Design and validation of primer sets for the detection and quantification of antibiotic resistance genes in environmental samples by quantitative PCR

doi:10.21203/rs.3.rs-3025831/v1

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Design and validation of primer sets for the detection and quantification of antibiotic resistance genes in environmental samples by quantitative PCR

https://doi.org/10.21203/rs.3.rs-3025831/v1

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The high prevalence of antibiotic resistant bacteria (ARB) in several environments is a great concern threatening human health. Hence, it is vital to dispose of molecular tools that allow proper monitoring of antibiotic resistant genes (ARGs) encoding resistances to these important therapeutic compounds. For an accurate quantification of ARGs, there is a need for sensitive and robust qPCR assays supported by a good design of primers and validated protocols. In this study, eleven relevant ARGs were selected as targets, including aadA and aadB (conferring resistance to aminoglycosides), ampC, bla_TEM, bla_SHV, and mecA (resistance to beta-lactams); dfrA1 (resistance to trimethoprim); ermB (resistance to macrolides); fosA (resistance to fosfomycin); qnrS (resistance to quinolones); and tetA(A) (resistance to tetracyclines). The in silico design of the new primer sets was performed based on the alignment of all the sequences of the target ARGs (orthology grade > 70%) deposited in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, allowing higher coverages of the ARG’s biodiversity than those of several primers described to date. The adequate design and well performance of the new molecular tools were validated in vivo in six samples, retrieved from both natural and engineered environments. The hallmarks of the optimized qPCR assays were high amplification efficiency (> 90%), good linearity of the standard curve (R² > 0.980), consistency across replicate experiments, and a wide dynamic range. The new methodology described here provide valuable tools to upgrade the monitorization of the abundance and emergence of the targeted ARGs in the environment by qPCR.

antibiotic resistance genes

primer design

assay optimization

qPCR

antibiotics

WWTPs

The antibiotic resistance phenomenon is one of the greatest health and environmental issues of concern of the twenty-first century, where the control of dissemination and redistribution of antibiotic resistance genes (ARGs) is one of the key challenges to ensure the therapeutic capacity of these important substances [1]. In this sense, the antibiotic compounds, once released into the environment even in sub-therapeutic dosages, could result in the enrichment of antibiotic resistant bacteria (ARB) [2]. Therefore, the widespread dissemination of ARB and the proliferation of ARGs and their potential mobilization through horizontal gene transfer requires a reliable determination of their occurrence and abundance in order to improve the understanding of their dynamics in environmental hotspots [3]. In this regard, the necessity to monitor the clinically and anthropogenically relevant ARGs has been highlighted by several governmental entities (U.S. Centers for Disease Control and Prevention, the National Aquatic Resource Survey, and European Antimicrobial Resistance Surveillance System). In addition, the absence of regulatory prescriptions about the critical levels of environmental contamination of antibiotics, ARB, and ARGs makes mandatory their quantification as the most suitable strategy to cope with the antimicrobial resistance issue [4].

Currently, ARGs’ monitorization relies on the use of highly accurate molecular-based methods, displacing progressively the time- and money-consuming culture-based approaches [5, 6]. In this respect, quantitative real-time polymerase chain reaction (qPCR) has become the gold standard method for the detection and quantification of ARGs in environmental samples [7, 8], due to technical advantages such as faster results, more specific detection, and the user-friendly methodology [5, 6]. A plethora of research addressed the abundance of the different ARGs in environmental samples by qPCR, including wastewater (WW) [6, 9], activated sludge [10, 11], and receiving waterbodies [7, 12], soil samples and manure [13, 14], or even mock communities of strains [15]. However, generally considered, the success of a qPCR assay requires appropriately validated primers and correct standards, together with the optimization of the assay performance according to the qPCR experimental guidelines (MIQE) [16]. Frequently these parameters are not optimized enough, which could result in non-specific amplification or under-quantification of the targets leading to untrustworthy and inconsistent results [17, 18]. Therefore, to contribute to the control of ARB dissemination, the reliable determination by qPCR of the abundance of ARGs in primary sources of contamination is mandatory. In this regard, to obtain the highest accuracy of the abundance measure of a specific target gene during qPCR assays, the proper design of the corresponding primers stands out as the most important factor [18, 19]. Literature regarding quality primer pair design describes several significant properties to consider, mainly, the primer size, the percentage of guanine and cytosine, the lack of formation of secondary structures, and an adequate range of annealing temperatures [17, 20, 21]. However, some developed qPCR assays are inadequately designed without reaching these key quality criteria [22]. On the other hand, most previous research has been focused on the characterization of individual pathogens or specific groups within a taxon, limiting the existence of universal primers for a given ARG, hence restricting the full detection and quantification of the broad diversity of the populations that encode the corresponding antibiotic resistance mechanism [23, 24]. In addition, it is necessary to continuously update the available molecular tools to avoid the underestimation of the ARGs, which could allow their dissemination and acquisition by previously sensitive bacteria.

Increasing the range of target bacteria encoding ARGs covered by the molecular tools is of vital interest and meets the requirements set in the MIQE guidelines for qPCR methodologies. Therefore, this study describes the development of new primer sets aimed to amplify the genes aadA and aadB (conferring resistance to aminoglycosides), ampC, bla_SHV, bla_TEM, and mecA (resistance to beta-lactams), dfrA1 (resistance to trimethoprim), ermB (resistance to macrolides), fosA (resistance to fosfomycin), qnrS (resistance to quinolones), and tetA(A) (resistance to tetracyclines). The selection of these ARGs was based on the reported clinical importance and the incremented resistance to the corresponding antibiotics in different environments [6], particularly their wide occurrence in wastewater treatment plants (WWTPs) [25, 26]. For that purpose, an in silico design of the different primer sets was made based on the alignment of sequences retrieved from the Kyoto Encyclopedia of Genes and Genome (KEGG) database with an orthology grade to the reference sequence > 70%. In addition, the molecular tools and the new qPCR protocols here developed were validated in vivo using DNA extracted from different environmental samples, including activated sludge, river sediment, and agricultural soils, ecosystems previously described as hotspots of the dissemination of the targeted ARGs and as environmental reservoirs of ARB [3, 27–29].

In silico design and validation of primer sets

The selection of ARGs encoding resistance to aminoglycosides (aadA and aadB genes), beta-lactams (Class A serine beta-lactamases: bla_TEM, bla_SHV genes, and Class C beta-lactamases: ampC gene) and meticillin (mecA gene), fosfomycin (fosA gene), tetracyclines (tetA-class A gene), macrolides (ermB gene), fluoroquinolones (qnrS gene) and trimethoprim (dfrA1 gene) was made according to their clinical importance and broad occurrence in the environment [25, 26].

The specific DNA sequences of the target genes were retrieved from the Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.genome.jp/kegg/), including sequences with an orthology grade > 70% for a given KEGG orthology number. The KEGG database entries employed for the present study are listed in Table 1. The sequences were aligned using the MAFFT algorithm, and the in silico design of the primers was performed using the Geneious 2021.1.1 software (Biomatters, Auckland, New Zealand). The specificity of the candidate primers over the target regions was assessed by querying the full genome (chromosomes and plasmids) of each strain employed in this study, to ensure the absence of non-specific annealing outside of the target fragment of the DNA. The complete genomes were retrieved from the GenBank DNA database (https://www.ncbi.nlm.nih.gov/genbank/). Finally, the in silico validated oligonucleotides were synthesized by the company Sigma-Aldrich (Merck KGaA, Germany).

