A REVIEW OF CURRENT WASTEWATER-BASED EPIDEMIOLOGY TO MONITOR
ANTIBACTERIAL RESISTANCE AMONG GRAM NEGATIVE BACTERIAL SPECIES IN
THE UNITED STATES

By

Sanchitha Meda

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Basic Medical Science – Master of Science
2024



ABSTRACT
Antimicrobial resistance (AMR) is an increasing major public health problem in the United
States. Therefore, monitoring the status of AMR is vital to protecting population health. While
there are established surveillance systems in clinical and veterinary settings, there is a lack of
well-developed surveillance in the environment. Wastewater-based epidemiology, with focus on
wastewater treatment plants (WWTPs), has been proposed as a solution to this gap in
observation. This kind of surveillance has many advantages, such as being able to receive results
in real-time, and is needed in addition to clinical and veterinary surveillance for AMR. However,
this system is not fully developed and is lacking in standardized gene targets and analysis
methodology. This review aims to provide a broad and current perspective of how antibacterial
resistance can be monitored in wastewater in the United States. It will assess the current
literature on method standardization which will allow for the ease of research comparison and
risk assessment. It will outline potential gene targets for gram-negative bacterial species,
specifically E. coli and Shigella spp., describe the advantages and disadvantages of the main
analysis technologies (culture-based, amplification-based [e.g., qPCR], and metagenomics), and
assess remaining knowledge gaps for the use of wastewater surveillance to monitor AMR.
Wastewater-based epidemiology has the potential to be a low cost, passive surveillance method
to help estimate the AMR status in a community and be used in conjunction with clinical and
veterinary surveillance systems to aid public health officials inform policy and mitigation
practices to slowing the spread of AMR.



TABLE OF CONTENTS
INTRODUCTION..........................................................................................................................1
STANDARDIZED METHODS FOR AMR MONITORING OF WASTEWATER ................7
KNOWLEDGE GAPS WITH THE UTILIZATION OF WASTEWATER
SURVEILLANCE FOR AMR ....................................................................................................20
CONCLUSION ............................................................................................................................22
REFERENCES.............................................................................................................................24
APPENDIX ..................................................................................................................................30

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INTRODUCTION
The World Health Organization (WHO) has declared that antimicrobial resistance (AMR) is
one of the top ten global health threats to humanity (2021). The ability to treat common
infections is being threatened by the emergence and spread of drug-resistant pathogens (WHO,
2021). According to a recent report from The Lancet, 1.27 million deaths worldwide were
directly attributed to antimicrobial resistance in 2019 (Antimicrobial Resistance Collaborators,
2022). Globally, Escherichia coli is one of the leading antibiotic resistant bacteria (ARB) and
responsible for over 600,000 deaths associated with AMR in 2019 (Antimicrobial Resistance
Collaborators, 2022). In the United States, more than 2.8 million AMR infections occur each
year (National Infection & Death Estimates for AR, 2022). Of those, drug-resistant Shigella
accounts for about 77,000 estimated drug-resistant infections per year and has been labeled as a
“serious threat,” or a pathogen that requires prompt and sustained action in the Centers for
Disease Control’s 2019 AR Threats Report (“Antibiotic Resistance Threats in the United States,
2019,” 2019).
To combat this drug-resistant crisis, it is essential to understand the status of AMR. The
surveillance of AMR provides information on AMR’s geographical and seasonal patterns, its
incidence, as well as monitoring for new or rare resistance traits and emerging trends (Tiwari et
al., 2022). Different sectors can utilize this information for mitigating AMR infections,
prioritizing certain actions, evaluating interventions, informing empirical treatment guidelines,
reducing adverse impacts, and developing new antimicrobial drugs (Tiwari et al., 2022). In the
United States, AMR surveillance has been implemented in clinical and veterinary settings.
However, AMR surveillance in the environment is currently not well established (BengtssonPalme et al., 2023). Not only is monitoring antibiotic resistance in the environment important for

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providing information on the dynamics of AMR in the regional population, but it also is
important since environmental pathogens can potentially disseminate AMR to clinical pathogens
(Bengtsson-Palme et al., 2023; Tiwari et al., 2022).
More recently, wastewater-based epidemiology has been proposed for the surveillance of
antibiotic resistance spread, especially since its successful application to monitoring SARS-CoV2. Wastewater-based epidemiology is an epidemiological approach that is based upon the
extraction, detection, analysis, and the interpretation of biomarkers, or chemical and/or
biological compounds, in wastewater (e.g., sewage) (Sims and Kasprzyk-Hordern, 2020). These
biomarkers can be linked to the community that is within the geographically defined water
catchment areas, or watersheds (Sims and Kasprzyk-Hordern, 2020). Untreated wastewater, from
places such as hospitals and municipal buildings, is usually disposed and collected by wastewater
treatment plants (WWTPs). Typically, one WWTP serves a town or a city. Wastewater has been
shown to have a high source of antibiotic pollution (Larsson and Flach, 2022). With the
emergence of antibiotic resistant pathogens of clinical and veterinary significance over the past
decades, WWTPs have been designated as a focal point in the fight against AMR (Nguyen et al.,
2021). This type of surveillance can be utilized to predict the occurrence of ARBs and its
respective genes at the population level (Tiwari et al., 2022).
Compared to traditional clinical surveillance, wastewater surveillance has been shown to be
exceptionally resource and cost efficient, that is, one sample can embody pathogens from
thousands of people and give comprehensive health information on communities (Larson et al.,
2023; Sims and Kasprzyk-Hordern, 2020). With its ability to collect data in near-real time, it may
have the potential to be utilized as a surveillance tool for early outbreak detection of rare forms
of resistance (Larson et al., 2023; Sims and Kasprzyk-Hordern, 2020). Additionally, given that

