Feb 2024].
64. Ahrenfeldt J, Waisi M, Loft IC, et al. Metaphylogenetic analysis of global sewage reveals that bacterial strains associated with human disease show less degree of geographic clustering. Sci Rep 2020;10:3033.
65. Hall W, Prichard J, Kirkbride P, et al. An analysis of ethical issues in using wastewater analysis to monitor illicit drug use. Addiction 2012;107:1767-73.
66. Centre for Disease Control and Prevention. National wastewater surveillance system. 2023. Available from: https://www.cdc.gov/nwss/wastewater-surveillance.html [Last accessed on 23 Feb 2024].
67. Castrignanò E, Yang Z, Feil EJ, et al. Enantiomeric profiling of quinolones and quinolones resistance gene qnrS in European wastewaters. Water Res 2020;175:115653.
68. O’Brien JW, Grant S, Banks APW, et al. A National wastewater monitoring program for a better understanding of public health: a case study using the Australian census. Environ Int 2019;122:400-11.
69. Gracia-Lor E, Rousis NI, Hernández F, Zuccato E, Castiglioni S. Wastewater-based epidemiology as a novel biomonitoring tool to evaluate human exposure to pollutants. Environ Sci Technol 2018;52:10224-6.
70. Choi PM, Tscharke BJ, Donner E, et al. Wastewater-based epidemiology biomarkers: past, present and future. TrAC Trends Anal Chem 2018;105:453-69.
71. Daughton CG. Illicit drugs in municipal sewage: proposed new nonintrusive tool to heighten public awareness of societal use of illicit-abused drugs and their potential for ecological consequences. ACS Symp Ser 2001;791:348-64.
72. Zuccato E, Chiabrando C, Castiglioni S, Bagnati R, Fanelli R. Estimating community drug abuse by wastewater analysis. Environ Health Perspect 2008;116:1027-32.
73. Thomas KV, Bijlsma L, Castiglioni S, et al. Comparing illicit drug use in 19 European cities through sewage analysis. Sci Total Environ 2012;432:432-9.
74. Choi PM, Tscharke B, Samanipour S, et al. Social, demographic, and economic correlates of food and chemical consumption measured by wastewater-based epidemiology. Proc Natl Acad Sci USA 2019;116:21864-73.
75. Lai FY, Gartner C, Hall W, et al. Measuring spatial and temporal trends of nicotine and alcohol consumption in Australia using wastewater-based epidemiology. Addiction 2018;113:1127-36.
76. Tscharke BJ, White JM, Gerber JP. Estimates of tobacco use by wastewater analysis of anabasine and anatabine. Drug Test Anal 2016;8:702-7.
77. Halden R, Terlinden E, Kraberger S, Scotch M, Steele J, Varsani A. Tracking harmful chemicals and pathogens using the human health observatory at ASU. 2019. Available from: https://ojphi.org/ojs/index.php/ojphi/article/view/9843 [Last accessed on 23 Feb 2024].
78. Boleda MR, Galceran MT, Ventura F. Trace determination of cannabinoids and opiates in wastewater and surface waters by ultra-performance liquid chromatography-tandem mass spectrometry. J Chromatogr A 2007;1175:38-48.
79. Castiglioni S, Zuccato E, Crisci E, Chiabrando C, Fanelli R, Bagnati R. Identification and measurement of illicit drugs and their metabolites in urban wastewater by liquid chromatography-tandem mass spectrometry. Anal Chem 2006;78:8421-9.
80. Boogaerts T, Covaci A, Kinyua J, Neels H, van Nuijs AL. Spatial and temporal trends in alcohol consumption in Belgian cities: a wastewater-based approach. Drug Alcohol Depend 2016;160:170-6.
81. Reid MJ, Langford KH, Mørland J, Thomas KV. Analysis and interpretation of specific ethanol metabolites, ethyl sulfate, and ethyl glucuronide in sewage effluent for the quantitative measurement of regional alcohol consumption. Alcohol Clin Exp Res 2011;35:1593-9.
