Environmental Pollution 285 (2021) 117402
Contents lists available at ScienceDirect
Environmental Pollution
journal homepage: www.elsevier.com/locate/envpol
Review
Distribution of antibiotic resistance genes in the environment☆
Mei Zhuang a, b, 1, Yigal Achmon a, b, 1, Yuping Cao a, b, Xiaomin Liang c, Liang Chen c, d,
Hui Wang e, Bupe A. Siame f, Ka Yin Leung a, b, *
a
Biotechnology and Food Engineering Program, Guangdong Technion – Israel Institute of Technology, Shantou, 515063, China
Faculty of Biotechnology and Food Engineering, Technion – Israel Institute of Technology, Haifa, Israel
c
Department of Computer Science, College of Engineering, Shantou University, Shantou, 515063, China
d
Key Laboratory of Intelligent Manufacturing Technology of Ministry of Education, Shantou University, Shantou, 515063, China
e
Department of Biology, College of Science, Shantou University, Shantou, 515063, China
f
Department of Biology, Trinity Western University, Langley, British Columbia, V2Y 1Y1, Canada
b
A R T I C L E I N F O
A B S T R A C T
Keywords:
Resistome
Metagenomics
One health
Antibiotics resistance genes
Antibiotic resistant bacteria
The prevalence of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in the microbiome is
a major public health concern globally. Many habitats in the environment are under threat due to excessive use
of antibiotics and evolutionary changes occurring in the resistome. ARB and ARGs from farms, cities and hos­
pitals, wastewater treatment plants (WWTPs) or as water runoffs, may accumulate in water, soil, and air. We
present a global picture of the resistome by examining ARG-related papers retrieved from PubMed and published
in the last 30 years (1990–2020). Natural Language Processing (NLP) was used to retrieve 496,640 papers, out of
which 9374 passed the filtering test and were further analyzed to determine the distribution and diversity of ARG
subtypes. The papers revealed seven major antibiotic families together with their respective ARG subtypes in
different habitats on six continents. Asia, especially China, had the highest number of ARGs related papers
compared to other countries/regions/continents. ARGs belonging to multidrug, glycopeptide, and β-lactam
families were the most common in reports from hospitals and sulfonamide and tetracycline families were
common in reports from farms, WWTPs, water and soil. We also highlight the ‘omics’ tools used in resistome
research, describe some factors that shape the development of resistome, and suggest future work needed to
better understand the resistome. The goal was to show the global nature of ARB and ARGs in order to encourage
collaborate research efforts aimed at reducing the negative impacts of antibiotic resistance on the One Health
concept.
1. Introduction
Antibiotics, the silver bullets of antimicrobial drugs, were hailed as
the greatest discovery of modern medicine in the twentieth century
(Wright, 2007). Microorganisms use antibiotics to protect themselves, to
exterminate surrounding neighbors, and allow them to colonize and
dominate different habitats. Antibiotic resistant bacteria (ARB) and
antibiotic resistance genes (ARGs) occur naturally and may date back
tens of millions or billions of years (Hall and Barlow, 2004; Wright,
2007). ARGs for β-lactams, tetracyclines, and vancomycin, have been
found in 30,000-year-old frozen sediment cores (D’Costa et al., 2011).
ARGs can be classified as intrinsic (from the producers) or as acquired
(from other bacteria through horizontal gene transfer, HGT) resistance
(Hu et al., 2017). Some ARB can evolve to become superbugs or
emerging pathogens (Fig. 1) associated with human and animal diseases
in clinical settings (e.g. drug-resistant Escherichia coli and Burkholderia).
Abbreviations: Antibiotic resistant bacteria, (ARB); antibiotic resistance genes, (ARGs); next generation DNA sequencing, (NGS); horizontal gene transfer, (HGT);
lateral gene transfer, (LGT); mobile genetic elements, (MGEs); multi-antibiotic resistance, (MAR); wastewater treatment plants, (WWTPs); carbapenem-resistant
Enterobacteriaceae, (CRE); extended spectrum β-lactams, (ESBL); Klebsiella pneumonia carbapenemase, (KPC); New Delhi metallo-β-lactamase, (NDM); Verona
integrin-encoded metallo-β-lactamase, (VIM); oxacillinase-48, (OXA-48).
☆
This paper has been recommended for acceptance by Da Chen.
* Corresponding author. Biotechnology and Food Engineering Program, Guangdong Technion – Israel Institute of Technology, Shantou, 515063, China.
E-mail address: kayin.leung@gtiit.edu.cn (K.Y. Leung).
1
Co-first authors.
https://doi.org/10.1016/j.envpol.2021.117402
Received 15 January 2021; Received in revised form 3 April 2021; Accepted 16 May 2021
Available online 19 May 2021
0269-7491/© 2021 The Author(s).
Published by Elsevier Ltd.
This is
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
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M. Zhuang et al.
Environmental Pollution 285 (2021) 117402
These ARB may acquire ARGs from the environmental resistome (Sultan
et al., 2018; Wright, 2007) due to antibiotic selection pressure or if the
fitness costs of maintaining ARGs in bacteria is low (Bengtsson-Palme
et al., 2018). Resistome, a term first proposed by D’Costa et al. (2006), is
a collection of all ARGs from pathogenic and non-pathogenic bacteria in
a microbial community.
Increased dissemination of ARB and ARGs in the environment may
result from selective pressures imposed by human activities. These ac­
tivities include overuse of antibiotics in clinics, hospitals, and nursing
homes (Duan et al., 2020; Feng et al., 2017). Other activities include
antibiotics use to prevent diseases and promote growth in farm animals
and aquaculture (Ji et al., 2012; Watts et al., 2017; Xiong et al., 2018)
(Fig. 1). ARB and ARGs from these activities collect in wastewater
treatment plants (WWTPs), wastewater, compost heaps, and water
runoffs from farms (Dong et al., 2019; Hendriksen et al., 2019; Karkman
et al., 2018; Wu et al., 2019). As the ARB die, the ARGs degrade or
dissipate, but others may accumulate in water (Anthony et al., 2020; Bai
et al., 2019; Yang et al., 2017), soil (Chen et al., 2016; Han et al., 2016;
Wu et al., 2020), or air (Brągoszewska and Biedroń, 2018; Pal et al.,
2016; Zhang et al., 2019) (Fig. 1). Different habitats may accumulate
specific ARGs. These localized ARGs are referred to as sub-resistomes in
this paper.
ARGs in the sub-resistomes are linked and intertwined in the
ecosystem. Understanding the relationships between ARGs in our
ecosystem can help us to apply the ‘One Health’ approach to reduce the
emergence of antibiotic resistance in bacteria. The ‘One Health’ concept
seeks to optimize health for people, animals and the environment
through collaborative efforts of governments, societies, corporations
and institutions (Al-Tawfiq et al., 2017; Hu et al., 2017). Collaborative
efforts to gain a good understanding of the relationship between ARGs in
sub-resistomes can lead to better surveillance and management strate­
gies in human, animal, and environmental health. The goal is to reduce
or prevent the spread of ARGs in the environment and thus prevent the
emergence and spread of superbugs.
In this review, we highlight some important antibiotic families and
the ARG subtypes found in different habitats, important ‘omics’ tools
used to study the resistome, and the factors that affect resistome
development. We also used a Natural Language Processing (NLP) of
496,640 papers published on the resistome in the last 30 years to show
the distribution, diversity, and spread of antibiotic resistance and ARGs
in different habitats in different countries/regions.
