Authors:
Ádám Kerek Department of Pharmacology and Toxicology, University of Veterinary Medicine, István street 2, H-1078 Budapest, Hungary
National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, Hungary

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Ábel Szabó Department of Pharmacology and Toxicology, University of Veterinary Medicine, István street 2, H-1078 Budapest, Hungary

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Krisztián Bányai Department of Pharmacology and Toxicology, University of Veterinary Medicine, István street 2, H-1078 Budapest, Hungary
National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, Hungary
Veterinary Medical Research Institute, Hungária krt. 21, H-1143, Budapest, Hungary

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Eszter Kaszab National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, Hungary
One Health Institute, University of Debrecen, Nagyerdei krt. 98., Debrecen, H-4032, Hungary
Department of Microbiology and Infectious Diseases, University of Veterinary Medicine, István u 2., Budapest, H-1078, Hungary

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Krisztina Bali National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, Hungary
Department of Microbiology and Infectious Diseases, University of Veterinary Medicine, István u 2., Budapest, H-1078, Hungary

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Márton Papp National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, Hungary
Centre for Bioinformatics, University of Veterinary Medicine, István street 2, H-1078 Budapest, Hungary

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László Kovács Department of Animal Hygiene, Herd Health and Mobile Clinic, University of Veterinary Medicine, István street 2, H-1078 Budapest, Hungary

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Ákos Jerzsele Department of Pharmacology and Toxicology, University of Veterinary Medicine, István street 2, H-1078 Budapest, Hungary
National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, Hungary

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Open access

Abstract

The authors aimed to investigate eight strains of Escherichia coli (E. coli) strains from Hungarian layer flocks for antimicrobial resistance genes (ARG), using metagenomic methods. The strains were isolated from cloacal swabs of healthy adult layers. This study employed shotgun sequencing-based genetic and bioinformatic analysis along with determining phenotypic minimum inhibitory concentrations. A total of 59 ARGs were identified in the eight E. coli isolates, carrying ARGs against 15 groups of antibiotics. Among these, 28 ARGs were identified as transferable. Specifically, four ARGs were plasmid-derived, 18 ARGs were phage-derived and an additional six ARGs were predicted to be mobile, contributing to their mobility and potential spread between bacteria.

Abstract

The authors aimed to investigate eight strains of Escherichia coli (E. coli) strains from Hungarian layer flocks for antimicrobial resistance genes (ARG), using metagenomic methods. The strains were isolated from cloacal swabs of healthy adult layers. This study employed shotgun sequencing-based genetic and bioinformatic analysis along with determining phenotypic minimum inhibitory concentrations. A total of 59 ARGs were identified in the eight E. coli isolates, carrying ARGs against 15 groups of antibiotics. Among these, 28 ARGs were identified as transferable. Specifically, four ARGs were plasmid-derived, 18 ARGs were phage-derived and an additional six ARGs were predicted to be mobile, contributing to their mobility and potential spread between bacteria.

Introduction

The discovery of antibiotics made it possible to treat bacterial infectious diseases, but over time, resistance has become widespread (Micoli et al., 2021), and is now one of the most significant global public health issues (von Wintersdorff et al., 2016). Even the most conservative estimates suggest that without any significant intervention, by 2050 nearly 10 million people will die from antimicrobial resistance (AMR) each year (O'Neill, 2014). The resistance mechanisms and genes can be so abundant, that in a single Escherichia coli (E. coli) strain, almost all known resistance mechanisms can be found, with 68 different resistance genes, making it resistant against 16 widely used antibiotics (Zhang et al., 2019). To reduce the possibilities of resistance development, a number of antibiotic alternatives have emerged, including the use of vaccines as a preventive measure (Micoli et al., 2021), which can prevent bacteria from multiplying to the point where resistance mutations are developed (Rappuoli et al., 2017). It should also be mentioned that the use of antibiotic resistance genes as marker genes in live bacterial vaccines is strongly discouraged, as these genes can be transferred to humans (El-Attar et al., 2012).

It is also important to note that correlations have been found between several resistance genes and virulence genes, which may lead to the selection of bacteria with different virulence genes (Boerlin et al., 2005). However, in case of some intestinal pathogenic E. coli strains it has been observed that the more resistance genes a bacterium has, the less virulence genes it carries (Nagy et al., 2015).