82. Rodríguez-Álvarez T, Rodil R, Rico M, Cela R, Quintana JB. Assessment of local tobacco consumption by liquid chromatography-tandem mass spectrometry sewage analysis of nicotine and its metabolites, cotinine and trans-3’-hydroxycotinine, after enzymatic deconjugation. Anal Chem 2014;86:10274-81.
83. Ahmed W, Tscharke B, Bertsch PM, et al. SARS-CoV-2 RNA monitoring in wastewater as a potential early warning system for COVID-19 transmission in the community: a temporal case study. Sci Total Environ 2021;761:144216.
84. Bade R, White JM, Nguyen L, et al. Determining changes in new psychoactive substance use in Australia by wastewater analysis. Sci Total Environ 2020;731:139209.
85. Reid MJ, Derry L, Thomas KV. Analysis of new classes of recreational drugs in sewage: synthetic cannabinoids and amphetamine-like substances. Drug Test Anal 2014;6:72-9.
86. Daughton CG. Monitoring wastewater for assessing community health: sewage chemical-information mining (SCIM). Sci Total Environ 2018;619-20:748-64.
87. O’Brien JW, Tscharke BJ, Bade R, et al. A wastewater-based assessment of the impact of a minimum unit price (MUP) on population alcohol consumption in the Northern Territory, Australia. Addiction 2022;117:243-9.
88. Aarestrup FM, Woolhouse MEJ. Using sewage for surveillance of antimicrobial resistance. Science 2020;367:630-2.
89. Robins K, Leonard AFC, Farkas K, et al. Research needs for optimising wastewater-based epidemiology monitoring for public health protection. J Water Health 2022;20:1284-313.
90. Holton E, Louw C, Archer E, Louw T, Wolfaardt G, Kasprzyk-Hordern B. Quantifying community-wide antibiotic usage via urban water fingerprinting: focus on contrasting resource settings in South Africa. Water Res 2023;240:120110.
91. Xu L, Zang J, Cong W, et al. Assessment of community-wide antimicrobials usage in Eastern China using wastewater-based epidemiology. Water Res 2022;222:118942.
92. Rahman Z, Liu W, Stapleton L, et al. Wastewater-based monitoring reveals geospatial-temporal trends for antibiotic-resistant pathogens in a large urban community. Environ Pollut 2023;325:121403.
93. Thai PK, O’Brien JW, Banks APW, et al. Evaluating the in-sewer stability of three potential population biomarkers for application in wastewater-based epidemiology. Sci Total Environ 2019;671:248-53.
94. Berendonk TU, Manaia CM, Merlin C, et al. Tackling antibiotic resistance: the environmental framework. Nat Rev Microbiol 2015;13:310-7.
95. Laht M, Karkman A, Voolaid V, et al. Abundances of tetracycline, sulphonamide and beta-lactam antibiotic resistance genes in conventional wastewater treatment plants (WWTPs) with different waste load. PLoS One 2014;9:e103705.
96. Li J, Shimko KM, He C, et al. Direct injection liquid chromatography-tandem mass spectrometry as a sensitive and high-throughput method for the quantitative surveillance of antimicrobials in wastewater. Sci Total Environ 2023;900:165825.
97. Ort C, Lawrence MG, Rieckermann J, Joss A. Sampling for pharmaceuticals and personal care products (PPCPs) and illicit drugs in wastewater systems: are your conclusions valid? A critical review. Environ Sci Technol 2010;44:6024-35.
98. Lin X, Choi PM, Thompson J, et al. Systematic evaluation of the in-sample stability of selected pharmaceuticals, illicit drugs, and their metabolites in wastewater. Environ Sci Technol 2021;55:7418-29.
99. Schiebelhut LM, Abboud SS, Gómez Daglio LE, Swift HF, Dawson MN. A comparison of DNA extraction methods for high-throughput DNA analyses. Mol Ecol Resour 2017;17:721-9.
100. Tanase AM, Mereuta I, Chiciudean I, et al. Comparison of total DNA extraction methods for microbial community form polluted soil. Agric Agric Sci Procedia 2015;6:616-22.