2. Major antibiotic families and ARG subtypes in the
environment
Common antibiotics can be divided into 16 families, based on the
chemical structures and modes of action. The major antibiotics struc­
tures include β-lactams, tetracyclines, sulfonamides, aminoglycosides,
Fig. 1. Interconnectivity of ARGs in six major habitats (farms, cities, WWTPs, water, soil, and air) under the One Health concept. (WWTPs = wastewater treatment
plants; ARGs = antibiotic resistant genes).
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Environmental Pollution 285 (2021) 117402
fluoroquinolones, macrolides, trimethoprim, and glycopeptides (Stoll
et al., 2012; Watts et al., 2017; dos Santos et al., 2017). Major modes of
antibiotic actions include disruption of the biosynthesis of the cell wall,
cell membrane, proteins, and DNA/RNA or the inhibition specific bac­
terial enzymes. Currently, 20–36 antibiotics are commonly used in
clinical, agricultural and aquaculture settings (Qiao et al., 2018; Zhang
et al., 2015). The widespread use of antibiotics has led to the emergence
of ARB and multi-antibiotic resistant bacteria (MAR). For example,
D’Costa et al. (2006) isolated a diverse group of spore-forming MAR
bacteria from soil resistomes that were resistant to 7–8 of common an­
tibiotics. Two of the isolates were resistant to 15 of the 21 commonly
used antibiotics. Antibiotic resistance is encoded on specific ARGs and
the widespread use of antibiotics can increase ARG subtypes in the
environment (Li et al., 2016; Mao et al., 2015).
The best studied bacteria in the resistome are enterics that are
associated with food- and water-borne diseases (Stange et al., 2016;
Wilson and Török, 2018). These multidrug resistant Enterobacteriaceae
can spread or disseminate ARGs from animal farms or clinics/hospitals
and can adversely affect agriculture and public health. ARGs spread in
bacterial communities predominantly by HGT (Lerminiaux and
Cameron, 2019; Sultan et al., 2018). Small plasmids carrying MAR genes
are not only a threat to human health, they can also disseminate ARGs in
the resistomes (Sultan et al., 2018). In this review, we highlight four
important ARG subtypes responsible for carbapenems, colistin, tetra­
cyclines, and sulfonamides resistance.
drugs (Suay-García and Pérez-Gracia, 2019).
2.2. Polymyxin-resistance genes
The polymyxin family are the second best-studied form of antibiotic
resistance due to polymyxin’s long history of use in animal production.
Polymyxins are cyclic cationic polypeptides that target the outer mem­
brane of Gram-negative bacteria. They associate with the lipid A of
lipopolysaccharide and lead to cell wall rupture (Sun et al., 2018).
Polymyxin B and colistin (polymyxin E) are the two major polymyxins
used in human medicine. Colistin is used as a prophylactic and growth
promoter in farm animals. Some countries have banned the use of
colistin in animals in order to reduce its impact on the resistome. Liu
et al. (2016) first reported the colistin resistance gene, mcr-1, isolated
from E. coli in a pig farm in China. Others genes (mcr-1 to mcr-8), most of
them plasmid borne, have been reported (Borowiak et al., 2017; Car­
attoli et al., 2017; Sun et al., 2018; Wang et al., 2018; Xavier et al., 2016;
Yang et al., 2018a, 2018b; Yin et al., 2017). Colistin and tigecycline are
the last line for controlling CRE and the worldwide jump in CRE has
pushed the heavy use of colistin, which in turn has contributed to
increased resistance to colistin (Al-Tawfiq et al., 2017; Sun et al., 2018).
2.3. Tetracycline-resistance genes
The tetracycline family (e.g. tetracycline, oxytetracycline, doxycy­
cline, tigecycline) is one of the most commonly used group of antibiotics
in veterinary and clinical settings to treat infections and as growth
promoters (Peiris et al., 2017; Zhang et al., 2015). Zhang et al. (2015)
ranked tetracycline as one of the top five antibiotics used in China in
2013. High concentrations of tetracyclines have been detected in live­
stock manures (Qian et al., 2018), WWTPs (Grabert et al., 2018), soil
(Xiang et al., 2016) and aquatic environment (Hoa et al., 2011). Anti­
biotic resistance genes include those responsible for the
energy-dependent efflux pumps (tetA, tetC, tetG, and tetK), ribosomal
protection proteins (tetM, tetO, tetQ, and tetW) and those involved in
enzymatic inactivation or modification (tetX) (Kim et al., 2016; Mao
et al., 2015). High usage and presence in the environment has led to
increased tetracycline resistance in some enterics, such as Aeromonas,
Enterobacter, Salmonella and Klebsiella species (Akiyama et al., 2013; Lee
and Wendy, 2017).
2.1. Carbapenem-resistance genes
β-Lactams (penicillins) were the first antibiotic family studied and
have long been used to treat serious bacterial infection of humans. These
antibiotics bind to penicillin binding proteins and inhibit peptido­
glycan/cell wall synthesis of both Gram-positive and Gram-negative
bacteria. Carbapenems are important last line of β-lactams used to
treat multidrug resistant enterics. However, carbapenems resistant
Enterobacteriaceae (CRE) are increasingly being detected in farm ani­
mals, possibly due to the overuse of antibiotics and other chemicals in
animal farms (Köck et al., 2018). CRE are well-studied ‘core’ component
of the resistome on farms and in the environment (Potter et al., 2016;
Suay-García and Pérez-Gracia, 2019). Additionally, CRE are becoming a
major health issue in humans. Increased incidents of extended spectrum
β-lactams (ESBL) resistant enterics, such as those against third genera­
tion cephalosporins, has made carbapenems the frontline drugs in the
control of ESBL-producing enterics (Wilson and Török, 2018).
Carbapenem resistance in enterics is due to the production of four
major carbapenemases; Class A β-lactamases, such as Klebsiella pneu­
monia carbapenemase (KPC), two Class B β-lactamases (New Delhi
metallo-β-lactamase (NDM) and Verona integrin-encoded metalloβ-lactamase (VIM)), and the Class D β-lactamases, such as oxacillinase48 (OXA-48) (Mills and Lee, 2019; Potter et al., 2016; Wilson and
Török, 2018). Other carbapenemase genes that play minor roles,
include: the Guiana extended-spectrum β-lactamase (GES) and the imi­
penem hydrolyzing β-lactamase (IMI) (Mills and Lee, 2019; Potter et al.,
2016). The four major carbapenemases have different properties and
geographical origins but all four have wide global distribution (Mills and
Lee, 2019). For example, KPC, a serine β-lactamase, was originally
discovered in K. pneumoniae but is widely distributed in the western
hemisphere (Mills and Lee, 2019). NDM is a metallo-β-lactamase that is
dominant in parts of Asia and is resistant to β-lactamase inhibitors. VIM
is mostly found in Europe (e.g. Italy and Greece) and OXA-48, which
cleaves oxacillin besides penicillin, is mostly found in North Africa and
Europe. Most carbapenemase genes are plasmid-borne, which makes
them spread efficiently in the microbiome. (Mills and Lee, 2019; Wilson
and Török, 2018). The major ARG subtypes include blaKPC, blaNDM,
blaVIM, and blaOXA. Other genetic determinants that contribute to car­
bapenem resistance include mutations in efflux pumps or porins that
change the bacterial membrane permeability against the uptake of these
2.4. Sulfonamide-resistance genes
Sulfonamides, first introduced in 1935, are a family of synthetic
antibiotics commonly used to treat bacterial and protozoa infections in
farm animals and humans. Sulfonamides (such as sulfadiazine and sul­
fadimethoxine), inhibit dihydrofolic acid biosynthesis in Gram-negative
and Gram-positive bacteria by binding to and inhibiting dihydropter­
oate synthase (Baran et al., 2011). High concentration of sulfonamide
residues and their corresponding ARG subtypes, sul1, sul2, sul3 and sulA,
have been detected in different habitats (Xu et al., 2015). The ARGs,
sul1, sul2 and sul3, are prevalent in WWTPs effluents (Xu et al., 2015),
livestock farms (Perreten and Boerlin, 2003), agricultural soils (Ji et al.,
2012), rivers (Jia et al., 2018) and nearshore systems. Although sulA is
rarely detected in the environment (Li et al., 2016), others such as sul1,
sul2, and sul3 are significantly correlated with each other, and are
noticeably correlated to mobile genetic element (MGEs) (Yu et al.,
2016). Thus, the ‘hot spots’ of sul genes and MGEs in WWTPs and live­
stock farms, may be reservoirs for the dissemination of the sul genes
among bacteria (Phuong Hoa et al., 2008; Poey et al., 2019).