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information is given on a whole population and not on an individual level, there is minimal legal
and ethical challenges and individual privacy concerns (Sims and Kasprzyk-Hordern, 2020).
Some of the traditional surveillance programs for antibiotic resistance target sick populations; as
such, the resistance rates in healthy populations are not well represented (Fahrenfeld and
Bisceglia, 2016). In contrast, wastewater-based surveillance has the potential for providing
variations in the levels of resistance for both healthy and sick human populations (Fahrenfeld and
Bisceglia, 2016).
However, if utilizing wastewater-based epidemiology in general, there are some
considerations. First, wastewater flow rates need to be tracked due to the wide variations in
influent flows (e.g., rainfall causing dilution) (Sims and Kasprzyk-Hordern, 2020). This is
conducted because when reporting upon the presence of a pathogen in a sample, it is reported as
the daily loads in wastewater (mg/day) (Sims and Kasprzyk-Hordern, 2020). Second, it is
challenging to extract specific targets from the abundance of chemical and biological targets that
are present in wastewater (Sims and Kasprzyk-Hordern, 2020). However, the development of
certain extraction methods such as solid phase extraction, immunoassay, and mass spectrometry
have allowed for the analysis of numerous compounds (Petrie et al., 2015). Another situation
associated with wastewater-based epidemiology is the problem constituted by dynamic
populations (e.g., populations that fluctuate due to commuters or tourism) (Sims and KasprzykHordern, 2020). The current standard is to calculate the levels of certain endogenous biomarkers
in humans (e.g., cortisol) as daily loads that have been normalized to the population (Sims and
Kasprzyk-Hordern, 2020). Although, there are challenges in estimating the population size of
individual WWTP catchment areas (Sims and Kasprzyk-Hordern, 2020).

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While there are opportunities and challenges to utilizing wastewater-based surveillance for
antibacterial resistance, there is a caveat; WWTPs are known for facilitating gene transfer and
antibacterial resistance. WWTPs are an environmental reservoir for bacteria, antibiotics, and
antibiotic resistance genes (ARGs) to persist, interact, and lead to the potential selection pressure
for ARGs (Uluseker et al., 2021; Sambaza and Naicker, 2023). WWTPs contain antibiotic
residues, ARGs, bacteria, rich supply of nutrients and other selectors which can facilitate
pathogen interactions and potentially promote genetic transmission amongst pathogens through
horizontal gene transfer of resistance genes via mobile genetic elements (MGEs), genetic
mutations, or subsequent vertical transmission of these mutations (Tiwari et al., 2022). With this
environment and other selection and stress factors, the production, transmission, and
multiplication of drug-resistant pathogens can occur, and, subsequently, antibacterial resistance
(Sambaza and Naicker, 2023). Furthermore, while WWTPs employ various treatment processes
to remove a variety of contaminants from wastewater and sewage and have shown to be effective
in reducing the ARB loads in effluent samples, some drug resistant pathogens and ARGs are still
retained in the treated water by attaching to the organic matter in the wastewater and may be
disseminated into the environment (Fouz et al., 2020; Sambaza and Naicker, 2023). These ARGs
and drug resistant pathogens can then go on to cause untreatable or difficult-to-treat infections in
humans (Sambaza and Naicker, 2023). Figure 1 illustrates the pathway of AMR in wastewater
through a WWTP.
To combat antibacterial agents, bacteria are genetically encoded to use natural or acquired
resistance mechanisms. Natural resistance may be intrinsic (always expressed in the bacteria) or
induced (naturally occurring genes in the bacteria that are only expressed after exposure to an
antibiotic) (Reygaert, 2018). Intrinsic resistance can be defined as a trait that is universal within a

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bacterial species (Reygaert, 2018). This is a bacterial species’ innate ability to resist activity of a
particular antibiotic and allow tolerance of that particular antibiotic (University of Minnesota,
2023). One of the most common intrinsic resistance mechanisms is the reduced permeability of
the outer membrane, specifically the lipopolysaccharide (LPS), in gram negative bacteria
(Reygaert, 2018). As such, all gram negatives, which include E. coli and Shigella spp., are
intrinsically resistant to glycopeptides and lipopeptides (Reygaert, 2018). E. coli is also
intrinsically resistant to macrolides (Reygaert, 2018). Acquired resistance occurs when a
particular bacterium obtains the ability to resist the efforts of a particular antibacterial agent to
which it was previously susceptible (University of Minnesota, 2023). Acquired resistance occurs
through mutations of genes involved in normal physiological process and cellular structures, the
acquisition of foreign resistance genes from horizontal or vertical gene transfer, or a combination
of these mechanisms (University of Minnesota, 2023). There are four main categories of
acquired mechanisms for antimicrobial resistance: (1) limiting drug uptake; (2) drug target
modification; (3) drug inactivation; (4) drug efflux (Reygaert, 2018). Gram negative bacteria
make use of all four main mechanisms (Reygaert, 2018). Thus, gram negative bacteria will be
the focus of this review, specifically E. coli and Shigella spp.
While wastewater surveillance of AMR appears to be promising, it is not fully developed and
there is a lot of work to be done before using the approach as a reliable surveillance tool (Tiwari
et al., 2022). Specifically, there is a lack of agreed upon targets and no standardized methodical
approach to monitoring AMR in the environment (Hu et al., 2018). This review aims to offer a
broad and current perspective of how antibacterial resistance can be monitored among two gramnegative bacterial species, specifically E. coli and Shigella spp., in wastewater in the United
States. It will attempt to delineate potential ARG targets, analysis methodology, and assess

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remaining knowledge gaps for the use of wastewater-based epidemiology for antibacterial
resistance surveillance among the gram-negative bacterial species. The information from this
review can aid in strengthening wastewater-based epidemiology for ARB surveillance among the
aforementioned pathogens. Specifically, this review can benefit the creation of a wastewaterbased surveillance system that assesses ARB status by determining which drug-resistant
pathogens and ARGs are dominant or emerging in a certain population and to inform public
health efforts. This can be a resource to provide context for government organizations that are
considering implementing this kind of surveillance system.