Table 1

KEGG database entries of sequences for each ARGs selected.
Gene Target	KEGG database entries
aadA	K00984: streptomycin 3”-adenylyltransferase
aadB	K17881: aminoglycoside 2''-adenylyltransferase
ampC	K01467: beta-lactamase class C
bla_TEM	K18698: beta-lactamase class A TEM
bla_SHV	K18699: beta-lactamase class A SHV
dfrA1	K18589: dihydrofolate reductase
ermB	K00561: 23S rRNA (adenine-N6)-dimethyltransferase
fosA	K21253: glutathione S-transferase fosA
mecA	K02545: penicillin-binding protein 2 prime
qnrS	K18555: fluoroquinolone resistance protein
tetA(A)	K08151: DHA1 family, tetracycline resistance protein

Construction of positive plasmids standards for ARGs quantification

To quantify the target genes, the corresponding standard curves were constructed using genomic DNA from selected culture strains carrying them, or from a pool of DNA previously isolated from activated sludge samples. The reference bacterial strains employed in this study were obtained from the Spanish Type Culture Collection and were cultivated in the corresponding media listed in Supplementary Table 1SI. Pure bacterial DNA was extracted using a FastDNA kit (QBio/MP Biomedicals, LLC, France) according to the manufacturer instructions.

Amplification reactions were implemented in a final volume of 25 µL, comprising 2.5 µL of 10× DreamTaq buffer (Thermo Scientific, USA), 0.5 µL of True Pure dNTPs (8 mM) (Canvax, Spain), 0.15 µL of each primer (10 µM), 0.125 µL of 5 U/µL DreamTaq Hot Start DNA Polymerase (Thermo Scientific, USA), 0.125 µL of dimethyl sulfoxide (SigmaAldrich, USA), 0.0625 µL of 20 mg/mL of bovine serum albumin (New England Biolabs, USA), 19.3875 µL of ultrapure water, and 2 µL of template DNA. The thermal profile for the reactions consisted of an initial denaturation at 95°C for 10 min, followed by 35 cycles of denaturation at 95°C for 30 s, annealing at the temperatures and times optimized for each primer couple, and extension at 72°C for 40 s; and a final extension of 72°C for 7 min. The size of the PCR amplification products was checked by electrophoresis in agarose 2%, and the amplicons were purified using the QIAquick PCR purification kit (QIAGEN, Germany).

The purified PCR products were ligated into the pCR 4-TOPO vector (Invitrogen, USA) and then used to transform Escherichia coli DH5α competent cells, according to the specifications of the TOPO TA cloning system (Invitrogen, USA) After the cloning process, plasmid inserts of randomly selected clones were verified by Sanger Sequencing in the facilities of the Genetic Information Unit, of the Scientific Instrumentation Center (University of Granada, Spain). Sequences sharing > 98% identity with the corresponding control sequences were subsequently used as standards.

Quantification assay and acceptance criteria of qPCR parameters

Quantification of the target ARGs in environmental samples was performed by qPCR on a QuantStudio-3 Real-Time -PCR system (Applied Biosystems, USA). Thermocycler conditions were the same as those described above for conventional PCR, employing SYBR-Green I (0.125 µL, 20× SYBR Green I (Thermo Scientific, USA)) as dye method for real-time fluorescence monitoring. The methods were optimized for linearity, sensitivity, specificity and reproducibility. Samples were run in three independent biological and technical replicates (n = 9) along with non-template controls in different assays, to evaluate the reproducibility of the qPCR protocols. Serial dilutions of linearized plasmids containing the target genes, ranging from 10⁸ to 10 copies/µL, were used to construct the standard curves for the absolute quantification. The general workflow employed in this study is summarized in Fig. 1.

Biological Samples and DNA extraction

This study included six biological samples: activated sludge, composting sludge, anaerobic digestion sludge, agricultural soil, and river sediment. Two different activated sludge samples were employed, collected from the aeration tank in the secondary treatment step of the biofactories “Churriana de la Vega-Sur” (AS-CHU) and “Los Vados” (AS-VA), Granada, Spain. Composting sludge (COM) was collected from sewage sludge composting performed in the set of the environmental complexes Ecoindustria del Reciclado (EIDER) (Guadix, Granada, Spain). The biomass of anaerobic digestion sludge (AD) employed in this study was collected from an operational bioreactor producing volatile acids from the olive residue (alperujo) from Instituto de la Grasa, Sevilla, Spain. The river sediment (SED) was obtained from the Genil River in Granada City, Spain (UTM coordinates 43940, 13314). The agricultural soil (SOIL) was retrieved from a farming field irrigated by the Genil River (UTM coordinates 30N 444331, 4114407). All samples were immediately frozen after collection and stored at -20°C until DNA extraction.

Total DNA from biological samples (500 mg) was extracted using the FastDNA-2mL SPIN Kit for Soil and the FastPrep24 apparatus (MP-BIO, USA), according to the manufacturer instructions. Three independent biological replicates were used from each sample. The quality and concentration of the extracted DNA were measured by spectrophotometry using NanoDrop 2000 (Thermo Scientific, USA) and verified by electrophoresis on 1% agarose. DNA samples were stored at -20°C for further use.

After the DNA extraction, the quantification of the numbers of copies of each of the eleven ARGs was made by qPCR in duplicates applying the PCR conditions previously optimized for the standard curves, to evaluate the usefulness of the new molecular tools and qPCR protocols developed in this study. The number of total copies of the ARGs was expressed per gram of biological sample, according to the predicted concentration of the corresponding standard amplification plot [30]. In addition, the relative abundance of ARGs was calculated as the normalized ratio of ARGs copies to the corresponding 16S rRNA copies [31].

Statistical analyses

The statistical differences among data sets from different samples were analyzed using the non-parametric Kruskal-Wallis and Conover Iman tests (p < 0.05 significance level) in XLSTAT v2020 (Addinsoft, USA).

Primer in silico design and ARGs diversity

Sequences of the target ARGs with orthology degrees higher than 70% were retrieved from the corresponding KEGG entries. The number of sequences selected was as follows: aadA: 96 sequences, aadB: 21, ampC: 81, bla_SHV: 89, bla_TEM: 81, dfrA1: 42, ermB: 58, fosA: 20, mecA: 37, qnrS: 15, tetA(A): 49. All the ARGs’ sequences retrieved from the KEGG website are listed in supplementary Table 2SI. Subsequently, alignments were conducted using the MAFFT algorithm implemented in the Geneious Prime software to perform the in silico primer design, following the recommendations based on efficacy and specificity scores of Dreier et al. [21]. According to the parameters suggested by Bustin and Huggett [17], the adequate annealing temperatures (50–65ºC), base composition (GC content ranging of 50–60%), length (between 18–25 pb), absence of secondary structure, lack of potential hairpin formation, and lack of self-annealing were accomplished. Besides, to guarantee the total in silico coverage of the newly designed primers on the template sequences, degenerated bases were introduced into the primers to match all the sequences (maximum 2 degenerations per primer) wherever necessary. The new primers’ set guaranteed the total coverage of the template sequences (allowing two ambiguities per primer), generating amplicon sizes between 215 and 494 bp. In addition, the specificity of amplification of the new primers was tested in the 589 genomes used in this study, without obtaining in silico annealings out of the genetic zones of the genomes previously described as antibiotic resistance coding sequences. The candidate primers that matched over every targeted sequence and only exclusively over the target regions were considered for further research. The sequences of the new primers designed here and their main features are presented in Table 2. Higher coverages of the newly developed primers for the quantification of the selected sequences of aadA, ampC, bla_SHV, dfrA1, mecA, qnrS, and tetA(A) genes were found compared to those of all the available primers tested in this study (Table 3SI). Additionally, the primers available for the fosA gene presented very low coverages, except those developed recently by Abbott et al. [32]; however, the presence of a triplet at the beginning of the forward primer and a higher level of homodimers in the reverse primer are detrimental in comparison with the new primer set (fosA-7F/fosA-306R) designed in this study. On the other hand, several available primers for aadB, bla_TEM, and ermB presented coverages of 100% over the selected sequences. Nevertheless, a large number of non-compliances with the recommendations of the MIQE guidelines were found for these primers, underlining the necessity to develop new primers that solve those issues. In this sense, Keenum et al. [6] recently reported poor coverages of some of the most common primers used in the literature for the quantification of ARGs, highlighting the necessity to improve the molecular tools available for qPCR quantification. Hence, the de novo design of the new primers achieved better performance and allowed higher representativity of the absolute abundance of the different ARGs, as a higher detection coverage was warranted in this study, since all the current sequences deposited in the KEGG database (> 70% orthology degree) for a given ARG were specifically amplified in silico by the new primers.