3. Distribution and diversity of ARGs in the environment
Antibiotics use has increased globally over the years. A study
covering 76 countries between 2000 and 2015, showed that antibiotic
consumption increased by 65% (Klein et al., 2018). Monitoring ARGs in
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Environmental Pollution 285 (2021) 117402
Cameron, 2019; Sultan et al., 2018).
In order to determine the distribution and diversity of antibiotics and
ARGs in different habitat in different countries, we applied an Entrez
package embedded in Biopython to query the PubMed to fetch the
literature information from last 30 years (1990–2020). The 496,640
papers retrieved were subjected to the Natural Language Processing
(NLP) step using two packages (NLTK and spaCy) to extract titles and
abstracts from the literature (Steven Bird and E.L, 2010; https://github.
com/explosion/spaCy). The process is outlined in the appendix (Suppl.
Fig. 1). The rule-based entity recognition was used to recognize habitats
and country names in the titles and abstracts. Only 9374 papers passed
the filtering and were analyzed further to obtain the ARG ID, AMR Gene
the environment can help us understand the extent of antibiotics accu­
mulation in different habitats (Chen et al., 2017). However, researchers
hold different opinions on whether or not there is a positive correlation
between antibiotics accumulated in the environment and the presence of
their respective ARGs (Jia et al., 2018; Wu et al., 2015). Nonetheless,
studies on ARGs in the environment can give us an alternative approach
to estimate the movement of antibiotics from the clinical and farm
habitats to the other environments (Fig. 1). Antibiotics in the environ­
ment may introduce selective pressures on bacteria lead to the prolif­
eration of ARB especially when the fitness cost is low (Bengtsson-Palme
et al., 2018). ARB are important disseminators of ARGs in sub-resistomes
and their plasmids play important roles in HGT (Lerminiaux and
Fig. 2. Frequency of reports on the top 50 ARG
subtypes, and their respective antibiotic families
extracted from PubMed publications from the last 30
years (1990–2020). The 16 major antibiotic families
were modified from dos Santos et al. (2017): AF,
amphenicol; AG, aminoglycoside; BL, β-lactam; FQ,
fluoroquinolone; GP, glycopeptide; LS, lincosamide;
MC, macrolide; MDR, multidrug; PA, phosphonic
acid; PP, polypeptide; PY, polymyxin; RY, rifamycin;
SF, sulfonamide; TET, tetracycline; TMP, trimetho­
prim; and others.
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Environmental Pollution 285 (2021) 117402
Family, Drug Class, and Resistance Mechanism from CARD database.
The Drug Class was re-annotated/classified into 16 antibiotic families
(dos Santos et al., 2017). The frequency of the ARG subtypes was
counted by the number of appearances in published papers with only
one count for each paper to avoid double counting (Suppl. Fig. 1).
NLP results returned a total frequency of 91 ARG subtypes out of
around 3000 ARG subtypes (from CARD) and covered 80% of all
retrieved publications (9374 papers). The top 50 ARG subtypes in all six
habitats in the environment are given in Fig. 2. The top 50 ARG subtypes
covered eight antibiotic families: β-lactam (bla), sulfonamide (sul),
tetracycline (tet), aminoglycoside (aad), multidrug (mec), amphenicol
(flo), trimethoprim (dfr), and glycopeptide (van). Among the top 15 ARG
subtypes reported, mecA (multidrug) was the most globally (1000 pa­
pers), followed by β-lactam (bla), glycopeptide (vanA, vanB), sulfon­
amide (sul1, sul2), and tetracycline (tetM).
The top 15 ARG subtypes in publications from six continents are
listed in Fig. 3. The top ARGs covered the same seven out of eight
antibiotic families given in Fig. 2 (except the amphenicol family). Most
of the ARG subtypes were reported in publications from Asia, followed
by Europe, Africa, North America, South America, with Oceania
reporting the fewest publications. The top 10 ARGs reported in Asia
included blaNDM-1, blaCTX-M-15, mecA, blaTEM-1, sul1, vanA, blaKPC-2, sul2,
blaCTX-M-14, and blaOXA-48 (responsible for β-lactam, multidrug, sulfon­
amide, and glycopeptide antibiotics). Similar trends were reported in
publications from the other five continents, suggested common antibi­
otics usage globally.
When the publications were divided according to ARG subtypes
presence in the six habitats from Fig. 1, reports from hospitals were the
most common, followed by those from aquaculture and agriculture
farms. The reported ARGs profile in hospitals were unique and differed
from those in other habitats. The most commonly reported ARGs in
hospitals were mecA, vanA, vanB, and bla (covering the multidrug,
glycopeptide, and β-lactam families). Upon further analysis, similar
ARGs were found in hospitals across all continents, except Oceana
(Suppl. Fig. 2). Reported ARGs profiles from farms were like those in
WWTPs, water and soil with sulfonamide (sul) and tetracycline (tet) ARG
types being the most common. Reuse of WWTPs effluents to irrigate
farms is increasingly becoming common in some countries, which may
further compound the ARGs differences between the different habitats
(Helmecke et al., 2020; Kampouris et al., 2021). Very few publications
on the ARGs in air were found and the ARG subtype composition
differed greatly from those in other habitats. Overall, these results show
differences in antibiotic use in clinics and hospitals versus farms. There
were more publications on ARGs belonging to the multidrug, glyco­
peptide, and β-lactam families in hospitals whereas there were more
publications on sul and tet families in farms, WWTPs, water and soil
(Fig. 4). Antibiotic usage on farms and aquaculture may affect the di­
versity of ARG types in WWTPs, soil and water.
The data from publications gave information on the presence of
antibiotic, ARB, and ARGs in different habitats from many different
geographical areas. This data was based on words mentioned only in the
titles and abstracts. In order to further understand the diversity and
distribution of ARG subtypes, we manually screened 46 representative
papers from five continents (minus South America) and covering six
habitats (Table 1). Papers were purposely chosen to include at least two
metagenomics reports per country/region in addition to qPCR papers.
Metagenomics studies cover more ARG subtypes than PCR studies.
Papers were chosen on publications from five different continents;
Asia (China), North America (USA), and Oceania (Australia), Europe
(several countries/regions), and Africa (several countries) (Table 1).
Information on the top 15 ARG subtypes from metagenomics papers and
the top 10 ARG subtypes from qPCR papers representing the major
antibiotic families from the five countries/regions are given in Table 2.