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STANDARDIZED METHODS FOR AMR MONITORING OF WASTEWATER
There have been calls to standardize targets and methods for environmental AMR monitoring
(Berendonk et al., 2015; Pruden et al., 2018). With AMR being quite complex, a multitarget and
adaptable approach would be required with emphasis on quality assurance and quality control
practices (Pruden et al., 2018; Liguori et al., 2022). There is a consensus that there needs to be an
agreement on targets for monitoring and a standardization of the methods for AMR surveillance
(Berendonk et al., 2015; Pruden et al., 2018; Liguori et al., 2022). With method standardization,
it is easier to share research findings and compare risks (Tiwari et al., 2022). The occurrence of
ARGs is frequently detected in WWTPs and studies have illustrated that the ARGs found in
wastewater often reside in clinically relevant pathogenic bacteria (Uluseker et al., 2021). This
section will attempt to shed light on potential ARG targets and methods that could be used to
monitor AMR among E. coli and Shigella spp. in humans to inform public health efforts.
Resistance Mechanisms and Antibiotic Resistance Gene Surveillance Targets: E. coli
Escherichia coli is a gram-negative bacterium that can cause severe infections and is a major
reservoir of resistance genes that may be responsible for the failure of clinical treatments in
humans (Poirel et al., 2018). During the last decades, there has been an increasing number of
resistance genes acquired by horizontal gene transfer identifies in E. coli isolates (Poirel et al.,
2018). E. coli acts in a dual manner as a donor of genetic material to other bacteria and as a
recipient of resistance genes from other microorganisms (Poirel et al., 2018). As such, the
Enterobacteriaceae family can develop resistance to many classes of antibiotics (GalindoMéndez, 2020). Currently, E. coli is resistant to many major classes of antibiotics including βlactams, quinolones, aminoglycosides, sulfonamides, fosfomycin, as well as last resource
antibiotic classes such as the polymyxins and carbapenem (Galindo-Méndez, 2020). The

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following will focus on the resistance mechanisms developed by E. coli against the β-lactam
antibiotic group.
β-lactams inhibit the synthesis of peptidoglycan, a component of a microorganism’s cell wall
by inactivating penicillin-binding proteins (PBPs) via hydrolysis (Galindo-Méndez, 2020). With
regards to E. coli’s resistance to the β-lactams, they produce a group of enzymes referred to as βlactamases (Galindo-Méndez, 2020). These enzymes are ancient compounds with over 2,800
unique proteins that emerged from environmental sources (Bush, 2018). The β-lactamase genes
are usually found in MGEs such as plasmids and can be transferred horizontally (Nzima et al.,
2020). The two clinically relevant β-lactamases that are of public health concern are extended
spectrum β-lactamases (ESBL) and the carbapenemases.
While most members of the Enterobacteriaceae family can produce these enzymes, E. coli is
one of the predominant ESBL-producing genera (Galindo-Méndez, 2020). Of the ESBLproducing E. coli, the most common types during the 1980s were blaTEM and blaSHV (Higgins et
al., 2023). However, the blaCTX-M has become the dominant ESBL in Enterobacteriaceae,
associated with human and animal infections (Higgins et al., 2023). A recent meta-analysis
reported that the prevalence of ESBL-producing Enterobacteriaceae in wastewater, with the
highest being E. coli, has been increasing over time in the United States (Zaatout et al., 2021).
The meta-analysis also confirmed that among the ESBL genes, blaCTX-M had the highest
prevalence in wastewater, followed by blaTEM and blaSHV (Zaatout et al., 2021). Another study
asked survey participants to select three targets from a list of ARGs for AMR monitoring of
water environments and blaCTX-M was one of the five most frequently selected targets (Liguori et
al., 2022). Based on the literature, it is acceptable to utilize these genes as wastewater

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surveillance targets for β-lactam resistance. More examples of the ARGs detected in wastewater
that could potentially serve as surveillance targets are provided in Table 1.
Another clinically relevant β-lactamases are the carbapenemases that decreases a bacteria’s
susceptibility to carbapenems. Carbapenems are a group of antibiotics that are considered the last
line of drugs for the treatment of severe infections (Murugan et al., 2019). They bind to PBPs
and induce spheroplast formation and cell lysis without filament formation (Galindo-Méndez,
2020). With the rise in ESBL-producing E. coli, there has been an increase in the carbapenem
usage, which has resulted in the spread of carbapenemase-producing E. coli (Murugan et al.,
2019). Some studies report that the carbapenemases in E. coli mainly include KPC, MBL,
including the NDM type and OXA; however, different reports illustrate the predominant types in
E. coli are NDM-1 and OXA-48 (Galindo-Méndez, 2020). As far as wastewater detection, a
study, funded by the U.S. Environmental Protection Agency (EPA), conducted a survey from
different U.S. WWTPs that confirmed the presence of carbapenem-resistant E. coli and found
that the most commonly detected carbapenemase gene was blaVIM, followed by blaKPC (Hoelle et
al., 2019). Another study found that blaOXA-363, blaOXA-309, and blaOXA-371 were prominent βlactam ARGs in US/Europe sewage (Prieto Riquelme et al., 2022). Overall, it can be concluded
that these genes can be targeted for surveilling carbapenem resistance in wastewater. More
examples of the ARGs detected in wastewater that could be potential surveillance targets are
provided in Table 1.
Resistance Mechanisms and Antibiotic Resistance Gene Surveillance Targets: Shigella spp.
Shigella is a genus consisting of four species, including Shigella dysenteriae, Shigella
flexneri, Shigella boydii and Shigella sonnei, and is considered as a major pathogen responsible
for the increasing rates of morbidity and mortality caused by dysentery (Ranjbar & Farahani,