Table 2

Sequences of the primers designed in thit study and their major features.
Gene Target	Primer ID	Sequence (5´- 3´)	Primer length (pb)	Amplicon size (pb)
aadA	aadA-336F	CATTCTTGCRGGTATCTTCGAGC	23	215
aadA	aadA-550R	GCACTACATTYCGCTCATCGC	21	215
aadB	aadB-118F	GACACAACGCAGGTCACATT	20	419
aadB	aadB-536R	GGTGGTACTTCATCGGCATAG	21	419
ampC	ampC-535F	GTGAAGCCRTCTGGTTTGAG	20	494
ampC	ampC-1028R	GCGACATAGCTACCAAATCCG	21	494
bla_SHV	blaSHV-286F	CAGGATCTGGTGGACTAYTC	20	219
bla_SHV	blaSHV-504R	CGCCTCATTSAGTTCCGTTTC	21	219
bla_TEM	blaTEM-335F	CGGATGGCATGACAGTAAGAG	20	275
	blaTEM-609R	TTGCCGGGAAGCTAGAGTAAG	21
dfrA1	dfrA-127F	GTMGGSCGCAAGACDTTYGA	20	255
dfrA1	dfrA-381R	GWARACATCACCYTCTGGCT	20	255
ermB	ermB-109F	GGAACAGGTAAAGGGCAT	18	434
	ermB-543R	TCTGTGGTATGGCGGGTAAG	20
fosA	fosA-7F	ACCGGTCTCAATCACCTGAC	20	300
fosA	fosA-306R	GAGGAAGTAGAACGAATCGCC	21	300
mecA	mecA-1196F	CTTCACCAGGTTCAACTCA	19	370
mecA	mecA-1565R	CCTTGTCCRTAACCTGAATC	20	370
qnrS	qnrS-244F	GCCAATTGYTACGGKATWGAG	21	227
qnrS	qnrS-470R	GACTCTTTCARTGATGCRCC	20	227
tetA(A)	tetA(A)-737F	TCATGCARCTYGTAGGMCAGG	21	454
tetA(A)	tetA(A)-1190R	AKCCATGCCMAWCCGTTCCA	20	454

In vivo amplification of ARGs using the new primer sets

The specificity and the efficacy of the new primer sets were tested in vivo using the corresponding reference strains previously described as carrying ARGs in their genomes, observing in all the cases a unique amplification band with the expected amplicon size (Table 3). According to the results of the different PCR reactions, all new primer pairs generated single bands of the expected size from both pure cultures (reference strains) and environmental DNA pools. An example of the in vivo validation of the specificity and the efficacy of the primers is showed in Fig. 1SI.

Table 3

Reference bacterial strains carrying target ARGs selected for this study.
Gene		Reference strain
aadA bla_TEM	Escherichia coli ATCC 35218 (CECT 943)
ampC	Escherichia coli K12 (CECT 433)
aadB/ bla_SHV	Klebsiella pneumoniae subsp. pneumoniae ATCC 700603 (CECT 7787)
mecA	Staphylococcus epidermidis RP62A, ATCC 35984 (CECT 4184)
fosA	Pseudomonas aeruginosa PAO1 (CECT 4122)
dfrA1, ermB, qnrS, tetA(A)	Environmental DNA extracted from sludge samples

Construction of the qPCR standars

The PCR products obtained for the in vivo validation of the primers were used as templates for the construction of the corresponding qPCR standards. For that purpose, purifications of the amplicons were made and the resulting cloning reactions were performed. Subsequently, eight transformant colonies obtained in each reaction were randomly selected to verify the presence of an insert of the right size by PCR, using agarose gel electrophoresis. Lately, four positive plasmids were selected for each ARG to confirm the correct identity of the inserts, by means of Sanger sequencing. All the sequenced clones were matched to the control sequences using the Map to Reference command of the Geneious Prime, obtaining sequence identities values ranging from 98–100%. The plasmid sequences with the highest homology were deposited in GenBank and used as standards for the qPCR assays. Generally considered, these results confirmed the specificity of the new primers designed here and their utility to accurately quantify ARGs in DNA isolated from complex environmental matrices, representing useful tools for monitoring the antibiotic-resistance phenomenon in the environment [13].

Validation of real-time quantitative PCR assays

The quantification of the gene copy numbers by qPCR is based on the linear relationship between the logarithm of the initial template quantity and the threshold cycle (Ct) value during amplification, allowing the calculations of the amplification efficiency and delineating its limits of detection (LOD) and quantification (LOQ) [33]. Hence, after validating different temperatures and times of amplification by qPCR, the thermal profiles that generated better amplification efficiencies are described in Table 4. Besides, the equations of linear regression and major properties of the linearity assays are presented in Table 5.

Table 4

Thermocycler conditions for quantification of the different ARGs by qPCR.
		aadA	aadB	ampC	bla_SHV	bla_TEM	dfrA1	ermB	fosA	mecA	qnrS	tetA(A)
Initial denaturalization		95°C, 10 min.	95°C, 10 min.	95°C, 10 min.	95°C, 10 min.	95°C, 10 min.	95°C, 10 min.	95°C, 10 min.	95°C, 10 min.	95°C, 10 min.	95°C, 10 min.	95°C, 10 min.
Amplification (×35 cycles)	Denaturalization	95°C, 30 sec.	95°C, 30 sec.	95°C, 30 sec.	95°C, 15 sec.	95°C, 15 sec.	95°C, 15 sec.	95°C, 15 sec.	95°C, 30 sec.	95°C, 30 sec.	95°C, 15 sec.	95°C, 30 sec.
	Annealing	60°C, 30 sec.	60°C, 30 sec.	60°C, 30 sec.	55°C, 30 sec.	60°C, 30 sec.	55°C, 30 sec.	55°C, 30 sec.	60°C, 30 sec.	50°C, 30 sec.	55°C, 30 sec.	55°C, 30 sec.
	Elongation	72°C, 40 sec.	72°C, 40 sec.	72°C, 40 sec.	72°C, 40 sec.	72°C, 40 sec.	72°C, 40 sec.	72°C, 40 sec.	72°C, 40 sec.	72°C, 40 sec.	72°C, 40 sec.	72°C, 40 sec.
Melting curve		60°C-95°C + 0.15°C/second. Fluorescence measured each 15 seconds

Table 5

Equation of the linear regression of standard curves, R² value, and amplification efficiency of each qPCR assay.
Target gene	Standard curve	R²	Amplification efficiency
aadA	y = -3.507x + 34.893	0.997	100%
aadB	y = -3.4644x + 31.965	0.995	94%
ampC	y = -3.5201x + 33.994	0.995	92%
bla_TEM	y = -3.5512x + 35.579	0.998	91%
bla_SHV	y = -3.5431x + 34.665	0.998	92%
dfrA1	y = -3.5276x + 33.713	0.998	92%
ermB	y = -3.5199x + 33.689	0.994	92%
fosA	y = -3.487x + 33.185	0.997	94%
mecA	y = -3.5821x + 34.339	0.997	90%
qnrS	y = -3.5798x + 34.293	0.998	90%
tetA(A)	y = -3.587x + 35.124	0.996	90%

The validation of a new qPCR assay must fulfill a set of minimal performance characteristics to assess that the reliability of the method has been achieved [34]. These characteristics are mainly working range, analytical sensitivity, trueness, LOD, and LOQ, efficiency ruggedness and precision, and selectivity. A wide dynamic working range of a well-designed assay is one of the key performance parameters to achieve in a new qPCR method, as it is one of the most important advantages of qPCR assays, and ensures the accurate quantification of the copy numbers of the target genes, even when their range of abundance spans several logarithmic units [17]. Also, the linearity in the qPCR quantification must be reported through the coefficient of determination (R² value) between the Ct values and the logarithm of gene copy numbers [13, 14]. Accordingly, an R² value > 0.980 provides good confidence in correlating Ct and target copy number, suggesting a proper fit of the distribution of the data along the dynamic range employed [17]. In this respect, the standard curves of the Ct values and the gene abundances here reported showed good linearity over a wide dynamic range (from 20 to 2 × 10⁸ gene copies (see details in Supplementary Fig. 2SI)), with R² values > 0.990. Hence, the new developed qPCR protocols provided an accurate estimation of the abundances of the different ARGs within a broad dynamic range.