The data were like the seven major antibiotic families reported in Fig. 3
(representing β-lactams, multidrug, and sulfonamide families). Howev­
er, tetracycline and aminoglycoside were over-represented whereas,
glycopeptide and trimethoprim were under-represented in Table 2
compared to what is reported in Fig. 3. This discrepancy may be due to
the small sample size, only 46 papers were used for Table 1. Nonetheless,
both the NLP and manual processing of the 46 papers showed similar
ARG subtypes corresponding to seven major antibiotic families across
different countries and regions. Furthermore, the results in Table 1 and
Fig. 4 suggested the presence of different sub-resistomes in different
habitats such as hospitals, farms, WWTPs, soil, and water. For example,
ARGs belonging to the sulfonamide and tetracycline families were more
prevalent in farms, WWTPs, soil, and water than in clinics and hospitals.
These findings suggest interconnectivity between the six sub-resistomes
in the environment in different countries/regions on different conti­
nents. Therefore, ARGs dissemination is a global problem that has no
borders or boundaries.
4. Tools used to study the resistome
One of the major hindrance to resistome and ARGs research is the
Fig. 3. Frequency of reports on the top 15 ARG subtypes, grouped by continents, in PubMed publications from the last 30 years (1990–2020).
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Environmental Pollution 285 (2021) 117402
Fig. 4. Frequency of reports on the top 15 ARG subtypes, grouped by habitats, in PubMed publications from the last 30 years (1990–2020).
fact that only a small fraction of microbial species can be cultured and
grown in the lab (Schmeisser et al., 2007). Since some microbial species
harboring ARGs are viable but non-culturable, next generation DNA
sequencing (NGS) and other ‘omics’ tools are especially useful in resis­
tome research. Many mechanistic studies about the resistome are still
done only on culturable species (Windels et al., 2019) or by computa­
tional modeling (Kumar et al., 2019; Vetsigian, 2017; Winkler, 2018).
Moreover, we propose that future studies find and define quantitative
indicators (e.g. specific microbes, genes, proteins, or metabolites). These
indicators can be used to measure and quantify the risks associated with
resistomes.
One common tool used to study the resistome is the 16S rRNA genes
profiles using PCR. The 16S rRNA genes are used as taxonomical identity
markers in computational analysis to link ARGs profiles to microbial
population. One downside to using taxonomical markers in resistome
research is the lack of sequence depth; most markers give a good esti­
mation only up to the family or the genus level but do not go down to the
species level (Zaheer et al., 2018). Additionally, it is difficult to identify
active operational taxonomic unit (OTU). Nonetheless, 16S rRNA gene
sequence information is useful in defining the core microbial commu­
nity. For example, Yan et al. (2019) used high-throughput qPCR to
identify 217 ARGs in green spaces inside urban areas and found Pro­
teobacteria to be one of the most dominant phylum. Su et al. (2017)
combined 16S rRNA with metagenomics to study the resistome of
several urban sewage facilities across China. They found the sewage
facilities to be dominated by the phyla Proteobacteria, Bacteroidetes,
Firmicutes, and Fusobacteria. Thus, even though 16S rRNA genes on
their own cannot provide comprehensive information, an integrated
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Environmental Pollution 285 (2021) 117402
Table 1
The major ARG subtypes found in different habitats in different countries/regions from five continents.
Habitatsa
Major ARG Subtypesb
Method used (Total
ARG subtypes
reported)
Samples Type; Location
Antibiotic families (Based
on Total ARG subtypes)
Reference
cfxA, aacA, ermB, ermD, tetQ, tetW, tetO,
sul2, tet32, tolC
tetQ, ermF, ermB, cfxA2, bacA, aph(3′ )-I,
tetW, tetX, tetM, class A β-lactamase, tetO,
aadE, tet40, ermC, aph(3′ )-III
tet36, tetM, tetQ, ermF, tetX, tetP(A), mefA,
tet40, sul1, aac(6)-Ie, sul2, tetS, tetP(B), ant
(6)-Ia, ermB
sul1, sul2, tetQ, tetM, tetW, tetO, tetA, tetB,
tetC, tetL, tetG, tetX
mexB, mexF, mexW, mexD, acrB, oprN,
adeB, mexE, emrD, mdtG, mdtF, tolC, bcR,
acrA, mdtH
sul1, sul2, tetA, tetC, tetO, tetS, tetX, aacA1,
blaTEM-1, blanps-1
sul1, sul2, tetW, tetX, ermA, ermB, blaTEM,
ampC, cat, cmr
sul1, tetC, aph(6′ )-ld, dnaK, aph(3′ )-IIb, ant
(3′ )-Ib, ant(2′′ )-I, ruvB, copB, actP, acc(6′ )Ib, sul2, blaTEM, blaOXA, arsB
sul1, sul2, tetA, tetB, tetG, tetM, tetQ, ermB,
tetO, ermA
blaTEM, emrD, ereA, floR, mexF, strB, sul2,
tetM, tetX, vanC
sul1, sul2, sul3, tetA, tetM, tetC, qepA,
blaOXA-1, blaTEM-1, blaOXA-2
ksgA, bacA, ftsI, mrcA, acrB, acrA, aacA,
tetA, mrdA, mdtC, pbpG, otrB, dacC, mdtK,
mdlB
aac(6′ )-IB-CR, sul2, tetA, tetX, tetW, sul1,
ermF, blaTEM
mexF, acrA-04, blaOXY, vanC-03, ermB,
blaCTX-M-04, mphA-01, blaTEM, aphA1, ermA
blaTEM, tetW, sul3, tetQ, tetM, ermB, tet32,
tetO
blaTEM, ermB, lnuA, tetW, tetL, sul1, aph
(6′ )-IIIa, cmr-II, tetQ, tetO, sul2, mphE, ant
(6)-la, ermT, lnuC
qPCR (122)
Fecal samples from 73 individuals;
Jiangsu and Liaoning
Fecal samples from 23 healthy
individuals; China
TET (10), AG (4), PP (1), BL
(3), SF (1), MDR (6)
Duan et al. (2020)
Metagenomics (79)
Wastewater from 3 swine farms;
Jiangmen, Guangdong
TET (23), SF (6), MDR (16),
BL (2), MC (1), AG (3)
qPCR (12)
Fecal samples from 8 livestock
farms; Hangzhou
Water samples from shrimp farm;
Jiangsu
Cheng et al. (2013)
Sediment samples from a frog
farm; Shantou
Sludge from pharmaceuticals and
hospitals; Northern China
Seeding sludge from municipal
WWTPs; Hangzhou
Yuan et al. (2019)
China
Humans
Farms
WWTPsc
Water
Soil
Air
Metagenomics (507)
Metagenomics (102)
qPCR (20)
qPCR (11)
Metagenomics (82)
qPCR (18)
Feng et al. (2017)
He et al. (2019)
Zhao et al. (2018)
TET (3), SF (4), BL (5), AG
(5), AF (2), MDR (2)
TET (12), SF (6), GP (1), AG
(2), BL (8), MDR (8), AF (1)
Dong et al. (2019)
Wu et al. (2018)
qPCR (40)
Sediments from 15 lakes and
reaches; Yangtze River
Water samples; Yellow River
Yang et al. (2017)
qPCR (28)
Surface sediments; Haihe River
Tan et al. (2018)
Metagenomics (71)
Water samples from upper and
middle branches; Huai River
Bai et al. (2019)
qPCR (8)
qPCR (135)
Surface soil from 7 agricultural
fields; Tianjin
Surface soil samples; Shandong
TET (3), SF (2), GP (1), MC
(2), AG (2), MDR (5), BL (5)
qPCR (39)
Air sample; Beijing
Metagenomics (280)
Air sample; Beijing
TET (9), SF (3), MC (4), AG
(2), BL (2), AF (1), MDR (2)
tetW, tetQ, bacA, vanR, tetM, tetO, ermB,
aadE, cfxA2, ompF, acrB, vanS, tet32, bcrA,
macB
tetQ, tetW, mefA, lnuC, tet40, aadE, ermF,
tetO, tet44, ermB, cfxA, aph(3′ )-IIIa, cfxA6,
tetX, tetL
sul1, tet32, tetM, tetO, tetT, tetW, aadA1,
aadA2, catA1, emrB, matA, mefA, msrA
sul1, tetM, blaOXA-58, blaTEM, blaOXA-48,
blaCTX-M-32, mcr-1, blaCTX-M-15, blaKPC-3
sul1, ermB, sul2, qnrS, tetM, blaCTX-M
Metagenomics (507)
Fecal samples from 54 healthy
individuals; France, Germany,
Iceland, and Sweden
Fecal samples from 176 pig farms;
9 European countries
qacEδ1, sul1, aadA, strB, blaOXA, ermF,
sul2, tetW, qacH
sul1, sul2, ermB, dfrA1, tetC, ampC, blaSHV,
tetB, cat2, tetA
sul1, ermB, tetM, blaSHV, ermF, blaOXA, tetB,
aph3a
tetM, tetW, ant(6)I, ant(3′′ )Ia, ant(3′′ )Ib,
shv, ermC, arr
aacA-aphD, ermA, tetK, mecA, vatB
qPCR (384)
Shi et al. (2019)
Wu et al. (2020)
Chen et al. (2016)
Zhang et al. (2019)
Pal et al. (2016)
Europe
Humans
Farms
WWTPs
Water
Soil
Air
Metagenomics (312)
qPCR (28)
qPCR (9)
qPCR (8)
PCR(24)
Fish tissues from fish farms;
Finland
Wastewater from 16 urban
WWTPs; 10 European countries
Wastewater from 62 WWTPs;
Denmark
Wastewater samples; 7 European
countries
Surface water; Rhine River,
Danube River, and tributaries
Water samples; Rhine River
TET (5), PP (1), GP (2), MC
(1), AG (1), BL (2), MDR (2),
Others (1)
TET (12), SF (1), AG (4), AF
(1), BL (2), MDR (4), MC
(3), Others (1)
Feng et al. (2017)
TET (3), SF (5), AG (1), PM
(1), BL (8), AG (1), FQ (2),
MDR (2), Others (1),
Cacace et al. (2019)
Van Gompel et al.
