Framework for establishing regulatory guidelines to control antibiotic resistance in treated effluents

Figures & data

Figure 1. Abundance of genes determined based on quantitative PCR in the raw influent (IN) and final effluent (OUT) of UWTPs in different countries and published in different studies. Data corresponds to determinations made in n UWTPs: 16S rRNA gene (n = 125 IN; n = 32 OUT); intI1 (n = 80 IN; n = 18 OUT); sul1 (n = 17 IN; n = 16 OUT); aadA (n = 11 IN; n = 10 OUT); ermF (n = 13 IN; n = 10 OUT); tet (n = 18 IN; n = 12 OUT); bla_OXA (n = 12 IN; n = 9 OUT); qnrS (n = 7 IN; n = 6 OUT). Please see text and Table SI-1 for other details.

Figure 2. Pathways of dissemination and/or removal of antibiotic resistance by urban wastewater treatment plants and potential impacts. Examples of sensitive areas that may be impacted by UWTPs are provided: areas subjected to other pollution sources (industry, intensive agriculture); areas that can experience overflow and flood events or with limited dilution capacity, particularly in drought regions and/or seasons; discharging in proximity to recreational areas or where water for drinking water production or agricultural irrigation are collected.

Table 1. Examples of intrinsic and exogenous factors (fitness drivers) that may influence the success of an ARB in wastewater or related environment.

Fitness drivers	Characteristics of the organism and the respective environment
Intrinsic	Diverse nutrient, carbon, and energy metabolism; High growth rate /short doubling time; Cell structures or mechanisms that may facilitate nutrients access or transitory insensitivity to stress conditions (adhesion and biofilm formation, endospores formation, cysts or dormancy, other); Fast and efficient response to stress challenges; Fast and efficient cell damage repair;
Under exogenous (biotic)	Competing microbiota with low growth rate/long doubling time; Competing microbiota with specific (mainly non-organotrophic) metabolism or narrow nutrient range; High load of invasive species;
Under exogenous (abiotic)	Abundance of nutrients that are mainly used by the fast-growers; Occurrence of substances that may be inhibitory for competing microbiota (antibiotic residues, metals) but tolerated by invasive species; Temperature, pH, oxygen or other parameters that may be more beneficial for one microbial group than for others;

Table 2. Summary of most common methodological options for ARB/ARG monitoring in wastewater and related samples.

Sample processing		Analytical method (references)	Type of results/conclusions	Observations
Culture-based	Dilutions to reach adequate number of colony forming units for counting/isolation Membrane filtration and subculturing	Selective culture media, for specific microbial groups or antibiotic resistance types (Ferreira da Silva et al., 2007; Honda et al., 2020; Marano et al., 2020; Novo & Manaia, 2010; Pallares-Vega et al., 2021)	Number of bacteria of a given group Antibiotic resistance phenotype and genotype Epidemiologic evaluation;	+ Possibility of detecting some extremely rare resistance types that may be not detected by other methods - Laborious, time-consuming, in general uninformative
Culture- independent	Membrane filtration for sample concentration Total DNA extraction	Quantitative PCR using gene-specific primers (Cacace et al., 2019; Pallares-Vega et al., 2019; Pärnänen et al., 2019; Rocha et al., 2019; Keenum et al., 2022)	Detection and quantification (absolute or relative abundance) of genetic determinants that are being searched (targeted analysis)	+ Possibility of measuring specific determinants and express per volume or mass - Limited number of genetic determinants may be examined - Primer design may influence the results - Non-viable cells and free-DNA may contribute to overestimate abundance or underestimate cell-inactivation
		Metagenomics (An et al., 2018; Bengtsson-Palme et al., 2016; Lira et al., 2020)	Detection and quantification (relative abundance) of a vast array of genetic determinants (non-targeted analysis)	+ Possibility of assessing the diversity and the relative abundance (in relation to total) of genetics determinants - Limited sensitivity and resolving capacity; results interpretation may be difficult in some situations - High background level of non-target DNA - The databases and search criteria have a strong influence on the results - Non-viable cells and free-DNA may contribute to overestimate abundance or underestimate cell-inactivation.

Table 3. Examples of biomarkers that might be used to monitor AR removal in UWTPs, impacts on the environment, and to establish a first line of human-health risk assessment.

Biomarker	Description	Criterion	References
16S rRNA gene	Universal biomarker of bacteria	Assessment of bacterial abundance bacterial removal/increase ARG relative abundance	1,2
crAssphage	Phage, associated with the genus Bacteroides, abundant in human gut	Abundant in wastewater Molecular indicator of fecal contamination	3,4
Class 1 integron integrase gene, intI1	Related with MGEs that may harbor ARGs	Among the most used AR biomarkers Abundant in wastewater Related with ARGs Occurs in multiple wastewater bacterial species Occurs in the chromosome and plasmids	5,6
Dihydropteroate synthase gene, sul1	Encodes resistance against sulfonamides, the first class of antibiotics used in medicine	Among the most examined ARGs in wastewater Abundant in wastewater Associated with intI1 Occurs in multiple wastewater bacterial species Occurs in the chromosome and plasmid	7,8
Aminoglycoside nucleotidyltransferase gene, aadA	Encodes resistance against streptomycin	Aminoglycoside resistance genes are among the most abundant in wastewater Associated with intI1 (variable region) Occurs in multiple wastewater bacterial species Occurs in the chromosome and plasmids	9,10
Beta-lactamase gene blaCTX-M	Encodes resistance against beta-lactams (penicillins and may include third generation cephalosporins)	Occurs in multiple wastewater bacterial species Include extended-spectrum beta-lactamase producers, a high clinical relevance (risk assessment) Occurs in the chromosome and plasmids	11,12
Erythromycin resistance methyltransferase genes, ermB or ermF	Encodes resistance against macrolides	Macrolide resistance genes are among the most abundant in wastewater Occur in multiple wastewater bacterial species	2, 13,14,15
Plasmid-mediated quinolone resistance, qnrS	Encodes resistance to quinolones	Abundant in wastewater Occurs in multiple wastewater bacterial species Mostly plasmid-associated	1,11

References: 1. Narciso-da-Rocha et al. (2018); 2. Pallares-Vega et al. (2019); 3. Karkman et al. (2019); 4. Park et al. (2020); 5. Gillings et al. (2015); 6. Ma et al. (2017); 7. Le et al. (2018); 8. Storteboom et al. (2010); 9. Iakovides et al. (2021); 10. Pärnänen et al. (2019); 11. Moreira et al. (2018); 12. Tong et al. (2019); 13. Zheng et al. (2019); 14. Tong et al. (2019); 15. Takali et al. (2020).

Figure 3. Proposed flow diagram for ARG monitoring in UWTPs to measure treatment efficiency, assess impacts, and to support the design of corrective interventions. Where, refers to preferential sampling sites; When, refers to periodicity of monitoring; How, summarizes the procedures; What, proposes what should be analyzed and the expected reporting outputs.

Figure 4. Workflow of ARG monitoring in UWTPs and the immediate/local and medium-term/global expected implications.

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