Considering that the abundance of ARGs in certain types of samples may be very low, the development of a qPCR assay must be designed to differentiate a low number of copies of a given ARG in a sample from the inherent noise of the method. In this regard, the Ct values of the non-template assays (negative controls) were null or at least 3.3 cycles higher than those of the last dilution of the last standard point, subsequently, no significant background noise was found in these experiments [33]. Besides, the LOD and LOQ values for the qPCR methods were estimated from the analysis of the replicate standard curves [35]. The LOD values, based on detecting the target sequence at the lowest concentration of the standard curve [33], were 80–120 copy numbers. Similarly, the LOQ values of the assays corresponded to 4 ×10⁴ gene copies per gram of biological sample, which reflects the assay’s capacity to precisely quantify the target genes expressed as gene copies abundance per gram of matrix [34]. According to these results, the new qPCR methods developed here achieved proper levels of analytical sensitivity and trueness.

The qPCR assays with efficiency values within the range 80–120% are considered acceptable and could be subsequently employed [6]. The efficiency values of the different qPCR assays described here ranged between 90% and 100% (Table 5), therefore confirming the good performance of the developed methods. In this respect, variable amplification efficiency have been reported in the quantification range for bla_CTX−M (95.3%), bla_TEM (107.4%), bla_OXA1 (92.1%), ermB (91.3%), tetA (95.4%), sul1 (95.8%), sul2 (83%), dfrA1(88.5%) and dfrA12 (99.4%) [14]. The ruggedness was determined by comparing the Ct values among analytical replicates in a given qPCR assay and those obtained in the different qPCR assays. In this regard, the intra-analytical deviations of the standards among all qPCR assays were very low (average Ct value = 0.345, ranging from 1.164 to 0.024 cycles). Similarly, the mean analytical deviations among different qPCR experiments were 1.01 cycles (ranging from 0.558 to 1.316). Therefore, these qPCR methods showed a good reproducibility both in the same qPCR experiment and when comparing independent qPCR assays, confirming their effectiveness and ruggedness to quantify several ARGs reliably and accurately.

Finally, the specificity of the qPCR methods was also determined by analyzing the amplicon products after performing the assay [36]. In all cases, the melt curves displayed a single sharp peak and were shoulderless, indicating that the amplicons obtained were free of unspecific products, highlighting the reliable amplification of the target ARGs (Fig. 3SI). Also, the verification of the melting temperature of the environmental samples was compared with the expected peak of melting temperature obtained for the standard amplification products. Both melting temperature peaks were equivalent, confirming the specificity of primer annealing previously observed in the in vivo amplification assays.

Total quantification of ARGs in environmental samples

Developing new qPCR methods requires detecting and quantifying the actual occurrence of a given population, including rare, cryptic, and elusive genes in complex DNA from different environmental samples [37]. For that purpose, the efficiency of the primers, the qPCR conditions, and the effects of different matrices were validated in six natural and engineered environmental samples to show the right level of compliance with the critical considerations that need to be addressed for the validation of the design of new primers and the establishment of newly developed qPCR approaches. The total abundance of the ARGs detected in this study is presented in Fig. 2. The genes aadA, aadB, bla_TEM, dfrA1 and fosA were prevalent in all samples. On the other hand, the ampC, bla_SHV, ermB, qnrS, and tetA(A) genes were only detected in some samples, suggesting that these ARGs are rare in the different environments analyzed. Finally, the mecA gene was not measurable in any samples. According to these results, high ARGs’ detection frequencies (ranging from 70–100%) were found in the environmental samples using the de novo primer sets and qPCR methodologies proposed in this research, except for the mecA gene. These broad ARGs’ occurrences lend support to the idea of the ubiquitous distribution of ARGs in the environment [38, 39]. The widespread occurrences of ARGs for the most used antibiotics (beta-lactams, fluoroquinolones, tetracyclines, macrolides and sulfonamides) in natural and engineered ecosystems have been identified as a severe public health concern [40], a fact widely attributed to the subminimum concentrations of antibiotics that reach these ecosystems [9, 41].

The total abundances of ARGs among all the samples oscillated from 10³ to 10⁸ copies/g of biomass. Generally considered, the most abundant ARG was the aadA gene (average value 2.81 × 10⁷ copies/g), followed by ermB (2.75 × 10⁶ copies/g), dfrA1 (2.75 × 10⁶ copies/g), ampC (1.90 × 10⁶ copies/g), aadB (1.18 × 10⁶ copies/g), qnrS (8.10 × 10⁵ copies/g), bla_TEM (5.45x10⁵ copies/g), tetA(A) (3.65x10⁵ copies/g), bla_SHV (1.33x10⁵ copies/g), and, finally, fosA (1.35x10⁴ copies/g). According to the Kruskal-Wallis and Conover-Iman tests (Fig. 2), ermB and aadA were the most abundant genes in all the samples in which these ARGs were detected. The abundances of aadB, bla_TEM, dfrA1, and tetA(A) genes presented a middle prevalence, and the lowest abundances were statistically found for bla_SHV, fosA and qnrS. Finally, the numbers of gene copies of ampC were highly variable among samples.

Relative abundance of ARGs in environmental samples

Figure 3 and Table 4SI display the normalized ratio of ARGs copies to 16S rRNA copies (see details of 16S rRNA values in Fig. 4SI).

The highest relative abundance of ARGs for aminoglycosides (aadA, average 3.02%) and macrolides (ermB, 1.00%) observed in this study, are in agreement with those previously found in different WWTPs [42], in anaerobic digestates [43], in farms [44], and in riverine ecosystems [27]. The aadA gene confers resistance to a plethora of aminoglycoside antibiotics (spectinomycin, gentamicin, hygromycin B, and neomycin), which cumulatively are the third-most-used antibiotic class [45]. The high prevalence of the aadA gene in the six samples regardless of their origin could be linked to the fact that streptomycin has been amply used in animal husbandry and plant disease control since the late 1950s [46], which could have had a strong impact in the development and dissemination of aminoglycoside resistance in the natural environment. However, the use of streptomycin as a first-line antibiotic for tuberculosis treatment makes mandatory to avoid the dissemination of the aadA gene within pathogens and other sensitive bacteria [47]. On the other hand, one of the most abundant genes in the bacterial communities was ermB [46], whose dissemination is linked to bacteriophages via transduction, a prevalence that has been connected to the common clinical use of macrolides [43]. Also, the presence of ermB confers resistance to macrolides and it is also frequently associated with resistance to lincosamide and streptogramin type B, resulting in treatment failure to these three antibiotics groups, which inhibit bacterial protein synthesis [48].

The low relative abundances of of aadB, ampC, bla_SHV, bla_TEM, dfrA1, qnrS, and tetA(A) genes suggests that these ARGs are not the dominant resistance mechanisms in the bacterial communities here analyzed. Similarly low relative abundances of these genes have been described in activated sludge from different WWTPs, anaerobic digestate, compost, riverine sediments, and agricultural soil samples for the aadB [43, 49], ampC [25, 50, 51], bla_SHV [25, 52], bla_TEM [2, 12, 25, 53–56], dfrA1 [6, 53, 54, 56, 57], qnrS [59, 60], and tetA(A) genes [51, 53, 54, 57]. However, although these ARGs were detected in low relative abundances, their common occurrence implies a loss of the therapeutic effects of some of the most employed antibiotics compounds, resulting in treatment failures in both developed and developing countries and could be important agents in the exacerbation of antibiotic resistance emergence [61]. The enzyme encoded by the aadB gene confers resistance to gentamicin, kanamycin, and tobramycin [62]. This gene is usually present in microorganisms carrying extensive antibiotic resistances [63]. The plasmid-mediated extended-spectrum beta-lactamase (ESBL) bla_SHV and bla_TEM genes and the chromosomal ampC gene encode enzymes that can hydrolyze and inactivate broad-spectrum beta-lactam antimicrobials (third-generation cephalosporins, penicillins and aztreonam), except cefepime and carbapenems [64, 65]. The expression of the dfrA1 gene inhibits the therapeutic effect of the synthetic antibiotic combination of trimethoprim/sulfamethoxazole [66], antibiotics that are poorly removed during WW treatment processes, increasing their prevalence in the environment as a selective pressure on the bacterial communities [42]. Also, this resistance mechanism is typically carried as cassettes in integrons or insertions, facilitating their dispersion to other sensitive bacteria [67]. The qnrS gene mainly mediates the resistance to quinolones, being considered an antimicrobial resistance of the highest priority due to its significance in human medicine, particularly in developing countries [68]. In addition, the development of unexpected resistance mechanisms to synthetic antibacterial quinolones and trimethoprim encoded by the qnrS and dfrA1 genes highlights that antibiotic resistance is a process in continuous development [6, 60]. Among all tetracycline resistance genes, tetA(A) has been recently suggested as the molecular marker of the tet operon due to its abundance and relationship with anthropogenic inputs [69]. The broad presence of this gene in the environment is related to the nearly universal use of tetracyclines in livestock production, resulting in increased tetracycline resistance among Escherichia coli strains [70]. Ultimately, although all these genes were detected in low relative abundances in the samples analyzed here, monitoring their occurrence in the environment is essential to prevent their overdispersion, which will increase antibiotic resistance emergence irretrievably.