(2019)
Muziasari et al. (2017)
Pallares-Vega et al.
(2019)
Pärnänen et al. (2019)
TET (5), SF (3), TMP (1), AF
(1), BL (4), AG (1), MDR (3)
Stoll et al. (2012)
Soil samples from 6 farms;
Lithuania
Bioaerosol in indoor air; Southern
Poland
TET (2), AG (3), BL (1), RY
(1), MDR (1)
TET (1), MC (2), AG (1), BL
(1)
Armalytė et al. (2019)
Metagenomics (183)
Fecal samples from 8 healthy
individuals; USA
TET (6), GP (2), PP (1), AG
(1), BL (1), MC (1), MDR (1)
Feng et al. (2017)
Metagenomics (50)
Compost samples from a poultry
farm; Vermont
TET (3), BL (2), AG (6), SF
(1), MC (1), MDR (1)
Eckstrom and Barlow
(2019)
Metagenomics (408)
Urban sewage samples; USA
qPCR (8)
PCR (8)
PCR (5)
Paulus et al. (2020)
Brągoszewska and
Biedroń (2018)
USA
Humans
Farms
WWTPs
tetW, tetQ, bacA, tetM, tetO, vanR, aadE,
ermB, CfxA2, class A β-lactmase, tet40,
tet32, vanS, bcrA, macB
blaCARB 8, blaCARB 10, aph(3′ )-Ic, strA,
aadA24, aph(6′ )-ld, tet39, aph(3′ )-IIa,
lsaE, tetL, tetW, lnuB, sul2, aadA1, aadA6
Hendriksen et al. (2019)
(continued on next page)
7
M. Zhuang et al.
Environmental Pollution 285 (2021) 117402
Table 1 (continued )
Habitatsa
Major ARG Subtypesb
Water
msrE, msrD, mphE, tetW, tetO, ermB, mefA,
tetQ, tetM, blaOXA, tet39, sul1/3, aadA,
tetX, mefB, tetC
ermB, tetA, tetW, tetX, mecA
sul1, tetA, tetX, tetW, blaIMP, blaKPC, blaOXA-
Method used (Total
ARG subtypes
reported)
48
Samples Type; Location
Antibiotic families (Based
on Total ARG subtypes)
Reference
TET (7), BL (1), MC (2),
MDR (3), AG (1), SF (1)
PCR (5)
PCR (7)
Raw and treated sewage; USA
Water samples; Bayou Lafourche
TET (3), MDR (2)
TET (6), SF (2), BL (3), MDR
(1), FQ (1)
Soil
tetA, tetW, tetX, ermB, sul1, qnrA
blaSHV, sul1, ermF, ermB
qPCR (6)
qPCR (4)
Water samples; Mississippi River
Soil from parks; California
Air
tetM, tetQ, tetO, tetW, tetB, tetL
qPCR (6)
TET (6), BL (1), SF (1)
blaSHV, sul1
qPCR (2)
Particulate matter samples from
beef cattle feed yards; Texas
Air from parks; California
aadA1, blaSHV, blaSHV(156G), blaSHV
(238G240E), ermA, ermB, mefA, tetA, tetB
aac3-1, tetA, acrA, ampC, pKD13, ermB,
tetB, ermA, aadA1, ermC, mphA, aadA14
msrE, sul1/3, tetQ, tetW, aadA, mphE,
blaOXA, tetM, tetO, msrD, tetA, sul2, strB,
tet32, tetC
sul1, aadA, aadA13, aadA15, ant(6)-Ia,
aph(3′′ )-Ib, sul2, tetW, tetS, tetO, tetB(P),
mefA, ermB, catQ, ant(6)-Id
ermB, tetM, tetL, vanC, aph(3′ )-IIIa, aac
(6′ )-Ie-aph(2′′ )-Ia, erfA, erfB, emeA, lsa
tetA, tetB, sul1, sul2, tetC, tetQ, sul3
qPCR (84)
Stool specimens from children;
Maputo, Mozambique
Cloacal samples from chicken
farms; Khartoum, Sudan
Urban sewage samples; 10 African
countries
TET (2), AG (1), BL (3), MC
(1), MDR (2)
TET (2), AG (3), MDR (4),
BL (1), MC (1)
TET (13), SF (4), GP (1), AG
(8), AF (1), BL (1), MC (2),
MDR (6)
sul1, sul2, aadA15, aadA13, aadA, blaLCR-1,
aac(3)-Ia, tetW, blaOXA-347, tetC, mefC, aph
(6)-Ib, aadA16, dfrA1, aph(3′′ )-Ib
Metagenomics (45)
bacA, tetW, tetO, vanR, tetM, ermB, aadE,
vanS, tet32, macB, bcrA, ompF, tetQ, acrB,
aph(3′ )-I
aadA, aadA2, aadA3, strB, ermB, sul2, tetK,
tetM, tetW, tetX
msrE, mphE, blaOXA, tet39, ermB, strB,
aadA, strA, tetW, sul1/3, msrD, tetX, ant
(3′′ )-Ih-aac(6′ )-IId, blaGES, aac(3)-I_aac
(3)-Ia
sul1, sul2, cat2, dfrA1, ampC, ermB, tetC,
tetA, tetB, blaSHV
Metagenomics (240)
qPCR (89)
blaTEM, blaCTX, blaOXA, blaSHV, tet1, tet2,
tet3, tet4, tetM, tetW, sul1, sul2, sul3
BL (1), SF (1), MDR (2)
Naquin et al. (2015)
Bird et al. (2019)
LaPara et al. (2015)
Echeverria-Palencia
et al. (2017)
McEachran et al. (2015)
Echeverria-Palencia
et al. (2017)
Africa
Humans
Farms
WWTPs
Water
Soil
PCR (13)
Metagenomics (408)
Metagenomics (235)
Raw sewage samples from canals;
Yaounde and Ngaoundere,
Cameroon
Surface water and hospital
sewage; Gauteng, South Africa
Sediment samples; Awash River
Basin, Ethiopia
Irrigated and non-irrigated soil; 3
African cities
PCR (68)
qPCR (7)
Berendes et al. (2019)
Abdelgader et al. (2018)
Hendriksen et al. (2019)
Bougnom et al. (2019)
Hamiwe et al. (2019)
TET (4), SF (3)
Ergie et al. (2019)
TET (2), SF (2), AG (7), BL
(2), MC (1), TMP (1)
Bougnom et al. (2020)
Fecal samples from 16 healthy
individuals; Australia
TET (5), PP (2), GP (2), MDR
(3), MC (1), AG (2)
Feng et al. (2017)
Shed and manure samples from
chicken farms; Australia
Urban sewage samples; Australia
TET (4), AG (4), MDR (1), SF
(1)
TET (3), BL (2), AG (5), MC
(1), SF (2), MDR (2)
Liu et al. (2020a,
2020b)
Hendriksen et al. (2019)
PCR (24)
Surface water samples; Brisbane
River
Stoll et al. (2012)
PCR (13)
Soil samples from residential
areas; Western Australia
TET (3), SF (2), BL (1), AF
(1), TMP (1), BL (1), MDR
(1)
TET (6), SF (3), BL (4)
Australia
Humans
Farms
WWTPs
Water
Soil
a
b
c
Metagenomics (199)
Knapp et al. (2017)
The six habitats important in the transfer of ARGs. Refer to Fig. 1 for details.