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2019; Shahin et al., 2019). In addition, about half of the strains of Shigella in many parts of the
world are now resistant to multiple drugs (Ranjbar & Farahani, 2019). The CDC found that
between 2015 and 2023, the percentage of multi-drug resistant Shigella was largely made up by
Shigella sonnei (66%), followed by Shigella flexneri (34%) (2023). There is multiple resistance
mechanisms Shigella spp. utilize to evade the effects of an antibacterial agent.
Being that Shigella spp. belongs to the family Enterobacteriaceae, it is closely related to and
shares many common characteristics with E. coli (Devanga Ragupathi et al., 2017). As such,
Shigella spp. also has the ability to acquire resistance to β-lactams via ESBLs and
carbapenemases (Ranjbar & Farahani, 2019). Common ESBL resistance enzymes found in
Shigella and detected in wastewater are TEM, SHV, and CTX-M (Ranjbar & Farahani, 2019;
Rizzo et al., 2013). For carbapenem resistance genes found in wastewater, VIM and IMP genes
were detected in S. sonnei and S. flexneri isolates (Ranjbar & Farahani, 2019; Rizzo et al., 2013).
Attributing the ESBL resistance enzymes in either E. coli or Shigella spp. in wastewater may be
difficult as they are the same. Other targets or indicators may be needed to overcome this
problem. On the other hand, the carbapenem resistance genes listed above would be adequate
wastewater targets to monitor. More examples of the ARGs related to β-lactams that are detected
in wastewater and could potentially be utilized as surveillance targets are provided in Table 1.
Shigella spp. also has acquired resistance to fluoroquinolones through several mechanisms
including mutations in the quinolone resistance-determining region (QRDR) and plasmidmediated quinolone resistance region (PMQR) (Teimourpour et al., 2019). One form of
fluoroquinolone-resistance is by targeting the two bacterial enzymes that play a role in DNA
replication and are encoded in the QRDR area are DNA gyrase (gyrA and gyrB genes) and
topoisomerase IV (parC and parE genes) (Teimourpour et al., 2019). While gyrA has been

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detected in wastewater, the more commonly detected gene is of the qnr genes (Pazda et al.,
2019). qnr genes are plasmid genes found in PMQR regions that encode proteins protecting
DNA gyrase and topoisomerase IV against quinolone compounds and are located on MGEs
(Pazda et al. 2019). These genes are the main reason for resistance to fluoroquinolones among
Shigella isolates (Ranjbar & Farahani, 2019). As such, these would be adequate target genes for
the surveillance of fluoroquinolone resistance. More examples of the ARGs related to
fluoroquinolones that are detected in wastewater are provided in Table 1.
Another class of antibiotics that Shigella spp. has acquired resistance to is the tetracyclines
(Ranjbar & Farahani, 2019). Tetracyclines’ main mechanism of resistance is due to the efflux
pump and ribosomal protection system (Shahsavan et al., 2017). The efflux pump genes encode
membrane proteins which export tetracycline from the cell, making it ineffective (Shahsavan et
al., 2017). Five efflux genes –tet(A), tet(B), tet(C), tet(D), and tet(G) — and one ribosomal
protection protein encoded by tet(M) have been identified among Shigella isolates (Ranjbar &
Farahani, 2019). All these resistance genes have been found in wastewater, with tetA and tetB
more commonly found (Nguyen et al., 2019; Pazda et al., 2019; Rizzo et al., 2013;
Szczepanowski et al., 2009). In fact, many studies have found that the tet resistance gene is one
of the most commonly occurring ARGs in wastewater treatment systems in many countries
(Uluseker et al., 2021). As such, it would be acceptable to utilize these genes as wastewater
surveillance targets to monitor tetracycline resistance. More examples of the tetracycline related
ARGs that are detected in wastewater are provided in Table 1.
Other Considerations
It is important to note the difference in concentration of ARGs in influent and effluent
sources. In a review of ARGs in WWTPs, Pazda et al. reported 98% of ARGs were removed

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from the effluent (2019). However, they also reported that there was enrichment of some
resistance genes in the effluent (Pazaa et al., 2019). This illustrates that WWTPs are not designed
to remove ARGs (Pazda et al., 2019). If using WWTPs as a surveillance source, the location of
where the samples are taken should be considered. With the objective of building a surveillance
system that determines which forms of AMR are dominant or emerging in human populations to
information public health efforts, samples should be taken from influent wastewater, as opposed
to effluent. This source is more relevant to the community that provides the sewage to the
WWTP.
At the heart of this is determining which ARGs should be used for the surveillance of
antibacterial resistance among humans and be used to inform public health efforts. There are
many different ideas that have been proposed. One is the “ResCon” risk ranking system in which
individual ARGs are assigned a risk value between 1 and 7, depending on a variety of
considerations (Martínez et al., 2015). ARGs that are more recently evolved, encode resistance to
new antibiotics, or that are associated with MGEs score higher on the risk scale (Martínez et al.,
2015). Others have argued that ARGs that are well-known and have been around for decades
pose a lesser risk (Vikesland et al., 2017). For emerging ARGs, Bengtsson-Palme et al. have
suggested to create a ranked watchlist for upcoming potential AMR threats (2023). They describe
this list to include latent ARGs that are of concern for various reasons, including high level
resistance to critical antibiotics observed in experiments, indications of broad-spectrum activity
or poor clinical outcomes when the gene is detected in pathogens, the gene is located on a highly
transferable MGE, and more (Bengtsson-Palme et al., 2023). They argue that this environmentbased watchlist for ARGs would provide an early warning about emerging AMR before these
genes are widespread (Bengtsson-Palme et al., 2023). This could potentially be one method of