Second-generation sequencing (NGS, next generation sequencing) is a fast parallel sequencing technique, also known as deep or shallow sequencing (Marguerat et al., 2008). New algorithms are continuously being developed to align huge amounts of data, in the form of short reads, identify operons and recombinant variants and build phylogenetic trees based on single nucleotide polymorphisms (SNP), among others (Chan, 2009). At first, sequencing was used for the comprehensive detection of ARGs of E. coli by several authors (Veenemans et al., 2014; Hernández-Fillor et al., 2021; Alvarez Narvaez et al., 2022). More recently, the possibility of genome sequencing has also enabled the development of safe vaccines (Prachi et al., 2013) to test strains for efficacy, primarily for virulence factors (Bidmos et al., 2018), but without putting an emphasis on transferable antimicrobial resistance. We aimed to investigate the resistome (complete ARG carriage) of different E. coli strains isolated from cloacal swabs of healthy adult poultry using NGS technique and to explore the risks of transmissible antimicrobial resistances for human and for animal health, including the theoretical possibility of using such bacteria for the development of live vaccine candidates.

Materials and methods

Strains

Eight commensal E. coli isolates were used in this study, isolated from cloacal swab samples from healthy layer hens in 2020, using Coliform Selective Agar (Biolab Zrt., Budapest, Hungary). These samples were collected from different large laying flocks in different geographical regions of Hungary. They were chosen from among strains collected from flocks based on preliminary phenotypic susceptibility testing. The selection focused on strains demonstrating the highest resistance to antibiotic agents commonly used in poultry.

Sequencing

DNA from bacterial suspension was isolated using the QIAmp DNA kit (Qiagen, Germany), following the manufacturer's protocol. DNA extraction was performed in a Qiagen Tissue Lyzer LT at 50 Hz for 10 min, with each sample eluting at 50 μg mL−1. Finally, fluorometric quantification was performed using Qubit® dsDNA BR Assay kit (Thermo Fisher SSC, Budapest, Hungary).

DNA libraries were prepared using Illumina® Nextera XT DNA Library Preparation Kit (Illumina, San Diego, USA). Indexes were used to label DNA fragments using the Nextera XT Index Kit v2 Set C (Illumina, San Diego, USA). The resulting indexed DNA library was purified using Gel/PCR DNA Fragments Extraction Kit (Geneaid Biotech, Xinpei, Taiwan), following the column purification protocol; and then Qubit® dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, USA) was used for fluorometric quantification for quality control. Paired-end reads generated from the DNA were determined using the Illumina NextSeq 500 sequencer (Sahin-Tóth et al., 2021).

Bioinformatics data processing

Initial quality control of the raw sequences was conducted using FastQC v0.11.9 software (Andrews, 2012), followed by the elimination of sequences with subpar quality using TrimGalore v0.6.7 (Krueger et al., 2021). The read sequences were assembled into longer contigs using MEGAHIT v1.2.9 (Li et al., 2015). For contig quality assessment (Table 1), the QUAST software was employed (Gurevich et al., 2013). All conceivable Open Reading Frames (ORFs) were subsequently identified from the resulting contigs utilizing Prodigal v2.6.3 (Hyatt et al., 2010) and deposited (1028257 BioProject ID).

Table 1.

Quality characteristics of contigs

StrainNo of trimmed readsNo of contigsCoverageNG50NG75LG50LG75
17,783,36330691.97341,18622,3553775
23,603,21924986.08777,51043,4642039
33,840,47323985.8898,05852,3211934
43,412,51022185.2585,76852,8881633
53,632,20821583.254100,78652,2181329
63,406,33720388.683,90255,5531734
73,440,73713882.821100,45362,4851428
83,815,33431283.32888,20044,2421937

Identification of ARGs within the ORFs was performed using Resistance Gene Identifier (RGI) v5.1.0 against the CARD database (downloaded on 23/04/2021) (Alcock et al., 2020). Only genes meeting the STRICT threshold criteria established by the CARD database were considered. Furthermore, these genes were only taken into account if they exhibited sequence identity and coverage of at least 95%.