Finally, low relative abundances of fosA and the null presence of mecA genes were found in the six environmental samples here analyzed. Although only a few studies have determined the occurrence of fosA gene in environmental samples [71, 72], its scarce detection is in agreement with the previous results found in isolated bacteria [73] and in different environmental samples [74]. The enzyme encoded by the fosA gene confers resistance to fosfomycin, an antibiotic routinely used for the treatment of urinary infections in the last decades, that is also currently employed to deal with infections caused by extensively drug-resistant (XDR) Gram-negative bacteria [75]. Also, it is important to note that the prevalence of fosA in the environment supposes the proliferation of its main reservoirs, which are the well-recognized human pathogens Klebsiella spp., Enterobacter spp., and Serratia marcescens [76]. The absence of mecA gene in the different environmental samples is in agreement with the previous report of Shoaib et al. [77], which described a prevalence of this gene mainly in healthcare environments. However, a starting high prevalence of methicillin-resistant bacteria in environmental samples, mainly WWTPs, has been addressed in recent years [78, 79]. The mecA gene codes for a penicillin-binding protein (PBP2a), making microorganisms resistant to all beta-lactam compounds [80]. The broad proliferation of the mecA gene has been considered one of the most serious global health threats due to its capacity to cause nosocomial infections by multi-drug resistant human pathogens (mainly Methicillin-resistant Staphylococcus aureus (MRSA)), and to avoid its dissemination to bacteria sensitive to beta-lactams through horizontal gene transfer is a major priority [81]. Therefore, monitoring of both ARGs needs to be conducted and included in the framework for surveillance in natural environments to fill the gaps in the knowledge of the prevalence in environmental samples of fosA and mecA encoding resistance to therapeutic compounds essential to fight bacterial infections [72].

The development of effective and reliably new qPCR methods represent an outstanding contribution to quantifying the abundance of the ARGs and can provide information about the occurrence of ARB useful to propose new politics that minimize the emergence of antibiotic resistances. Hence, the improved primer sets developed in this study could be considered valuable tools to accurately monitor the ARGs in the environment as the first step to ameliorate antibiotic resistance phenomena.

This study has addressed the need to develop new qPCR assays using primers that allow a complete quantification of the increasing diversity of different ARB populations. In this respect, eleven primer sets targeting aadA, aadB, ampC, bla_SHV, bla_TEM, dfrA1, ermB, fosA, mecA, qnrS, and tetA(A) genes were designed fulfilling the strict requisites for the proper development of primers. The designs of the new primer sets enabled the in silico amplification of all the ARGs sequences deposited in KEGG database with orthology grades > 70%, allowing higher coverages than those of the currently available primers. The in vitro validation of the new molecular tools and qPCR protocols showed adequate values for an ample dynamic working range of concentrations, analytical sensitivity, trueness, LOD, and LOQ, efficiency, ruggedness, precision, and selectivity, demonstrating their robustness for environmental studies, even in samples with very low target DNA concentrations. Finally, the in vivo validation of six environmental samples, including natural (river sediment and agricultural soil) and engineered samples (activated sludge, compost, and anaerobic digestate), indicated the reliability of the new protocols according to their high precision between experimental replicates, robust consistency across replicate experiments, and wide dynamic range of quantification. On the other hand, different ARG abundance trends were found among samples, highlighting the prevalence of aadA and ermB genes in all of them. Low relative abundances of aadA, bla_SHV, bla_TEM, dfrA1, fosA, qnrS, tetA(A) genes and a null-presence of mecA gene were found. Particularly, the importance of these ARGs in antibiotic resistance emergence combined with the high occurrence of aadA and ermB genes confirmed the importance of WW treatments as hotspots of ARB dissemination. Therefore, the newly developed qPCR assays are useful to monitor the abundance of ARGs in several ecosystems in order to implement policies towards ameliorating antibiotic resistance emergence.

Ethical Approval

Not applicable

Competing interests

The authors declare not conflict of interest, including interests of a financial or personal nature

Authors' contributions

Lizandra Perez-Bou: Formal analysis, Investigation, Term, Formal analysis, Writing - review & editing, Visualization, Supervision.
Alejandro Gonzalez-Martinez: Funding acquisition, Term, Conceptualization, Resources, Writing - review & editing, Visualization.
Juan J. Cabrera: Term, Conceptualization, Resources, Writing - review & editing, Visualization.
Belen Rodelas: Funding acquisition, Term, Conceptualization, Resources, Formal analysis, Writing - review & editing, Visualization, Supervision.
Jesús González-López: Funding acquisition, Term, Conceptualization, Resources, Writing - review & editing, Visualization.
David Correa-Galeote: Formal analysis, Investigation, Term, Conceptualization, Resources, Formal analysis, Writing - review & editing, Visualization, Supervision.

Funding

This research was supported by the Andalusian Government through the ECORESISTOME project (A-RNM-62-UGR20). Lizandra Perez-Bou is grateful for her predoctoral fellowship of the Carolina Foundation 2021. David Correa-Galeote is grateful for his Aid in the Hiring of Doctoral Research Personnel Program of the Andalusian Government for his postdoctoral contract.