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determining ARG wastewater surveillance targets. A more obvious choice would be to select
ARG targets based on the geographical location of the WWTP.
A major challenge with focusing on ARGs as surveillance targets is that they are inherently
difficult to detect in the environment (Vikesland et al., 2017). Prior to the broad antibiotic use in
modern medicine, antibiotic resistance has been a natural phenomenon (Li et al., 2020). In fact,
genes encoding resistance to β-lactams, tetracycline and glycopeptide antibiotics have been
detected in natural soil and even in 30,000-year-old permafrost sediments (Li et al., 2020).
Therefore, it is vital for ARG source tracking that autochthonous and allochthonous ARG in an
environment are differentiated (Li et al., 2020). The quantification and report of ARGs needs to
be interpreted based on the significance of their presence and how it relates to the rapid evolution
and spread of MDR bacteria (Nguyen et al., 2021). Seasonality changes can also impact
surveillance efforts through diluting or enriching ARGs in a certain area (Li et al., 2020). As of
late, the class 1 integron - integrase gene, intl1, has been proposed as an indicator for AMR and
HGT because of its common linkage with ARGs and its quick response to diverse environmental
pressures (Li et al., 2020; Rumky et al., 2022). Overall, it is clear that to conduct AMR
surveillance in wastewater, other indicator or control factors are needed to differentiate target
bacteria or genes from background bacteria and environmental conditions.
Wastewater Analysis Methodology for ARGs and ARBs
As mentioned previously, to utilize wastewater-based epidemiology for monitoring AMR, the
standardization of methods is needed. Currently, there is no universal monitoring method
available (Miłobedzka et al., 2022). It is important to consider the objective of the analysis (i.e.,
studying either the diversity of ARGs and ARBs or to measure the abundance (per mass/volume
of sample) or prevalence (per total bacteria) in a given environment) when determining which

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method is appropriate (Manaia et al., 2018). There are three main wastewater analysis methods
that are being considered for monitoring antibacterial resistance: culture-based methods,
amplification-based methods, and metagenomic-based methods (Liguori et al., 2022).
Culture-based methods
Culture-based methods have been considered for AMR monitoring because specific clinically
relevant targets can be selected, the methods are well standardized for defining clinical resistance
levels and not technically difficult to conduct, and the recovered target is viable (Scott et al.,
2020; Liguori et al., 2022). Its main appeal is being able to determine phenotypic traits, which is
the basis to assess the propagation or gene transfer potential of specific ARB under
environmental conditions (Manaia et al., 2018). Those isolated can be subject to further analysis
including multidrug-resistance testing, sequence-based testing, or whole genome sequencing
(i.e., can help in identification MGEs carrying ARGs, identifying sources of outbreak strains, or
delineate phylogenetic relationships among strains) (Liguori et al., 2022; Tiwari et al., 2022). In
addition, since the current AMR surveillance approach is based on clinical isolates, a culturebased method can be relatively convenient at the local level (Tiwari et al., 2022). A key
distinction is that culture-based methods have been better able to inform human health risk
assessments (Liguori et al., 2022).
However, with the large number of microorganisms found in wastewater, the likelihood of
interference to isolate the target is increased (Liguori et al., 2022). One study attempted to
address this concern by adapting and improving already standardized tests originally developed
for human medical purposes (Schreiber et al., 2021). While the researchers were able to provide
an appropriate culture-based approach for the microbiological investigation of environmental
water samples for ESBL-producing bacteria, they recommended that for the determination of

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carbapenem-resistant bacteria, gene detection via PCR would be better suited since numerous
environmental bacteria harbor intrinsic carbapenemase genes (Schreiber et al., 2021).
Furthermore, only less than 1% of environmental microorganisms can be cultured, therefore
those that harbor resistance can be overlooked by culture-based methods (Tiwari et al., 2022).
Moreover, certain pathogens may be overlooked because they have concentrations that are too
low for detection by culture, but that concentration may be high enough to cause infection
(Girones et al., 2010). Additionally, after prolonged exposure to water, bacteria might enter a
viable but non-culturable state, while retaining their infective potential (Girones et al., 2010).
Another practical challenge with culture-based methods is that it is a costly and laborious
procedure that involves enrichment and different selective media to be able to isolate pathogens
from background bacteria (Girones et al., 2010; Manaia et al., 2018). Furthermore, attempting to
achieve appropriate enrichment is often difficult and time-consuming (Girones et al., 2010). It
also does not provide any information of the mechanisms of resistance which may disseminate to
other bacterial species via MGEs (Anjum, 2015). These limitations are important to consider,
especially in cases when critical and timely intervention for infectious diseases is required. With
all that in mind, it is clear that a different methodology is needed in combination with cultivation
for the detection and quantification of ARGs and pathogens in wastewater. Table 2 describes the
strengths and weaknesses in using culture-based methods to detect ARGs and bacteria in
wastewater.
Amplification-based methods
As technologies have advanced, the analysis of ARGs have started to move towards cultureindependent methods (Nguyen et al., 2021; Liguori et al., 2022; Miłobedzka et al., 2022). PCR
techniques, more notably real-time quantitative PCR (qPCR), are considered to be highly