To assess the potential mobility of identified resistance genes, MobileElementFinder (v1.0.3) (Johansson et al., 2021) was employed, predicting Mobile Genetic Elements (MGEs) on the contigs. For evaluating mobility, only ARGs within the range of the longest composite transposon (Tn1681, 24,488 nucleotides in length), typical for E. coli in the database, were considered potentially mobile. Additionally, contigs' plasmid origin was investigated using PlasFlow v1.1 software (Krawczyk et al., 2018), while the presence of phage genomes on the contigs was determined using VirSorter v2.2.2 (Roux et al., 2015) software.

As a quality control, we looked at the assembly size (i.e. how many nucleotides there are in the assembly). LG50 (length genome metrics) is the least number of contigs that are 50% of the genome length, if we sort the contigs by size. NG50 (number genome metrics) is the length of the shortest of these contigs (i.e. those that reach 50% of the genome length). The NG75 and LG75 values are relative to 75% of the genome. The abbreviations LG50 and LG75 signify that the shortest contigs reaching 50% and 75% of the genome length are determined when contigs are sorted by size. NG50 is always greater than or equal to NG75 and LG50 is always less than or equal to LG75.

Minimum inhibitory concentration determination

The phenotypic manifestation of AMR was evaluated by determining the minimum inhibitory concentration (MIC) value of each bacterial strain against specific antibiotics. The testing protocol adhered to the methodology outlined by the CLSI (Clinical and Laboratory Standards Institute, 2018). Breakpoints for interpretation were established using criteria from both the CLSI and the European Committee on Antimicrobial Susceptibility Testing (EUCAST, 2015) (Table 4).

The active substances used for testing (Merck KGaA, Darmstadt, Germany) included amoxicillin and amoxicillin-clavulanic acid (in a 2:1 ratio) in phosphate buffer solution at pH 6 (0.1 mol L−1); cefquinome, cefotaxime, oxytetracycline, doxycycline, gentamicin, and colistin dissolved in distilled water; imipenem in phosphate buffer solution at pH 7.2 (0.01 mol L−1); sulfamethoxazole dissolved in hot water with a few drops of 2.5 mol L−1 NaOH; trimethoprim dissolved with 0.05 mol L−1 HCl; and a potent sulphonamide (sulfamethoxazole and trimethoprim in a 20:1 ratio) dissolved as previously described. Enrofloxacin dissolution was prepared with a few drops of 1 mol L−1 NaOH solution in distilled water.

The MIC determination range was as follows: 32-0.06 μg mL−1 for amoxicillin, amoxicillin-clavulanic acid, gentamicin, oxytetracycline and doxycycline; 16-0.03 μg mL−1 for cefquinome, colistin, cefotaxime, imipenem and enrofloxacin; 128-0.25 μg mL−1 for sulfamethoxazole, trimethoprim and the potent sulphonamide.

Results

The frequency of each resistance mechanism within each antibiotic group is presented in Fig. 1, while the identified ARGs are comprehensively listed in Table 2. In this analysis, a total of eight distinct E. coli strains were subjected to examination, with the majority of the resistance genes listed in the table being detected across all strains.

Fig. 1.
Fig. 1.

Frequency of genes encoding major antimicrobial resistance mechanisms by antibiotic group

Citation: Acta Veterinaria Hungarica 72, 1; 10.1556/004.2024.00988

Table 2.

List of antimicrobial resistance genes and gene products identified in the eight E. coli isolates investigated. (The number of isolates with the respective genes identified are in parentheses)