Availability of data and materials

Not applicable

Brown CL, Keenum IM, Dai D, Zhang L, Vikesland PJ, Pruden A. Critical evaluation of short, long, and hybrid assembly for contextual analysis of antibiotic resistance genes in complex environmental metagenomes. Sci Rep. 2021; 11: 3753. doi: 10.1038/s41598-021-83081-8.
Arsand JB, Hoff RB, Jank L, Bussamara R, Dallegrave A, Bento FM., Kmetzsch L, Falção DA, do Carmo M, de Araujo A, Pizzolato TM. Presence of antibiotic resistance genes and its association with antibiotic occurrence in Dilúvio River in southern Brazil. Sci Total Environ. 2020; 738: 139781. doi: 10.1016/j.scitotenv.2020.139781.
Schages L, Wichern F, Geisen S, Kalscheuer R, Bockmühl D. Distinct resistomes and microbial communities of soils, wastewater treatment plants and households suggest development of antibiotic resistances due to distinct environmental conditions in each environment. Antibiotics. 2021; 10 (5): 514. doi: 10.3390/antibiotics10050514.
Mancuso G, Midiri A, Gerace E, Biondo C. Bacterial antibiotic resistance: The most critical pathogens. Pathogens. 2021; 10: 1310. doi: 10.3390/pathogens10101310.
Kim J, Lim J, Lee C. Quantitative real-time PCR approaches for microbial community studies in wastewater treatment systems: applications and considerations. Biotechnol Adv. 2013; 31(8): 1358-73. doi: 10.1016/j.biotechadv.2013.05.010.
Keenum I, Liguori K, Calarco J, Benjamin CD, Milligan E, Harwood VJ, Pruden A. A framework for standardized qPCR targets and protocols for quantifying antibiotic resistance in surface water, recycled water and wastewater. Crit Rev Environ Sci Technol. 2022; 52: 4395-419. doi: 10.1080/10643389.2021.2024739.
Rocha J, Cacace D, Kampouris ID, Guilloteau H, Jäger T, Marano RB, Karaolia P, Manaia CM, Merlin C, Fatta-Kassinos D, Cytryn E, Berendonk TU, Schwartz T. Inter-laboratory calibration of quantitative analyses of antibiotic resistance genes. J Environ Chem Eng. 2020; 8(1): 102214. doi: 10.1016/j.jece.2018.02.022.
Li B, Yan T. Next generation sequencing reveals limitation of qPCR methods in quantifying emerging antibiotic resistance genes (ARGs) in the environment. Appl Microbiol Biotechnol. 2021; 105 (7): 2925-36. doi: 10.1007/s00253-021-11202-4.
Karkman A, Do TT, Walsh F, Virta MPJ. Antibiotic-resistance genes in wastewater. Trends Microbiol. 2018; 26(3): 220-8. doi: 10.1016/j.tim.2017.09.005.
Geets J, de Cooman M, Wittebolle L, Heylen K, Vanparys B, De Vos P, Verstraete W, Boon N. Real-time PCR assay for the simultaneous quantification of nitrifying and denitrifying bacteria in activated sludge. Appl Microbiol Biotechnol. 2007; 75(1): 211-21. doi: 10.1007/s00253-006-0805-8.
Wei Z, Feng K, Li S, Zhang Y, Chen H, Yin H, Xu M, Deng Y. Exploring abundance, diversity and variation of a widespread antibiotic resistance gene in wastewater treatment plants. Environ Int. 2018; 117: 186-95. doi: 10.1016/j.envint.2018.05.009.
Eramo A, Morales WR, Fahrenfeld NL. Viability-based quantification of antibiotic resistance genes and human fecal markers in wastewater effluent and receiving waters. Sci Total Environ. 2019; 656: 495-502. doi: 10.1016/j.scitotenv.2018.11.325.
Tolosi R, Carraro L, Laconi A, Piccirillo A. Optimization of five qPCR protocols toward the detection and the quantification of antimicrobial resistance genes in environmental samples. Methods X. 2021; 8: 101488. doi: 10.1016/j.mex.2021.101488.
Xu L, Chen H, Canales M, Ciric L. Use of synthesized double-stranded gene fragments as qPCR standards for the quantification of antibiotic resistance genes. J Microbiol Methods. 2019; 164: 105670. doi: 10.1016/j.mimet.2019.105670.
Stedtfeld RD, Guo X, Stedtfeld TM, Sheng H, Williams MR, Hauschild K, Gunturu S, Tift L, Wang F, Howe A, Chai B, Yin D, Cole JR, Tiedje JM, Hashsham SA. Primer set 2.0 for highly parallel qPCR array targeting antibiotic resistance genes and mobile genetic elements. FEMS Microbiol Ecol. 2018; 94(9): fiy130. doi: 10.1093/femsec/fiy130.
Taylor S, Wakem M, Dijkman G, Alsarraj M, Nguyen MA practical approach to RT-qPCR-Publishing data that conform to the MIQE guidelines. Methods. 2010; 50(4): S1-5. doi: 10.1016/j.ymeth.2010.01.005.
Bustin S, Huggett J. qPCR primer design revisited. Biomol Detect Quantif. 2017;14: 19-28. doi: 10.1016/j.bdq.2017.11.001.
Ruiz-Villalba A, van Pelt-Verkuil E, Gunst QD, Ruijter JM, van den Hoff MJ. Amplification of non-specific products in quantitative polymerase chain reactions (qPCR). Biomol Detect Quantif. 2017; 14: 7-18. doi: 10.1016/j.bdq.2017.10.001.
Bae J, Jeon H, Kim MS. GPrimer: a fast GPU-based pipeline for primer design for qPCR experiments. BMC Bioinformatics. 2021; 22 (1): 220. doi: 10.1186/s12859-021-04133-4.
Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Mueller R, Nolan T, Pfaffl MW, Shipley GL, Vandesompele J, Wittwer CT. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009; 55(4): 611-22. doi: 10.1373/clinchem.2008.112797.
Dreier M, Berthoud H, Shani N, Wechsler D, Junier P. SpeciesPrimer: a bioinformatics pipeline dedicated to the design of qPCR primers for the quantification of bacterial species. PeerJ. 2020; 18 (8): e8544. doi: 10.7717/peerj.8544.
Bustin S, Nolan T. Talking the talk, but not walking the walk: RT‐qPCR as a paradigm for the lack of reproducibility in molecular research. Eur J Clin Invest. 2017; 47(10): 756-74. doi: 10.1111/eci.12801.
Storteboom H, Arabi M, Davis JG, Crimi B, Pruden A. Identification of antibiotic-resistance-gene molecular signatures suitable as tracers of pristine river, urban, and agricultural sources. Environ Sci Technol. 2010; 44(6): 1947-53. doi: 10.1021/es902893f.
Tremblay ÉD, Carey J, Bilodeau GJ, Hambleton S. Four in silico designed and validated qPCR assays to detect and discriminate Tilletia indica and T. walkeri, individually or as a complex. Biology (Basel). 2021; 10(12): 1295. doi: 10.3390/biology10121295.
Wang J, Chu L, Wojnárovits L, Takács E. Occurrence and fate of antibiotics, antibiotic resistant genes (ARGs) and antibiotic resistant bacteria (ARB) in municipal wastewater treatment plant: An overview. Sci Total Environ. 2020; 744: 140997. doi: 10.1016/j.scitotenv.2020.140997.
Nguyen AQ, Vu HP, Nguyen LN, Wang Q, Djordjevic SP, Donner E, Yin H, Nghiem L D. Monitoring antibiotic resistance genes in wastewater treatment: Current strategies and future challenges. Scie Total Environ. 2021; 783: 146964. doi: 10.1016/j.scitotenv.2021.146964.
Marti E, Jofre J, Balcazar JL. Prevalence of antibiotic resistance genes and bacterial community composition in a river influenced by a wastewater treatment plant. PLoS One. 2013; 8(10): e78906. doi: 10.1371/journal.pone.0078906.
Zhang S, Huang J, Zhao Z, Cao Y, Li B. Hospital wastewater as a reservoir for antibiotic resistance genes: a meta-analysis. Front. Public Health. 2020; 8: 574968. doi: 10.3389/fpubh.2020.574968.
Omuferen LO, Maseko B, Olowoyo JO. Occurrence of antibiotics in wastewater from hospital and convectional wastewater treatment plants and their impact on the effluent receiving rivers: current knowledge between 2010 and 2019. Environ Monit Assess. 2022; 194(4): 306. doi: 10.1007/s10661-022-09846-4.
Correa-Galeote D, Tortosa G, Bedmar EJ. Determination of denitrification genes abundance in environmental samples. Metagenomics. 2013; 2: 1-14. doi: 10.4303/mg/235702.