The top 15 ARG subtypes (from metagenomics papers) and the top 10 ARG subtypes (from q-PCR papers) based on the relative detection frequency in publications.
WWTPs = wastewater treatment plants.
Table 2
The 16 antibiotic families derived from the ARG subtypes as reported from the five continents.a
Continent
AG
AF
BL
FQ
GP
LS
MC
PA
PP
PY
RY
SF
TET
TMP
MDR
Others
Asia
Europe
North America
Africa
Oceania
Total
18
11
8
19
1
57
4
2
–
1
–
7
25
18
9
7
8
67
–
2
1
–
–
3
2
2
2
1
2
9
–
–
–
–
–
–
7
6
4
5
2
24
–
–
–
–
–
–
1
1
1
–
2
5
–
–
–
–
–
–
–
1
–
–
–
1
22
9
6
9
8
54
60
28
31
23
21
163
–
1
–
1
1
3
39
12
10
12
7
80
–
3
–
–
–
3
Abbreviations of 16 antibiotic families: AG, aminoglycoside; AF, amphenicol; BL, β-lactam; FQ, fluoroquinolone; GP, glycopeptide; LS, lincosamide; MC, macrolide;
PA, phosphonic acid; PP, polypeptide; PY, polymyxin; RY, rifamyxin; SF, sulfonamide; TET, tetracycline; TMP, trimethoprim; MDR, multidrug; and others.
a
The numbers are based on the total ARG subtypes column in Table 1.
approach that combines 16S rRNA, metagenomics, and other tools, can
show bacterial interactions and dynamics in the resistome communities.
4.1. Metagenomics analysis
Metagenomics can reveal genetic information about the microbial
population without the need to culture microbes. Metagenomics is a
8
M. Zhuang et al.
Environmental Pollution 285 (2021) 117402
powerful tool for looking at the abundance of ARGs in the entire pop­
ulation, but it demands far more computational resources than 16S
rRNA gene analysis and hence is more challenging. A review by
Bengtsson-Palme et al. (2017) outlines specific bioinformatics oppor­
tunities on advanced tools specifically tailored for resistome study. It is
important to note that metagenomics analysis in resistome research is in
its infancy and is constantly changing. New sequencing technologies,
such as nanopore sequencing, are constantly being upgraded for resis­
tome research (Che et al., 2019); (Bertrand et al., 2018; Gupta et al.,
2019).
One challenge in using NGS techniques to study the resistome is the
fact that a significant number of ARGs are found on mobile genetic el­
ements (MGEs). This makes it harder to connect specific sequences with
specific genomes. MGEs include plasmids, virus segments (e.g. bacte­
riophages), transposons, and integrons that can move between and
within living cells (Lerminiaux and Cameron, 2019; Sultan et al., 2018).
Che et al. (2019) compared metagenomics sequencing with cultural
dependent validation techniques and showed that ARGs associated with
MGEs dominated the WWTPs resistome and their relative abundance
increased in the effluents. Ma et al. (2015) also used metagenomics tools
to show that many ARGs, such as macA-macB, tetA-tetR or those carrying
class 1 integron aadA5-dfrA17, were shared across different fecal habi­
tats (chicken, pig, and human). Although metagenomics is a powerful
tool to study the resistome, one important limitation is that the presence
of a gene does not necessarily mean that it is functional or is expressed
by the cells. To bridge this data gap, metagenomics tools should be
combined with additional tools, such as functional metagenomics.
2018; Cardoso et al., 2018; Erickson et al., 2017). The technique will
only grow in popularity with advancements in total RNA purification
techniques from environmental samples and as RNA sequencing costs
drop. Recent studies have shown the advantage of using metatran­
scriptomics in resistome research. Marcelino et al. (2019) used meta­
transcriptomics studies of anthropogenic resistome to show a positive
correlation between common antibiotic residues in WWTPs and the gut
microbiomes of migrating birds. In another study, Liu et al. (2019) used
metatranscriptomics and metagenomics analyses to show the presence
of ARGs in WWTPs and the important connection between MAR gene
transcription and the hosts. Specifically, the authors reported the pres­
ence of MAR genes that harbor 14 and 50 ARGs in the genus Mycobac­
terium and the family Burkholderiaceae respectively. Moreover, the
importance of distinguishing between a ‘dormant’ resistome and an
‘active’ resistome was demonstrated by showing that more than 60% of
the recognized ARGs were actively translated in wastewater (Liu et al.,
2019). A major disadvantage of metatranscriptome studies is that RNA
expression may not correlate with protein levels. Finding and defining
‘dormant’ or ‘active’ resistomes require additional tools such as meta­
proteomics and metabolomics.
4.4. Metaproteomics and metabolomics analysis
Protein studies can provide deep insight into enzyme activity and
protein function in microbial cells in the resistome. Studies have shown
that metaproteomics and metabolomics analysis of isolated culturable
bacteria can provide significant information about specific ARGs char­
acteristics (Hoerr et al., 2016; Schelli et al., 2017; Vranakis et al., 2014).
There are few metaproteomics and metabolomics reports on antibiotic
resistance in microbial populations in the environment. Unlike meta­
genomics and metatranscriptomics studies that are accelerated by ad­
vances in NGS, metaproteomics and metabolomics rely on technologies
that advance slowly, such as two-dimensional or differential in-gel
electrophoresis techniques, liquid chromatography with mass spec­
trometry or MALDI-TOF (Vranakis et al., 2014). However, meta­
proteomics was used to evaluate different methods of managing cow
manure in order to minimize the spread of ARGs (Zhang et al., 2020).
These studies showed that bacteria with greater relative abundance in
the metagenomes also produced more proteins, including proteins
encoded by ARGs, in manure samples. Y. Liu et al. (2020) also used
metaproteomes to measure bacterial abundance in the human gut
microbiomes in response to different chemicals, including 4 antibiotics.