101. Zielińska S, Radkowski P, Blendowska A, Ludwig-Gałęzowska A, Łoś JM, Łoś M. The choice of the DNA extraction method may influence the outcome of the soil microbial community structure analysis. Microbiologyopen 2017;6:e00453.
102. Berendsen BJ, Elbers IJ, Stolker AA. Determination of the stability of antibiotics in matrix and reference solutions using a straightforward procedure applying mass spectrometric detection. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2011;28:1657-66.
103. McCall AK, Bade R, Kinyua J, et al. Critical review on the stability of illicit drugs in sewers and wastewater samples. Water Res 2016;88:933-47.
104. Yang Z, Castrignanò E, Estrela P, Frost CG, Kasprzyk-Hordern B. Community sewage sensors towards evaluation of drug use trends: detection of cocaine in wastewater with DNA-directed immobilization aptamer sensors. Sci Rep 2016;6:21024.
105. Tscharke BJ, O’Brien JW, Ort C, et al. Harnessing the power of the census: characterizing wastewater treatment plant catchment populations for wastewater-based epidemiology. Environ Sci Technol 2019;53:10303-11.
106. O’Brien JW, Thai PK, Eaglesham G, et al. A model to estimate the population contributing to the wastewater using samples collected on census day. Environ Sci Technol 2014;48:517-25.
107. Dutta E, Loy JD, Deal CA, et al. Development of a multiplex real-time PCR assay for predicting macrolide and tetracycline resistance associated with bacterial pathogens of bovine respiratory disease. Pathogens 2021;10:64.
108. Ciannella S, González-Fernández C, Gomez-Pastora J. Recent progress on wastewater-based epidemiology for COVID-19 surveillance: a systematic review of analytical procedures and epidemiological modeling. Sci Total Environ 2023;878:162953.
109. Jiménez-Rodríguez MG, Silva-Lance F, Parra-Arroyo L, et al. Biosensors for the detection of disease outbreaks through wastewater-based epidemiology. Trends Anal Chem 2022;155:116585.
110. Bauer S. Societal and ethical issues in human biomonitoring-a view from science studies. Environ Health 2008;7:S10.
111. Prichard J, Hall W, Zuccato E, et al. Ethical research guidelines for wastewater-based epidemiology and related fields. 2015. Available from: https://qaehs.centre.uq.edu.au/files/880/WBE%20Ethical%20Guidelines.pdf [Last accessed on 23 Feb 2024].
112. Carneiro J, Pascoal F, Semedo M, et al. Mapping human pathogens in wastewater using a metatranscriptomic approach. Environ Res 2023;231:116040.
113. Schaeffer J, Desdouits M, Besnard A, Le Guyader FS. Looking into sewage: how far can metagenomics help to detect human enteric viruses? Front Microbiol 2023;14:1161674.
114. da Silva M, Vaz-Moreira I, Gonzalez-Pajuelo M, Nunes OC, Manaia CM. Antimicrobial resistance patterns in Enterobacteriaceae isolated from an urban wastewater treatment plant. FEMS Microbiol Ecol 2007;60:166-76.
115. Savin M, Bierbaum G, Hammerl JA, et al. Antibiotic-resistant bacteria and antimicrobial residues in wastewater and process water from German pig slaughterhouses and their receiving municipal wastewater treatment plants. Sci Total Environ 2020;727:138788.
116. Varela AR, André S, Nunes OC, Manaia CM. Insights into the relationship between antimicrobial residues and bacterial populations in a hospital-urban wastewater treatment plant system. Water Res 2014;54:327-36.
117. Coutu S, Wyrsch V, Wynn HK, Rossi L, Barry DA. Temporal dynamics of antibiotics in wastewater treatment plant influent. Sci Total Environ 2013;458-60:20-6.
118. Golovko O, Kumar V, Fedorova G, Randak T, Grabic R. Seasonal changes in antibiotics, antidepressants/psychiatric drugs, antihistamines and lipid regulators in a wastewater treatment plant. Chemosphere 2014;111:418-26.