Antimicrobial resistance genes and gene productsCoverage %Sequence identity %M1Ph2Pl3Resistance class *Antibiotic mechanism *
acrB (8)100100fluoroquinolone, cephalosporin, glycylcycline, penam, tetracycline, rifamycin, phenicol, triclosanantibiotic efflux
acrD (8)10099.81+aminoglycosideantibiotic efflux
acrE (8)100100+fluoroquinolone, cephalosporin, cephamycin, penamantibiotic efflux
acrF (8)10099.61+fluoroquinolone, cephalosporin, cephamycin, penamantibiotic efflux
acrS (7)100100fluoroquinolone, cephalosporin, glycylcycline, cephamycin, penam, tetracycline, rifamycin, phenicol, triclosanantibiotic efflux
bacA (8)100100peptide antibioticantibiotic target alteration
baeR (8)10099.58+aminoglycoside, aminocoumarinantibiotic efflux
baeS (8)10099.14+aminoglycoside, aminocoumarinantibiotic efflux
cpxA (8)100100+aminoglycoside, aminocoumarinantibiotic efflux
CRP (7)10099.52macrolide, fluoroquinolone, penamantibiotic efflux
emrA (8)10099.74fluoroquinoloneantibiotic efflux
emrB (8)100100fluoroquinoloneantibiotic efflux
emrK (8)10099.72+tetracyclineantibiotic efflux
emrR (7)100100fluoroquinoloneantibiotic efflux
emrY (8)10099.8+tetracyclineantibiotic efflux
eptA (8)10099.63peptide antibioticantibiotic target alteration
acrA (8)100100fluoroquinolone, cephalosporin, glycylcycline, penam, tetracycline, rifamycin, phenicol, triclosanantibiotic target alteration, antibiotic efflux
acrR (8)100100fluoroquinolone, cephalosporin, glycylcycline, penam, tetracycline, rifamycin, phenicol, triclosanantibiotic target alteration, antibiotic efflux
ampC (5)10097.35+cephalosporin, penamantibiotic inactivation
ampC1 (3)99.7799.08+cephalosporin, penamantibiotic inactivation
ampH (7)10099.74cephalosporin, penamantibiotic inactivation
cyaA (3)10099.29fosfomycinantibiotic target alteration
EF-Tu (5)96.3399.75elfamycinantibiotic target alteration
emrE (8)10098.18+macrolideantibiotic efflux
glpT (8)10099.78fosfomycinantibiotic target alteration
gyrA (3)10099.77fluoroquinoloneantibiotic target alteration
marR (8)10098.61fluoroquinolone, cephalosporin, glycylcycline, penam, tetracycline, rifamycin, phenicol, triclosanantibiotic target alteration, antibiotic efflux
mdfA (8)10096.59tetracycline, benzalkonium chloride, rhodamineantibiotic efflux
soxR (8)100100+fluoroquinolone, cephalosporin, glycylcycline, penam, tetracycline, rifamycin, phenicol, triclosanantibiotic target alteration, antibiotic efflux
soxS (8)100100+
1M: Mobile genetic elements; 2Ph: located on Phage; 3Pl: located on Plasmid.

* The Comprehensive Antibiotic Resistance Database (Alcock et al., 2023).
Antibiotic resistance genes (ARG) and gene productsCoverage %Sequence identity %M1Ph2Pl3Resistance class*Antibiotic mechanism*
uhpT (1)10099.57+fosfomycinantibiotic target alteration
evgA (8)100100macrolide, fluoroquinolone, penam, tetracyclineantibiotic efflux
evgS (8)10099.42
gadW (7)10096.28
gadX (1)10098.54
H-NS (8)100100macrolide, fluoroquinolone, cephalosporin, cephamycin, penam, tetracyclineantibiotic efflux
kdpE (7)10099.11+aminoglycosideantibiotic efflux
marA (8)100100fluoroquinolone, monobactam, carbapenem, cephalosporin, glycylcycline, cephamycin, penam, tetracycline, rifamycin, phenicol, triclosan, penemantibiotic efflux, reduced permeability to antibiotic
mdtA (6)10099.28+aminocoumarinantibiotic efflux
mdtB (8)100100+aminocoumarinantibiotic efflux
mdtC (8)10099.41+aminocoumarinantibiotic efflux
mdtE (8)100100macrolide, fluoroquinolone, penamantibiotic efflux
mdtF (7)10099.81macrolide antibiotic; fluoroquinolone antibiotic; penamantibiotic efflux
mdtG (8)100100++fosfomycinantibiotic efflux
mdtH (8)100100+fluoroquinoloneantibiotic efflux
mdtM (6)10097.8+fluoroquinolone, lincosamide antibiotic, nucleoside antibiotic, acridine dye, phenicol, disinfecting agents and intercalating dyesantibiotic efflux
mdtN (8)10099.42+nucleoside antibiotic, acridine dye, disinfecting agents and intercalating dyesantibiotic efflux
mdtO (7)95.7598.62+
mdtP (8)10098.16+
msbA (8)100100++nitroimidazole antibioticantibiotic efflux
pmrF (8)100100peptide antibioticantibiotic target alteration
qnrS1 (1)100100++fluoroquinoloneantibiotic target protection
sul2 (2)100100++sulfonamide antibioticantibiotic target replacement
blaTEM-1 (1)100100++monobactam, cephalosporin, penam, penemantibiotic inactivation
tetB (2)10099.25tetracyclineantibiotic efflux
tetR (1)99.52100tetracyclineantibiotic target alteration, antibiotic efflux
tolC (8)99.6100+macrolide, fluoroquinolone, aminoglycoside, carbapenem, cephalosporin, glycylcycline, cephamycin, penam, tetracycline, peptide antibiotic, aminocoumarin, rifamycin, phenicol, triclosan, penemantibiotic efflux
ugd (7)10099.23++peptide antibioticantibiotic target alteration
yojI (8)10099.82peptide antibioticantibiotic efflux

1M: Mobile genetic elements; 2Ph: located on Phage; 3Pl: located on Plasmid.