Identified bacterial proteins were used to analyze the microbiome
composition and some functional pathways. The metaproteomics plat­
form thus present compelling evidence for resistome studies, not only at
the gene level but also at the protein level. Metabolomics studies often
use mass spectrometry techniques but may include techniques based on
nuclear magnetic resonance (NMR) (Wishart, 2019).
In order to make these techniques relevant to future resistome
studies, advancements in high throughput sampling techniques are
required. Although limited in scope, use of metaproteomics and
metabolomics analysis in resistome research can provide answers to
questions about mechanisms involved in resistome development. These
studies can give an indication to which ARGs are active at any time and
which active ARGs are related. Additionally, assessing the resistome’s
‘intensity’ in the environment based on the changes in metabolic profiles
may be possible. In other words, do different stressors, such as antibi­
otics, affect the composition of the resistome? For example, metatran­
scriptomics and metabolomics studies were used to show the inhibitory
effects of persimmon tannin on methicillin-resistant Staphylococcus
aureus (MRSA) (M. Liu et al., 2020). The authors showed that 370 genes
and 19 metabolites were differentially expressed in MRSA and the
changes in the metabolome were comparable to changes in the
transcriptome.
A holistic view of the resistome will only emerge from an integrated
approach that includes many different techniques. Moreover, as
4.2. Functional metagenomics analysis
Functional metagenomics not only validate the presence of known
ARGs biochemically but can also be used to identify novel ARGs (Perry
and Wright, 2014). Unlike metagenomics, functional metagenomics
studies do not need prior knowledge about the type of ARGs in an
environment. The main techniques include simulations of the growth
environment, such as growth in diffusion chambers, or expressing genes
from metagenomic clones into surrogate hosts for accurate biochemical
studies. These techniques can be applied to non-culturable organisms
and are therefore independent of cultural biases (Perry and Wright,
2014). New information can then be curated in databases. Although
functional metagenomics is a powerful tool to study novel and known
ARGs, a major limitation is that the ARGs are taken “out of context” and
introduced into surrogate expression systems. The disruption of the
natural environment in functional metagenomics is much greater than
that in other techniques such as 16S rRNA gene sequencing and meta­
genomics (dos Santos et al., 2017). Furthermore, to overcome the bias of
sequencing and functional metagenomics techniques, recent studies
have demonstrated the great potentials of culturomics in the rapid
identification of bacteria (Nowrotek et al., 2019). Culturomics use
high-throughput tools such as matrix-assisted laser desorption ioniza­
tion time of flight mass spectrometry (MALDI-TOF) to comprehensively
identify many bacteria from environmental samples to the strain tax­
onomical level (Nowrotek et al., 2019). Combined functional meta­
genomics and culturomics techniques will likely drive future resistome
research, especially in combination with third generation sequencing or
Nanopore sequencing techniques (Van Der Helm et al., 2017).
4.3. Metatranscriptomics analysis
Metatranscriptomics techniques require the isolation and purifica­
tion of RNA from environmental samples to give an indication of active
genes in the microbial community. The sequencing cost for RNA per
sample is higher than that for DNA and this may limit the wider appli­
cation of metatranscriptomics in resistome studies. However, meta­
transcriptomics is an important tool that has been applied to
unculturable bacterial species in the environment (Brochmann et al.,
9
M. Zhuang et al.
Environmental Pollution 285 (2021) 117402
suggested by Nowrotek et al. (2019), combining the ‘omics’ tools with
classical microbial research tools can only improve our understanding of
the resistome. Such an approach is resource intensive and we suggest the
establishment of a multinational interdisciplinary group that can cover
the wide range of techniques and expertise needed for such an under­
taking, similar to the successful story of the Human Genome Project in
the early 2000’s. Such an undertaking can bridge the gap that exists
today between ‘structure and function’ in resistome research and from
qualitative data to also include quantitative data. This will enable sci­
entists to create models that can be used by the health and environ­
mental sectors to monitor, control and prevent the spread of antibiotic
resistance.
reduced the abundance of Enterobacteriaceae, the main carrier of ARGs in
chicken (Xiong et al., 2018). Klümper et al. (2019) also reported that
minimal selective concentrations of gentamicin and kanamycin
increased when E. coli was embedded in a bacterial community. It is vital
that we understand the effect of antibiotics use on the resistome of
livestock on farms due to the tight connection between livestock and
humans. A recent broad-range survey in Europe found resistome profiles
of urban wastewater treatment plants to be similar to those found in
clinics (Pärnänen et al., 2019). Therefore, reducing ARGs in clinics can
have a positive impact on ARGs in the urban wastewater treatment
plants resistomes and other resistomes.
Recent studies have highlighted the need for models that will predict
emerging ARGs using machine learning and need for new laboratory
techniques to detect novel ARGs (Arango-Argoty et al., 2018). Yet, more
effort is required to curate the models and information. These stan­
dardized models, once accepted by the scientific community, will
facilitate surveillance efforts on the transfer of new ARGs between
bacteria, including clinically relevant pathogens. Thus, many different
studies, surveys, and/or simulations using unified parameters for easy
comparison in different countries/regions, are required to help us un­
derstand and accurately model resistome behavior.
5. Monitoring ARGs and factors that affect resistome
development
HGT is believed to be faster in providing crucial genes for bacterium
survival rather than spontaneous mutations (Lerminiaux and Cameron,
2019). This type of gene transfer is mediated by MGEs, plasmids being
the main elements in the formation and spreading of ARGs (Sultan et al.,
2018). For example, transfer of ARGs for common antibiotics such as
β-lactams, glycopeptides, and quinolones is plasmid-mediated (Sultan
et al., 2018). Many factors affect the development and evolution of
resistome. HGT is widely accepted as the key driver in the spread of
ARGs in the environment (Soucy et al., 2015). Antibiotic selective
pressure can also increase the volume of ARGs in the environment and
accelerate the evolution of resistome (Chen et al., 2019; Qiao et al.,
2018). The probability of ARGs evolution increases rapidly in ‘hot spots’
of resistomes (habitats where endogenous microbial populations
encounter sub-lethal dose of antibiotics). Apart from accumulation of
antimicrobials (Zeineldin et al., 2019), the resistome is also shaped by
many different external factors that include plants (Lu and Lu, 2019),
manure (Zhu et al., 2013), biochar (Duan et al., 2017), struvite (Chen
et al., 2017), heavy metals (Ji et al., 2012), sewage sludge (Burch et al.,
2016), and aromatic compounds (Xia et al., 2019).
5.2. Factors affecting resistome development
Endophytic bacteria that harboring ARGs can colonize plant and may
persist throughout the vegetable growth stage (Pu et al., 2019). Addi­
tionally, increased occurrence of antibiotic resistance bacteria and
increased relative abundances of ARGs were reported in endophytic
systems of pakchoi (Brassica chinensis L.) exposed to antibiotics (Zhang
et al., 2017). The fern, Azolla imbricate, was also shown to effectively
reduce the number and diversity of ARGs in soils by eliminating anti­
biotics and available heavy metals (Lu and Lu, 2019).
Organic fertilizer (manure) is widely used to improve the organic
matter and nutrient contents of soils used for crop production. However,
studies have shown that these organic fertilizers can introduce ARB and
ARGs to the soils (Xie et al., 2018). Additionally, ARB and ARGs from
organic fertilizers have been detected in crops grown on soils enrich
with organic fertilizers (Marti et al., 2013). The application of sewage
sludge to soil was found to boosts the evolution and dissemination of
ARGs in the soil-plant system (Yang et al., 2018a, 2018b). For example,
vegetable grown in soils fertilized with animal manure had more ARGs
upon harvested (Marti et al., 2013; Rahube et al., 2014). It is important
to note that the presence of some bacteria in manure can actually reduce
the abundance of pathogenic bacteria and ARGs. For example, Duan
et al. (2019) found that inoculating composting manure with Bacillus
subtilis decreased the relative abundance of ARGs, MGEs and pathogenic
bacteria. This result is not surprising because all bacteria compete for
the limited resources.