Correa-Galeote D, Roibás-Rozas A, Mosquera-Corral A, Juárez-Jiménez B, González-López J, Rodelas B. Revealing the dissimilar structure of microbial communities in different WWTPs that treat fish-canning wastewater with different NaCl content. J Water Process Eng. 2021; 44: 102328. doi: 10.1016/j.jwpe.2021.102328.
Abbott, I.J., van Gorp, E., Wijma, R.A., Dekker, J., Croughs, P.D., Meletiadis, J., Mouton, J.W., Peleg, A.Y. Efficacy of single and multiple oral doses of fosfomycin against Pseudomonas aeruginosa urinary tract infections in a dynamic in vitro bladder infection model. J Antimicrob Chemother. 2020; 75:1879-1888. https://doi.org/10.1093/jac/dkaa127.
Forootan A, Sjöback R, Björkman J, Sjögreen B, Linz L, Kubista M. Methods to determine limit of detection and limit of quantification in quantitative real time PCR (qPCR). Biomol Detect Quantif. 2017; 12: 1-6. doi: 10.1016/j.bdq.2017.04.001.
Hedman J, Lavander M, Salomonsson EN, Jinnerot T, Boiso L, Magnusson B, Rådström P. Validation guidelines for PCR workflows in bioterrorism preparedness, food safety and forensics. Accreditation Qual Assur. 2018; 23: 133-44. doi: 10.1007/s00769-018-1319-7.
Klymus KE, Merkes CM, Allison MJ, Goldberg CS, Helbing CC, Hunter M, Jackson C, Lance RF, Mangan AM, Monroe EM, Piaggio AJ, Stokdyk JP, Wilson CC, Richter CA. Reporting the limits of detection and quantification for environmental DNA assays. Environ DNA. 2019; 2: 271-82. doi: 10.1002/edn3.29.
Ponchel F, Toomes C, Bransfield K, Leong FT, Douglas SH, Field SL, Bell SM, Combaret V, Puisieux A, Mighell AJ, Robinson PA, Inglehearn CF, Isaacs JD, Markham AF Real-time PCR based on SYBR-Green I fluorescence: an alternative to the TaqMan assay for a relative quantification of gene rearrangements, gene amplifications and micro gene deletions. BMC Biotechnol. 2003; 3: 18. doi: 10.1186/1472-6750-3-18.
Beng KC, Corlett RT. Applications of environmental DNA (eDNA) in ecology and conservation: opportunities, challenges and prospects. Biodivers Conserv. 2020; 29: 2089-121. doi: 10.1007/s10531-020-01980-0.
Hao Y-L, Li G, Xiao Z-F, Liu N, Azeem M, Zhao Y, Xu Y-Y, Yu X-W. Distribution and influence on the microbial ecological relationship of antibiotic resistance genes in soil at a watershed scale. Sustainability. 2021; 13(17): 9748. doi: 10.3390/su13179748.
Nimnoi P, Pongsilp N. Identification, Characterization, and virulence gene expression of marine enterobacteria in the upper gulf of Thailand. Microorganisms. 2022; 10(3): 511. doi: 10.3390/microorganisms10030511.
Oberoi AS, Jia Y, Zhang H, Khanal SK, Lu H. Insights into the fate and removal of antibiotics in engineered biological treatment systems: a critical review. Environ Sci Technol. 2019; 53(13): 7234-64. doi: 10.1021/ACS.EST.9B01131.
Laht M, Karkman A, Voolaid V, Ritz C, Tenson T, Virta M, Kisand V. Abundances of tetracycline, sulphonamide and beta-lactam antibiotic resistance genes in conventional wastewater treatment plants (WWTPs) with different waste load. PLoS One. 2014; 9(8): e103705. doi: 10.1371/journal.pone.0103705.
Szczepanowski R, Linke B, Krahn I, Gartemann KH, Gutzkow T, Eichler W, Euhler A, Schl Euter AE. 2009. Detection of 140 clinically relevant antibiotic-resistance genes in the plasmid metagenome of wastewater treatment plant bacteria showing reduced susceptibility to selected antibiotics. Microbiology (Reading). 2009; 155(Pt 7): 2306-19. doi: 10.1099/mic.0.028233-0.
Tang M, Dou X, Wang C, Tian Z, Yang M, Zhang Y. Abundance and distribution of antibiotic resistance genes in a full-scale anaerobic-aerobic system alternately treating ribostamycin, spiramycin and paromomycin production wastewater. Environ Geochem Health. 2017; 39(6): 1595-605. doi: 10.1007/s10653-017-9987-5.
Chen J, Yu Z, Michel FC Jr, Wittum T, Morrison M. Development and application of real-time PCR assays for quantification of erm genes conferring resistance to macrolides-lincosamides-streptogramin B in livestock manure and manure management systems. Appl Environ Microbiol. 2007; 73(14): 4407-16. doi: 10.1128/AEM.02799-06.
Weist K, Högberg LD. ECDC publishes 2015 surveillance data on antimicrobial resistance and antimicrobial consumption in Europe. Euro Surveill. 2016; 21(46): 30401. doi: doi: 10.2807/1560-7917.
Wang X, Gu J, Gao H, Qian X, Li H. Abundances of clinically relevant antibiotic resistance genes and bacterial community diversity in the Weihe River, China. Int J Environ Res Public Health. 2018; 15(4): 708. doi: 10.3390/ijerph15040708.
Cohen KA, Stott KE, Munsamy V, Manson AL, Earl AM, Pym AS. Evidence for expanding the role of streptomycin in the management of drug-resistant Mycobacterium tuberculosis. AAC. 2020; 64(9): e00860-20. doi: 10.1128/AAC.00860-20.
Timsina R, Shrestha U, Singh A, Timalsina B. Inducible clindamycin resistance and erm genes in Staphylococcus aureus in school children in Kathmandu, Nepal. Future Science OA. 2020; 7(1): FSO361. doi: 10.2144/fsoa-2020-0092.
Liao H, Friman VP, Geisen S, Zhao Q, Cui P, Lu X, Chen Z, Yu Z, Zhou S. Horizontal gene transfer and shifts in linked bacterial community composition are associated with maintenance of antibiotic resistance genes during food waste composting. Sci Total Environ. 2019; 660: 841-50. doi: 10.1016/j.scitotenv.2018.12.353.
Xu YB, Hou MY, Li YF, Huang L, Ruan JJ, Zheng L, Qiao QX, Du QP. Distribution of tetracycline resistance genes and AmpC β-lactamase genes in representative non-urban sewage plants and correlations with treatment processes and heavy metals. Chemosphere. 2017; 170: 274-81. doi: 10.1016/j.chemosphere.2016.12.027.
Lin X, Ruan J, Huang L, Zhao J, Xu Y. Comparison of the elimination effectiveness of tetracycline and AmpC β-lactamase resistance genes in a municipal wastewater treatment plant using four parallel processes. Ecotoxicology. 2021; 30: 1586-97. doi: 10.1007/s10646-020-02306-0.
Graham DW, Knapp CW, Christensen BT, McCluskey S, Dolfing J. Appearance of β-lactam resistance genes in agricultural soils and clinical isolates over the 20th century. Sci Rep. 2016; 6(1): 1-8. doi: 10.1038/srep21550.
Cao R, Wang J, Ben W, Qiang Z. The profile of antibiotic resistance genes in pig manure composting shaped by composting stage: Mesophilic-thermophilic and cooling-maturation stages. Chemosphere. 2020; 250: 126181. doi: 10.1016/j.chemosphere.2020.126181.
Patil HJ, Gatica J, Zolti A, Benet-Perelberg A, Naor A, Dror B, Al Ashhab A, Marman S, Hasan NA, Colwell RR, Sher D, Minz D, Cytryn E. Temporal resistome and microbial community dynamics in an intensive aquaculture facility with prophylactic antimicrobial treatment. Microorganisms. 2020; 8(12): 1984. doi: 10.3390/microorganisms8121984.
Stachurová T, Piková H, Bartas M, Semerád J, Svobodová K, Malachová K. Beta-lactam resistance development during the treatment processes of municipal wastewater treatment plants. Chemosphere. 2021; 280: 130749. doi: 10.1016/j.chemosphere.2021.130749.
Shin J, Lee S, Park H, Son H, Raza S, Wang J, Kim YM. Effects of thermal hydrolysis on anaerobic digestion and abundance of antibiotic resistance genes during recuperative thickening digestate treatment of sewage sludge. Chem Eng. 2022; 450: 138128. doi: 10.1016/j.cej.2022.138128.