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specific and sensitive (Girones et al., 2010; Liguori et al., 2022). qPCR is quickly becoming
established in the environmental sector and is currently the preferred method for the
identification and quantification of ARGs (Girones et al., 2010; Scott et al., 2020). This costeffective and rapid technique uses specific probes to gather a significant amount of information
on the presence, quantity and distribution of pathogens in water (Girones et al., 2010). With its
high sensitivity, qPCR has the power to nearly doubling the gene target every thermal cycle,
allowing for a lower limit of detection (Borchardt et al., 2021). In addition, qPCR can
simultaneously analyze a large number of genes (Manaia et al; 2018; Loguori et al., 2022;
Miłobedzka et al., 2022). This circumvents the limitations of culture-based methods, e.g., the
requirement to choose a single species or genera of bacteria to study, including the limited ability
of selective media to isolate and quantify targets against background bacteria (Keenum et al.,
2022). qPCR, in combination with epidemiological surveys, can also be useful for risk
assessment (Girones et al., 2010). Furthermore, it is advantageous that qPCR uses DNA, as it can
detect ARGs in non-culturable bacteria (Abramova et al., 2023).
Conversely, there are some limitations to utilizing qPCR. First, it is incapable of
distinguishing living from dead cells (Manaia et al., 2018; Scott et al., 2020; Liguori et al.,
2022). There have been studies that illustrate overcoming this limitation, for instance, using
propidium monoazide to distinguish membrane injured cells from intact cells (Manaia et al.,
2018). However, there are other mechanisms of cell inactivation that those studies do not address
(Manaia et al., 2018). In addition, qPCR is designed to follow the amplification of a specific
gene fragment through the use of primers that have been previously described, therefore, for
unknown/new genes, creating primers would be virtually impossible (Manaia et al., 2018; Scott
et al., 2020). Furthermore, if the primer is designed based on a specific ARG variant, it may not

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universally capture all versions of these variants (Keenum et al., 2022). Therefore, if the
objective is to identify emergent resistance threats, qPCR may not be the best method (Abramova
et al., 2023). It is also important to keep in mind that qPCR is susceptible to factors such as the
reaction components, the analytical equipment, master mixes, the type of sample (e.g.,
wastewater) (Miłobedzka et al., 2022). This can produce significant difference in results (Girones
et al., 2010). Additionally, it is important that the personnel have expertise in molecular biology,
which isn’t as needed with culture-based methods (Liguori et al., 2022). Moreover, some
protocols may have limited details with regards to key quality assurance aspects for
environmental samples, including positive and negative controls, limit of detection, and limit of
quantification, etc. (Borchardt et al., 2021; Keenum et al., 2022). Overall, if the objective is to
detect high-risk ARGs for monitoring resistance, qPCR would be an appropriate method to use
because of its sensitivity. Table 2 describes the strengths and weaknesses of using amplificationbased methods in detecting ARGs and bacteria in wastewater.
Metagenomic-based methods
While qPCR is limited to only conducting targeted analysis, metagenomics is suitable for
non-targeted analysis (Manaia et al., 2018). Metagenomic methods sequence the whole
metagenome present in the sample (Manaia et al., 2018). Utilizing next-generation DNA
sequencing (NGS) allows for the possibility of profiling all ARGs, unculturable bacteria, and
other genes in a sample without prior knowledge of gene targets (Liguori et al., 2022;
Miłobedzka et al., 2022). This allows for the possibility to provide an overview of not only the
already known ARGs, but also of their variants or possible new ARGs that may exist in an
environment (Manaia et al., 2018). Wastewater surveillance via metagenomics could potentially
prove to be useful for identifying emergent resistance threats (Miłobedzka et al., 2022). In fact,

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there has already been an attempt to monitor and predict the occurrence of AMR in a global
healthy human population using metagenomics (Hendriksen et al., 2019). Not only can
metagenomics provide broad contextual information, but it can also provide a large amount of
information on the diversity of ARGs and MGEs in different environments (Rice et al., 2020). It
is also being utilized for examining shifts in the resistome thorough WWTPs and identifying
MGEs to determine the extent of HGT events (Liguori et al., 2022). The information can also be
stored and reused later to allow for retrospective analysis of resistance genes after the initial run
(Bengtsson-Palme et al., 2017).
Currently, there are some challenges with using metagenomics for wastewater surveillance
for AMR. One major challenge is that there are multiple ways to generate, analyze, and interpret
the data, making it difficult to compare metagenomic data across studies (Liguori et al., 2022). If
there are differences in sampling, storage conditions, DNA extraction methodologies, and
sequencing depths, it can bias the generation and comparison of metagenomic libraries,
influencing the abundance and diversity of ARGs detected in a sample (Liguori et al., 2022;
Miłobedzka et al., 2022). Additionally, the obtained genes, or close variants of them, are present
in a reference database to assign them to a resistance phenotype (Bengtsson-Palme et al., 2017).
These databases are inconsistent and vary in completeness and nomenclature (Liguori et al.,
2022). Additionally, regarding measuring specific gene abundances, metagenomic-based
methods are less sensitive, or require higher detection limits, than qPCR (Bengtsson-Palme et al.,
2017). Furthermore, because metagenomics has a non-targeted approach, there is a higher
representation of genes that are not of interest, and thus very rare genes or targets of interest will
likely not be detected (Liguori et al., 2022). To overcome this limitation, deeper sequencing is
required, however that is very expensive (Miłobedzka et al., 2022; Liguori et al., 2022). It also