* The Comprehensive Antibiotic Resistance Database (Alcock et al., 2023).

The majority of ARGs were associated with the fluoroquinolone group within The Antimicrobial Advice Ad Hoc Expert Group (AMEG) class B, holding significant implications for both human and animal health. Notably, 73% of these ARGs were attributed to efflux pumps, a critical component of resistance mechanisms. Furthermore, within this group, which encompasses generation 3–4 cephalosporins, 57.8% of β-lactam ARGs were identified as encoding cephalosporin resistance. Within the same category, the occurrence of genes encoding resistance to polymyxins (colistin) reached six, constituting 3.7% of the total genes detected. Collectively, the total count of ARGs targeting drug groups categorized in the AMEG class B amounted to 50, representing 30.9% of all identified genes.

In total, we detected a diverse spectrum of 59 unique ARGs across the eight sequenced bacterial strains. These 59 genes collectively comprised the entirety of the distinct ARGs identified. Notably, some genes were found to be present in multiple strains, contributing to the total count of 162 ARGs detected within the dataset encompassing all eight strains. The predominant resistance mechanism was efflux pumps, accounting for 110 of the identified genes (67.9%). Among these, 22 genes were associated with fluoroquinolones (13.6%), 17 with β-lactams (10.5%) and the third most frequent occurrence was observed for tetracyclines (16, 9.9%). The prevalence of mutation or enzymatic target alteration type resistance mechanisms was also most notable among the aforementioned drug groups. The differences between the individual gene pool of each strain, compared to the total of 59 resistance genes identified are summarised in Table 3.

Table 3.

Antimicrobial resistance genes not detected in the individual eight E. coli strains studied relative to their total number of AMR genes

Differences for all 59 genes identified
StrainsMissing genes
1.

Total genes: 49
ampC1, ampH, cyaA, EF-Tu, gyrA, uhpT, gadW, mdtF, qnrS1, blaTEM-1
2.

Total genes: 48
CRP, ampC, gyrA, uhpT, gadX, kdpE, qnrS1, blaTEM-1, sul2, tetB, tetR
3.

Total genes: 51
gyrA, uhpT, gadX, qnrS1, blaTEM-1, sul2, tetB, tetR
4.

Total genes: 50
acrS, ampC, uhpT, gadX, qnrS1, blaTEM-1, sul2, tetB, tetR
5.

Total genes: 48
ampC1, cyaA, EF-Tu, uhpT, gadX, mdtO, qnrS1, blaTEM-1, sul2, tetB, tetR
6.

Total genes: 49
ampC, ampC1, cyaA, gyrA, uhpT, gadX, qnrS1, blaTEM-1, tetB, tetR
7.

Total genes: 46
emrR, ampC1, cyaA, EF-Tu, uhpT, gyrA, gadX, qnrS1, blaTEM-1, mdtM, sul2, tetB, tetR
8.

Total genes: 50
ampC1, cyaA, uhpT, gadX, mdtA, mdtM, sul2, tetR, ugd

Moreover, we identified ARGs responsible for enzymatic inactivation against specific β-lactam antibiotics. Particularly significant were the two reduced permeability genes, soxS and marA, which exhibited their role in conferring resistance against fluoroquinolones, β-lactams, tetracyclines, aminoglycosides, phenicol and rifamycin. A single target replacement gene, sul2, was associated with sulphonamides. Additionally, a singular instance of target protection was found, specifically the qnrS1 gene that contributed to fluoroquinolone resistance. Regarding their origin, 10 of the identified genes were associated with MGEs (6.2%), 23 were located on phage (14.2%) and 3 were located on plasmid (1.9%). Interestingly, three genes displayed both MGEs and were located on sequences of phage attributes (1.9%), while another three genes exhibited dual MGEs and were located on sequences of plasmid characteristics (1.9%). In our assessment, matches with a coverage above 98% and sequence identity above 98% were considered for analysis.