119. Ekwanzala MD, Lehutso RF, Kasonga TK, Dewar JB, Momba MNB. Environmental dissemination of selected antibiotics from hospital wastewater to the aquatic environment. Antibiotics 2020;9:431.
120. Li Y, Taggart MA, McKenzie C, et al. A SPE-HPLC-MS/MS method for the simultaneous determination of prioritised pharmaceuticals and EDCs with high environmental risk potential in freshwater. J Environ Sci 2021;100:18-27.
121. Serra-Compte A, Pikkemaat MG, Elferink A, et al. Combining an effect-based methodology with chemical analysis for antibiotics determination in wastewater and receiving freshwater and marine environment. Environ Pollut 2021;271:116313.
122. Keshaviah A, Hu XC, Henry M. Developing a flexible national wastewater surveillance system for COVID-19 and beyond. Environ Health Perspect 2021;129:45002.
123. Nourbakhsh S, Fazil A, Li M, et al. A wastewater-based epidemic model for SARS-CoV-2 with application to three Canadian cities. Epidemics 2022;39:100560.
124. Field E, Dyda A, Hewett M, et al. Development of the COVID-19 real-time information system for preparedness and epidemic response (CRISPER), Australia. Front Public Health 2021;9:753493.
125. Fernandez-Cassi X, Timoneda N, Martínez-Puchol S, et al. Metagenomics for the study of viruses in urban sewage as a tool for public health surveillance. Sci Total Environ 2018;618:870-80.
126. Petrovich ML, Zilberman A, Kaplan A, et al. Microbial and viral communities and their antibiotic resistance genes throughout a hospital wastewater treatment system. Front Microbiol 2020;11:153.
127. Yasir M. Analysis of microbial communities and pathogen detection in domestic sewage using metagenomic sequencing. Diversity 2021;13:6.
128. Yoo K, Yoo H, Lee J, Choi EJ, Park J. Exploring the antibiotic resistome in activated sludge and anaerobic digestion sludge in an urban wastewater treatment plant via metagenomic analysis. J Microbiol 2020;58:123-30.
130. Alekshun MN, Levy SB. Molecular mechanisms of antibacterial multidrug resistance. Cell 2007;128:1037-50.
131. Mao D, Yu S, Rysz M, et al. Prevalence and proliferation of antibiotic resistance genes in two municipal wastewater treatment plants. Water Res 2015;85:458-66.
132. Rodriguez-Mozaz S, Chamorro S, Marti E, et al. Occurrence of antibiotics and antibiotic resistance genes in hospital and urban wastewaters and their impact on the receiving river. Water Res 2015;69:234-42.
133. Sun Y, Shen YX, Liang P, Zhou J, Yang Y, Huang X. Multiple antibiotic resistance genes distribution in ten large-scale membrane bioreactors for municipal wastewater treatment. Bioresour Technol 2016;222:100-6.
134. Gao P, Munir M, Xagoraraki I. Correlation of tetracycline and sulfonamide antibiotics with corresponding resistance genes and resistant bacteria in a conventional municipal wastewater treatment plant. Sci Total Environ 2012;421-2:173-83.
135. Sims N, Kannan A, Holton E, et al. Antimicrobials and antimicrobial resistance genes in a one-year city metabolism longitudinal study using wastewater-based epidemiology. Environ Pollut 2023;333:122020.
136. Xu J, Xu Y, Wang H, et al. Occurrence of antibiotics and antibiotic resistance genes in a sewage treatment plant and its effluent-receiving river. Chemosphere 2015;119:1379-85.
137. Elder FCT, Proctor K, Barden R, et al. Spatiotemporal profiling of antibiotics and resistance genes in a river catchment: Human population as the main driver of antibiotic and antibiotic resistance gene presence in the environment. Water Res 2021;203:117533.
138. BioRender. Adapted from “FullTemplateName”. 2023. Available from: https://www.biorender.com/ [Last accessed on 23 Feb 2024].
139. Holton E, Sims N, Jagadeesan K, Standerwick R, Kasprzyk-Hordern B. Quantifying community-wide antimicrobials usage via wastewater-based epidemiology. J Hazard Mater 2022;436:129001.