18



does not assess bacterial viability (Scott et al., 2020; Liguori et al., 2022). Another limitation of
metagenomics is that it requires extensive technical knowledge to analyze the data and is
laborious and time consuming (Scott et al., 2020; Miłobedzka et al., 2022). Overall,
metagenomics is a promising method to utilize for monitoring AMR in wastewater, but because
of its cost and other challenges, it should be used in conjunction with other methods, such as
qPCR. Table 2 describes the strengths and weaknesses of using metagenomics in detecting ARGs
and bacteria in wastewater.
Future Directions for Method Standardization
With each method having different strength and weaknesses, it leaves the question of which
method would be best for AMR monitoring in wastewater. Pruden et al. have suggested that the
standardization of methods should be employed in a tiered fashion (2021). The first tier would be
the most accessible to all and should be carried out by all participating locales (Pruden et al.,
2021). This would include sample collection and concentration, culture and storage of the
samples, and nucleic acid extraction (Pruden et al., 2021). The second tier would be carried out
in-house or by centralized facilities (Pruden et al., 2021). This is where phenotypic and genotypic
antimicrobial resistance profiling via qPCR would occur (Pruden et al., 2021). The final third tier
would be conducted by centralized facilities and would be the least accessible due to cost
(Pruden et al., 2021). This is where whole genome sequencing and metagenomic methods would
occur (Pruden et al., 2021). All of the samples and data would be collected in a centralized
sample archives and would then be used to create public-facing dashboards to further facilitate
standardization of data analysis, reporting, and sharing (Pruden et al., 2021). This seems like an
excellent option in implementing a surveillance system for monitoring AMR in wastewater. With
standardization and centralized sample archives, sharing and comparing findings will be easier.

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KNOWLEDGE GAPS IN THE UTILIZATION OF WASTEWATER SURVEILLANCE
FOR AMR
When developing a surveillance system, arguably one of the main concerns that should be
addressed is the human health risk assessment. For the wastewater surveillance of AMR, that is
unclear. To evaluate human health risk, there needs to be more research done on AMR
characterization and the findings of wastewater-based ARB studies need to be compared with
clinical evidence (Nguyen et al., 2021; Tiwari et al., 2022). However, with wastewater, this
comparison and characterization is not straightforward.
To start, the ARB detected in wastewater may not just come from symptomatic human
individuals, but it could also be from asymptomatic carriers or from animal sources (Tiwari et
al., 2022). To overcome this hurdle, this would require comprehensive expert interpretation and
having knowledge on the current events around the community (Tiwari et al., 2022). To
supplement, monitoring ARBs with host-specific primers and using microbial source tracking
methods may help in differentiating the source (Tiwari et al., 2022). Without this information, the
prevalence estimation of ARBs reported at the population level would be inaccurate. More
research into how to determine what source the ARB came from is vital in being able to assess
risk.
Additionally, there is no current threshold for wastewater surveillance of ARBs with regards
to how much diversity and abundance of bacteria is too high or result in elevated exposure and
risk of acquiring a resistant infection (Liguori et al., 2022; Tiwari et al., 2022). Interpreting the
results from molecular methods can be challenging and not being able to accurately interpret the
results of wastewater surveillance of ARBs and relay that to health risk is of concern. Standards
need to be put in place for this AMR characterization, as well as, when to declare an outbreak or

20



emergency state (Tiwari et al., 2022). There also needs to be more research on how to determine
what an accurate representation of the extent of ARB/ARG exposure is and to be able to correlate
that to associated health risks (Larsson et al., 2018; Nguyen et al., 2021). This would require
more knowledge on exposure levels and to what extent the exposure leads to disease or
colonization (Larsson et al., 2023). Overall, there needs to be extensive research done on AMR
characterization and human health risk assessment before AMR surveillance in wastewater can
be utilized on a grander scale.

21



CONCLUSION
Wastewater-based surveillance is an up-and-coming surveillance system that has great
potential to be used as a tool to monitor AMR in the United States by estimating the extent to
which AMR might be circulating in a given community in real-time. However, wastewater-based
epidemiology for AMR monitoring has not been fully developed for quantitative surveillance.
Specifically, the literature assessed in this review illustrates that while there has been progress in
method standardization, there needs to be a consensus on which ARGs to target and what
analysis methods should be used for this system to be a reliable surveillance tool. The current
position on ARG target standardization has been to focus on clinically relevant and/or high-risk
genes as potential targets. With regards to analysis technologies, each of the three main methods
currently used have their own advantages and disadvantages. Cost, the amount of expertise
needed, and human health risk assessments are essential factors that should be taken into
consideration for public health departments when implementing an analysis method. There is not
one single method that can fulfill all the requirements of wastewater analysis for AMR
monitoring. Additionally, there are many knowledge gaps with regards to interpreting wastewater
results and determining the prevalence of the source of AMR that need to be addressed. Future
directions should focus on assessing human health risk by comparing wastewater findings with
clinical evidence. There needs to be a coordinated effort among public health departments,
laboratories, hospitals, and other organizations in employing established standard methods at the
local, state, and national level to slow the spread of AMR. Once assessed to be consistent and
reliable, wastewater surveillance can help provide AMR burden data for a community. AMR
surveillance in the environment via wastewater is just one component of being able to capture
the complete spectrum of AMR. Wastewater surveillance should be used in conjunction with

22



clinical and veterinary surveillance to monitor AMR status and together these systems can
provide essential information to public health officials to inform policy and mitigation practices
to help maintain the effectiveness of antibiotics in the future.