Biochar, a plant-based charcoal used as a farmland soil amendment,
has been shown to effectively reduce the abundances of antibiotics, ARB,
and ARGs (Duan et al., 2017). Struvite, a phosphate mineral recovered
from wastewater that is used as a phosphorous and nitrogen source in
organic fertilizer. However, the application of struvite was found to
increase the abundance and diversity of ARGs in the rhizosphere and
phyllosphere of plants such as Brassica (Chen et al., 2017). The potential
risks associated with struvite application to soils are not well-understood
since antibiotics and ARGs appear to be enriched in the struvite modified
soils (Chen et al., 2017). Heavy metals, such as Pb, Cd, Cr, Ni, Hg, Cu,
and Zn, have been reported to exert a strong selection pressure on the
abundance of ARG in manure, wastewater, soil, and landfill leachates
(Gao et al., 2015; Ji et al., 2012; Wu et al., 2015; You et al., 2020). These
findings suggest that heavy metals can alter bacterial communities, in­
crease the abundance of ARGs, and lead to the spread of ARGs in the
environment.
Studies have shown that sewage sludge, a by-product of WWTPs, is a
reservoirs of many ARGs (Burch et al., 2016; Su et al., 2015) and has
5.1. Monitoring ARGs in the environment
Hot spots of resistomes in urban areas such as WWTPs (Liu et al.,
2019), intensive animal farms (Cheng et al., 2013) and hospitals
(Rodriguez-Mozaz et al., 2015) are usually under extensive surveillance.
However, there is a high probability that ARGs evolve rapidly in these
hot spots due to the accumulation of antimicrobials and other factors
that can co-select ARGs (Ji et al., 2012). It has been proposed that
WWTPs are favorable places for taxonomic shifts for strains possessing
intrinsic resistance or chromosomal mutations rather places for
acquiring MGEs (Bengtsson-Palme et al., 2019). For example, although
anaerobic digestion, a key step in some WWTPs, reduced most human
bacterial pathogens by 70–95%, the process only decrease ARGs by
20–52% (Ju et al., 2016). Effluents from these hot spots contain an
abundance of ARGs that can inevitably accelerate the evolution of the
environmental resistome. This evolution can facilitate the acquisition of
ARGs by human pathogens under antibiotic selection pressure. Recently
a retrospective study showed the dominance of multi-drug resistance
genes (bacitracin and aminoglycoside) in Asian WWTPs (Zhang, 2016).
Additionally, many common ARGs found in human pathogens appear to
originate from the environmental resistome (Forsberg et al., 2012;
Wright, 2010). Therefore, there is urgent need to assess and monitor
changes in environmental pool of ARGs as a function of selective pres­
sures applied on the environment. Simulation studies to examine various
variables (bacteria, antibiotics, manure, and other factors discussed
earlier) are required to aid the construction of models to show the effect
various selective pressures on the development of the resistome in
different habitats. For example, Xiong et al. (2018) used chicken models
to show that a compositional change in gut microbiome was key to the
resistome’s variance. More specifically, therapeutic doses of tetracycline
10
M. Zhuang et al.
Environmental Pollution 285 (2021) 117402
Acknowledgments
high microbial density and diversity that may increase the chances of
horizontal transfer of ARGs (Schlüter et al., 2007). However, a meta­
genomics study by Tian et al. (2016) found no significant difference in
the abundance of total ARGs between thermophilic and mesophilic
anaerobic digestion of sewerage sludge. In contrast, elevated tempera­
tures during aerobic digestion were key to reducing bacterial and ARG
diversity in sludge (Jang et al., 2018). Interestingly, a significant
enrichment of ARGs in wastewater bioreactors that contained aromatic
compounds (p-aminophenol and p-nitrophenol) was also observed (Xia
et al., 2019). Therefore, a better understanding of how different factors
such as temperature, biochar, struvite, organic manure, heavy metal,
and other WWTPs by-products interact to shape soil resistome is
required. The evolution of the resistome results from confounding var­
iables that need attention if we are to gain insights into how ARGs are
disseminated in the environment.
We thank our lab members for engaging discussions and improve­
ments of the manuscript.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.envpol.2021.117402.
Authors’ contributions
MZ, YA, BAS, KYL conceptualized the manuscript, wrote the first
draft, and edited subsequent versions. YC, WH, and LC contributed
ideas, wrote some paragraphs, and commented on the text. LC and XL
did the NLP analysis. All authors read and approved the final
manuscript.
6. Conclusion
The widespread nature of ARGs in the environment is well docu­
mented in countries/regions from six continents. However, the modes of
actions of many ARGs and the interactions among ARGs still need to be
worked out. Additionally, indicator ARGs in the resistomes should be
identified in order to determine the rates of ARGs transfer among
different bacteria. The evolution of the resistome is complicated and
multi-factorial. ARGs research requires the use of sophisticated ‘omics’
tools found in specialized labs. Therefore, resistome research should be a
collaborative effort that utilizes different expertise and available tools to
elucidate the intricate relationships between ARGs, ARB, and other
factors in different environment. Although factors such as heavy metals,
struvite, biochar and others, have been shown to affect ARB and ARGs
diversity and abundancy, it is not clear how some of these factors,
individually or collectively, affect the development and evolution of the
resistome. Experiments in controlled environments that closely
approximate habitats are required in order to unravel the development
of the resistome in different habitats.
Survey of the literature using NLP of papers published in the last 30
years had limitations in elucidating the diversity and distribution of ARB
and ARGs in the environment. Future work with NLP should attempt to
extract information from the full papers, including results and discus­
sion, in order to get a better picture of the type of studies done in
different countries and habitats. This will make it easier to obtain precise
and detailed information on the nature of sub-resistomes in different
regions for comparative studies. A good understanding of the extent of
ARGs pollution in the environment, such as the different ways in which
ARGs disseminate within and among the different habitats, is required
before we can propose ways to prevent or limit the spread of ARGs in the
environment.
An integrated approach that connects ARGs information from the
different ‘omics’ tools used in resistome research is urgently needed. As
more data become available, this information will need to be curated to
allow easy of access, similar to the antibiotic resistance databases of
MEGARES and CARD (Alcock et al., 2019; Doster et al., 2019). Curated
information will enable scientists, government, and policy makers to
build reliable models for use in decision making by stakeholders in
healthcare, agriculture and environmental management. This will also
make it easier to institute measures to reduce or to remove the impacts
of antibiotics and ARB through biological, physical, and chemical means
(Li et al., 2021) and thereby enhance the health of humans, animals, and
the environment as outlined in the One Health concept.
Funding
This research was supported by the National Natural Science Foun­
dation of China (#31873048) to Dr. Ka Yin Leung and (#62002212) to
Dr. Liang Chen, 2020 Li Ka Shing Foundation (LKSF) Cross-Disciplinary
Research Grant (#2020LKSFG07A) to Drs. Ka Yin Leung, Hui Wang,
Yigal Achmon, and Liang Chen. And 2020 LKSF Cross-Disciplinary
Research Grant (#2020LKSFG07D) and the STU Scientific Research
Foundation for Talents (#35941918) to Dr. Liang Chen.
Availability of data and materials
Not applicable.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
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