140. Manoharan RK, Srinivasan S, Shanmugam G, Ahn YH. Shotgun metagenomic analysis reveals the prevalence of antibiotic resistance genes and mobile genetic elements in full scale hospital wastewater treatment plants. J Environ Manag 2021;296:113270.
141. Prieto Riquelme MV, Garner E, Gupta S, et al. Demonstrating a comprehensive wastewater-based surveillance approach that differentiates globally sourced resistomes. Environ Sci Technol 2022;56:14982-93.
142. Che Y, Xia Y, Liu L, Li AD, Yang Y, Zhang T. Mobile antibiotic resistome in wastewater treatment plants revealed by Nanopore metagenomic sequencing. Microbiome 2019;7:44.
143. Makowska N, Philips A, Dabert M, et al. Metagenomic analysis of β-lactamase and carbapenemase genes in the wastewater resistome. Water Res 2020;170:115277.
145. Kanwar N, Blanco C, Chen IA, Seelig B. PacBio sequencing output increased through uniform and directional fivefold concatenation. Sci Rep 2021;11:18065.
146. Zhang L, Chen F, Zeng Z, et al. Advances in metagenomics and its application in environmental microorganisms. Front Microbiol 2021;12:766364.
147. New FN, Brito IL. What is metagenomics teaching us, and what is missed? Annu Rev Microbiol 2020;74:117-35.
148. Al-Jassim N, Ansari MI, Harb M, Hong PY. Removal of bacterial contaminants and antibiotic resistance genes by conventional wastewater treatment processes in Saudi Arabia: is the treated wastewater safe to reuse for agricultural irrigation? Water Res 2015;73:277-90.
149. Quintela-Baluja M, Abouelnaga M, Romalde J, et al. Spatial ecology of a wastewater network defines the antibiotic resistance genes in downstream receiving waters. Water Res 2019;162:347-57.
150. Munk P, Brinch C, Møller FD, et al. Global Sewage Surveillance Consortium. Genomic analysis of sewage from 101 countries reveals global landscape of antimicrobial resistance. Nat Commun 2022;13:7251.
151. Majlander J, Anttila VJ, Nurmi W, Seppälä A, Tiedje J, Muziasari W. Routine wastewater-based monitoring of antibiotic resistance in two finnish hospitals: focus on carbapenem resistance genes and genes associated with bacteria causing hospital-acquired infections. J Hosp Infect 2021;117:157-64.
152. Wang Q, Wang P, Yang Q. Occurrence and diversity of antibiotic resistance in untreated hospital wastewater. Sci Total Environ 2018;621:990-9.
153. Mtetwa HN, Amoah ID, Kumari S, Bux F, Reddy P. Wastewater-based surveillance of antibiotic resistance genes associated with tuberculosis treatment regimen in kwazulu natal, South Africa. Antibiotics 2021;10:1362.
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Clarke LM, O’Brien JW, Murray AK, Gaze WH, Thomas KV. A review of wastewater-based epidemiology for antimicrobial resistance surveillance. J Environ Expo Assess 2024;3:7. http://dx.doi.org/10.20517/jeea.2023.29
AMA Style
Clarke LM, O’Brien JW, Murray AK, Gaze WH, Thomas KV. A review of wastewater-based epidemiology for antimicrobial resistance surveillance. Journal of Environmental Exposure Assessment. 2024; 3(1): 7. http://dx.doi.org/10.20517/jeea.2023.29
Chicago/Turabian Style
Clarke, Leah M., Jake W. O’Brien, Aimee K. Murray, William H. Gaze, Kevin V. Thomas. 2024. "A review of wastewater-based epidemiology for antimicrobial resistance surveillance" Journal of Environmental Exposure Assessment. 3, no.1: 7. http://dx.doi.org/10.20517/jeea.2023.29
ACS Style
Clarke, LM.; O’Brien JW.; Murray AK.; Gaze WH.; Thomas KV. A review of wastewater-based epidemiology for antimicrobial resistance surveillance. J. Environ. Expo. Assess. 2024, 3, 7. http://dx.doi.org/10.20517/jeea.2023.29
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