23



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https://doi.org/10.3389/fmicb.2021.717809
University of Minnesota. (2023). Microbiology. Antimicrobial Resistance Learning Site;
University of Minnesota.
https://amrls.umn.edu/microbiology#:~:text=Innate%20resistance%20is%20normally%2
0expressed
Vikesland, P. J., Pruden, A., Alvarez, P. J. J., Aga, D., Bürgmann, H., Li, X. D., Manaia, C. M.,
Nambi, I., Wigginton, K., Zhang, T., & Zhu, Y. G. (2017). Toward a Comprehensive
Strategy to Mitigate Dissemination of Environmental Sources of Antibiotic
Resistance. Environmental science & technology, 51(22), 13061–13069.
https://doi.org/10.1021/acs.est.7b03623
World Health Organization (2021). Fact sheets antimicrobial resistance. Geneva: World Health
Organization. https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance
Zaatout, N., Bouras, S., & Slimani, N. (2021). Prevalence of extended-spectrum β-lactamase
(ESBL)-producing Enterobacteriaceae in wastewater: a systematic review and metaanalysis. Journal of water and health, 19(5), 705–723.
https://doi.org/10.2166/wh.2021.112
Zhang, A., Call, D. R., Besser, T. E., Liu, J., Jones, L., Wang, H., & Davis, M. A. (2019). βlactam resistance genes in bacteriophage and bacterial DNA from wastewater, river water,
and irrigation water in Washington State. Water research, 161, 335–340.
https://doi.org/10.1016/j.watres.2019.06.026

29



APPENDIX
Figure 1
Pathway of AMR via wastewater through a wastewater treatment plant.

Image Source: Meda, S. (2023). AMR Pathway in Wastewater via a WWTP [Graphic Image].

30



Table 1
Examples of antibiotic resistance genes detected in wastewater that could serve as potential
surveillance targets.
Antibiotic Class Mechanism
Example of genes a
Reference
β-lactams
Hydrolyze narrow
blaTEM, blaSHV, blaCTX-M,
Ranjbar &
ESBL antibiotics
blaPER, blaVEB, blaGES,
Farahani, 2019:
blaTLA
Szczepanowski et
al., 2009; Zhang et
al., 2019

Fluoroquinolones

Tetracyclines

a

Hydrolyze
carbapenems

blaIMP, blaVIM, blaNDM,
blaKPC, blaOXA-363, blaOXA309, blaOXA-371

Ranjbar &
Farahani, 2019:
Szczepanowski et
al., 2009; Zhang et
al., 2019; Prieto
Riquelme et al.,
2022

Plasmid-mediated

qnr (A, B, C, D, S)

Chromosomal targetsite mutations

gyr (A B), parC

Efflux pumps

tet (A, B, C, D, E)

Nguyen et al., 2021;
Pazda et al., 2019;
Rizzo et al., 2013
Pazda et al., 2019;
Szczepanowski et
al., 2009
Nguyen et al., 2019;
Pazda et al., 2019;
Rizzo et al., 2013;
Szczepanowski et
al., 2009

Ribosomal protection
protein

tetM

Nguyen et al., 2019;
Pazda et al., 2019;
Rizzo et al., 2013;
Szczepanowski et
al., 2009

This is not an exhaustive list; Gene variations are within parentheses for simplicity.

31



Table 2
Strengths and weaknesses of methods used for detecting ARGs and bacteria in wastewater.
Methods
Strengths
Weaknesses
References
Culture-based
Girones et al.,
• Not technically
• Difficult to isolate
2010; Anjum,
difficult.
pathogens with
2015; Manaia
background bacteria.
• Highly
et al., 2018;
standardized.
• Expensive and timeScott et al.,
consuming
• Confirms
2020;
procedures.
bacterial viability
for human health • <1% of environmental Schreiber et
al., 2021;
risk assessment.
bacteria can be
Liguori et al.,
cultured.
• Able to determine
2022; Tiwari et
phenotypic traits
• Hard to detect low
al., 2022
for clinically
concentrations of
relevant targets.
bacteria.
• Already
• Provides no indication
established
of the mechanisms of
infrastructure.
resistance.
• Requires additional
testing for further
analysis.
Amplification (qPCR) • High sensitivity
Girones et al.,
• Does not assess
2010; Manaia
and specificity.
bacterial viability.
et al., 2018;
• Rapid and cost• Impossible to design
Scott et al.,
effective tool.
primers for
2020;
new/unknown
genes.
• Allows for
Borchardt et
specific genes or
• Protocols may lack
al., 2021;
mutations to be
quality assurance,
Nguyen et al.,
targeted.
especially for
2021; Keenum
environmental
• Can
et al., 2022;
samples.
simultaneously
• Requires personnel to Liguori et al.,
analyze multiple
2022;
ARGs targets.
be knowledgeable in
Miłobedzka et
molecular biology.
• In combination
al., 2022;
with
Abramova et
epidemiological
al., 2023
studies, can carry
out risk
assessment
studies.
• Can detect ARGs
on non-culturable
bacteria.

32



Table 2 (cont’d)
Metagenomics

•

•

•

•

•

Non-targeted
approach allows
for
comprehensive
overview of
unknown and
variety of targets
in a microbial
community.
Wealth of
knowledge on the
taxonomic and
functional genetic
diversity in a
sample.
Provides
information on
AMR prevalence,
distribution, and
routes of
transmission.
Sequence
libraries can be
stored and
retrospectively
analyzed.
Can identify
unculturable or
emergent
microorganisms.

33

•

•

•
•
•
•
•

•

Lack of
standardization for
data generation and
analysis.
Non-targeted
approach ultimately
hinders detection
limit.
Expensive.
Laborious and timeconsuming.
Does not assess
bacterial viability.
Less sensitive than
qPCR.
Only allows detection
of known genes
present in a reference
database, that of
which are
inconsistent.
Requires extensive
technical knowledge
to analyze data.

BengtssonPalme et al.,
2017; Manaia
et al., 2018;
Hendriksen et
al., 2019; Rice
et al., 2020;
Scott et al.,
2020; Liguori
et al., 2022;
Miłobedzka et
al., 2022