Mahbub KR, King WL, Siboni N, Nguyen VK, Rahman MM, Megharaj M, Seymour JR, Franks AE, Labbate M. Long-lasting effect of mercury contamination on the soil microbiota and its co-selection of antibiotic resistance. Environ Pollut. 2020; 265: 115057. doi: 10.1016/j.envpol.2020.115057.
Zhang Y, Zheng Y, Zhu Z, Chen Y, Dong H. Dispersion of antibiotic resistance genes (ARGs) from stored swine manure biogas digestate to the atmosphere. Sci Total Environ. 2021; 761: 144108. doi: 10.1016/j.scitotenv.2020.144108.
Huang YH, Liu Y, Du PP, Zeng LJ, Mo CH, Li YW, Lü H, Cai QY. Occurrence and distribution of antibiotics and antibiotic resistant genes in water and sediments of urban rivers with black-odor water in Guangzhou, South China. Sci Total Environ. 2019; 670: 170-80. doi: 10.1016/j.scitotenv.2019.03.168.
Slipko K, Marano RB, Cytryn E, Merkus V, Wögerbauer M, Krampe J, Jurkevitch E, Kreuzinger N. Effects of subinhibitory quinolone concentrations on functionality, microbial community composition, and abundance of antibiotic resistant bacteria and qnrS in activated sludge. J Environ Chem Eng. 2021; 9(1): 104783. doi: 10.1016/j.jece.2020.104783.
Meini S, Tascini C, Cei M, Sozio E, Rossolini GM. AmpC β-lactamase-producing Enterobacterales: what a clinician should know. Infection. 2019; 47(3): 363-75. doi: 10.1007/s15010-019-01291-9.
Ambrose S, Ibri P, Harmer CJ, Pong CH, Hall RM. Characterisation of an early South African multiply antibiotic-resistant global clone 1 (GC1) Acinetobacter baumannii isolate. J Glob Antimicrob Resist. In press. https://doi.org/10.1016/j.jgar.2023.05.003
Nigro SJ, Holt KE, Pickard D, Hall RM. Carbapenem and amikacin resistance on a large conjugative Acinetobacter baumannii plasmid. J Antimicrob Chemother. 2015; 70 (4): 1259-61. doi: 10.1093/jac/dku486.
Mata C, Miró E, Alvarado A, Garcillán-Barcia MP, Toleman M, Walsh TR, de la Cruz F, Navarro F. Plasmid typing and genetic context of AmpC β-lactamases in Enterobacteriaceae lacking inducible chromosomal ampC genes: findings from a Spanish hospital 1999-2007. J Antimicrob Chemother. 2012; 67(1): 115-22. doi: 10.1093/jac/dkr412.
Dirar MH, Bilal NE, Ibrahim ME, Hamid ME. Prevalence of extended-spectrum β-lactamase (ESBL) and molecular detection of blaTEM, blaSHV and blaCTX-M genotypes among Enterobacteriaceae isolates from patients in Khartoum, Sudan. The Pan Afr Med J. 2020; 37: 213. doi: 10.11604/pamj.2020.37.213.24988.
Sánchez-Osuna M, Cortés P, Llagostera M, Barbé J, Erill I. Exploration into the origins and mobilization of di-hydrofolate reductase genes and the emergence of clinical resistance to trimethoprim. Microb Genom. 2020; 6(11): mgen000440. doi: 10.1099/mgen.0.000440.
Tagg KA, Francois-Watkins L., Moore MD, Bennett C, Joung YJ, Chen JC, Folster JP. Novel trimethoprim resistance gene dfrA34 identified in Salmonella heidelberg in the USA. J Antimicrob Chemother. 2019; 74(1): 38-41. doi: 10.1093/jac/dky373.
Castrignanò E, Kannan AM, Proctor K, Petrie B, Hodgen S, Feil EJ, Lewis SE, Camacho-Muñoz D, Rice J, Cartwright N, Barden R, Kasprzyk-Hordern B. (Fluoro)quinolones and quinolone resistance genes in the aquatic environment: A river catchment perspective. Water Res. 2020; 182: 116015. doi: 10.1016/j.watres.2020.116015.
Nnadozie CE, Odume ON. Freshwater environments as reservoirs of antibiotic resistant bacteria and their role in the dissemination of antibiotic resistance genes. Environ Pollut. 2019; 254: 113067. doi: 10.1016/j.envpol.2019.113067.
Møller TSB, Overgaard M, Nielsen SS, Bortolaia V, Sommer MOA, Guardabassi L, Olsen JE. Relation between tetR and tetA expression in tetracycline resistant Escherichia coli. BMC Microbiol. 2016; 16: 39. doi: 10.1186/s12866-016-0649-z.
Li S, Liu Z, Süring Ch, Chen L, Müller S, Zeng P. The impact of the antibiotic fosfomycin on wastewater communities measured by flow cytometry. Front Microbiol. 2022; 12: 737831. doi: 10.3389/fmicb.2021.737831.
Güneri CÖ, Stingl K, Grobbel M, Hammerl JA, Kürekci C. Different fosA genes were found on mobile genetic elements in Escherichia coli from wastewaters of hospitals and municipals in Turkey. Sci Total Environ. 2022; 824: 153928. doi: 10.1016/j.scitotenv.2022.153928.
Tuo H, Yang Y, Tao X, Liu D, Li Y, Xie X, Li P, Gu J, Kong L, Xiang R, Lei C, Wang H, Zhang A. The prevalence of colistin resistant strains and antibiotic resistance gene profiles in Funan River, China. Front Microbiol. 2018; 9: 3094. doi: 10.3389/fmicb.2018.03094.
Chen L, Zhang M, Ning D, Van Nostrand JD, Yang Y, Zhou J, Zuo J. Behaviors of homologous antibiotic resistance genes in a cephalosporin WWTP, subsequent WWTP and the receiving river. Front Environ Sci. 2021; 9: 783676. doi: 10.3389/fenvs.2021.783676.
Zurfluh K, Treier A, Schmitt K, Stephan R. Mobile fosfomycin resistance genes in Enterobacteriaceae-an increasing threat. Microbiology open. 2020; 9(12): e1135. doi: 10.1002/mbo3.1135.
Ito R, Mustapha MM, Tomich AD, Callaghan JD, McElheny CL, Mettus RT, Shanks RMQ, Sluis-Cremer N, Doi,Y. Widespread fosfomycin resistance in Gram-negative bacteria attributable to the chromosomal fosA gene. mBio. 2017; 8(4): e00749-17. doi: 10.1128/mBio.00749-17.
Shoaib M, Rahman SU, Aqib AI, Ashfaq K, Naveed A, Kulyar MdF., Bhutta Z, Younas M, Sarwar I, Naseer M, Ghaffar A. Diversified epidemiological pattern and antibiogram of mecA gene in Staphylococcus aureus isolates of pets, pet owners and environment. Pak Vet J. 2020; 40: 331-6. doi: 10.29261/pakvetj/2020.039.
Boopathy R. Presence of methicillin resistant Staphylococcus aureus (MRSA) in sewage treatment plant. Bioresour Technol. 2017; 240: 144-8. doi: 10.1016/j.biortech.2017.02.093.
Pimenta LKL, Rodrigues CA, Filho ARG, Coelho CJ, Goes V, Estrel M, de Souza P, Avelino MAG, Vieira JDG, Carneiro L. Staphylococcus spp. causatives of infections and carrier of blaZ, femA, and mecA genes associated with resistance. Antibiotics. 2023; 12: 671. doi: 10.3390/antibiotics12040671.
Liao X, Cullen PJ, Liu D, Muhammad AI, Chen S, Ye X, Wang J, Ding T. Combating Staphylococcus aureus and its methicillin resistance gene (mecA) with cold plasma. Sci Total Environ. 2018; 645: 1287-95. doi: 10.1016/j.scitotenv.2018.07.190.
Gan T, Shu G, Fu H, Yan Q, Zhang W, Tang H, Yin L, Zhao L, Lin J. Antimicrobial resistance and genotyping of Staphylococcus aureus obtained from food animals in Sichuan Province, China. BMC Vet Res. 2021; 17: 177. doi: 10.1186/s12917-021-02884-z.

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Design and validation of primer sets for the detection and quantification of antibiotic resistance genes in environmental samples by quantitative PCR

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Abstract

Figures

Introduction

Materials and Methods

Construction of positive plasmids standards for ARGs quantification

Quantification assay and acceptance criteria of qPCR parameters

Biological Samples and DNA extraction

Statistical analyses

Results and Discussion

Construction of the qPCR standars

Validation of real-time quantitative PCR assays

Total quantification of ARGs in environmental samples

Relative abundance of ARGs in environmental samples

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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