Upon comparing the phenotypic test results (Table 4) with the identified resistance genes, a clear alignment emerged between putative resistance genes and their corresponding high MIC values. This suggests that the genes potentially accountable for the observed resistance are closely matched with the individual elevated MIC levels.

Table 4.

Phenotypic analysis of strains and comparison of results with sequencing results. Data highlighted in red are considered resistant

For instance, in the case of amoxicillin and amoxicillin-clavulanic acid, the prominence of β-lactamase overproduction, particularly attributed to the ampC gene, is indicative of its responsibility. However, no discernible increase in MIC value was observed with cephalosporins. Fortunately, low MIC value was observed with colistin, too. Regarding imipenem resistance, the activation of efflux pumps (soxS, marA, tolC) could be suspected. A similar trend was inferred for resistance to aminoglycosides and tetracyclines, with the acrD and kdpE genes associated with the former and the tetB, tetR and mdfA genes associated with the latter. For fluoroquinolone resistance observed in a single strain, an efflux pump mechanism - involving the acrA, acrB, and tolC gene – could be presumed.

Discussion

Genes responsible for enzymatic inactivation were found only against β-lactams (ampC, ampC1, ampH, blaTEM-1 genes). Genes responsible for permeability reduction were found for fluoroquinolones, β-lactams, tetracyclines, aminoglycosides, phenicols and rifamycin (soxS, marA genes). Target site modification occurred only against sulphonamides by the sul2 gene that causes resistance of dihydropteroate synthase to sulphonamides and is usually on small plasmids (Sköld, 2001). The qnrS1 gene is usually a fluoroquinolone resistance gene on a plasmid preventing the binding of the drug by protecting the target of antibiotic action (Hata et al., 2005).

MIC values determined in phenotypic testing reflect the expression of individual resistance genes. In the case of amoxicillin and amoxicillin-clavulanic acid, the observed resistance is due to the ampC, ampH and blaTEM-1 genes, of which ampH is on a plasmid, ampC is an MGE and the blaTEM-1 gene is on a plasmid and is also an MGE. The former two are classified by Ambler as Class-C β-lactamase, the blaTEM-1 gene is a broad-spectrum Class-A β-lactamase and able to hydrolyse the first generation cephalosporins (Mittal et al., 2007; Lister et al., 2009). Resistance to cephalosporins is not phenotypically expressed, despite the fact that the Escherichia coli bacterial species carry a number of these genes. In the case of imipenem, the soxS, marA and tolC genes may be responsible for the emergence of resistance, of which the soxS (Aly et al., 2015) and marA (Cohen et al., 1988) genes are regulator genes responsible for the upregulation of the acrAB efflux pump system, complemented by the subprotein encoded by the tolC gene, thus forming a multidrug efflux pump (Tikhonova et al., 2011). The acrD gene expresses a phage-encoded aminoglycoside efflux pump (Rosenberg et al., 2000) and kdpE is also an aminoglycoside efflux pump encoding gene (Freeman et al., 2013). The emergence of resistance to tetracyclines is due to tetB and tetR as MFS-type efflux pump encoding genes and the mdfA gene is an efflux pump on a phage (Roberts, 2005). The sul2 gene is on the plasmid is also an MGE, encoding sulphonamide resistance (Sköld, 2001). In the case of fluoroquinolones, the emerging resistance is attributed to the multidrug efflux pumps acrA, acrB and tolC and in addition, point mutations in the enzyme encoded by the gyrA gene prevent binding of fluoroquinolones to the DNA gyrase alpha subunit (Webber et al., 2017). The qnrS1 gene is a plasmid-mediated, mobile gene that prevents drug binding (Hata et al., 2005). No phenotypic resistance was observed with colistin, but it should be noted that the ugd gene encoding increased arabinose synthesis in the lipid layer was found as an MGE (Gunn et al., 1998).

The study of ARGs entering the human body by any means is a critical One Health concept. In the case of E. coli in particular, the diversity of ARGs is well observed. Strains harbouring ARGs are given the opportunity to interact with other non-pathogenic bacteria and horizontal gene transfer occurs. This is particularly likely if the ARG is on a mobile DNA gene sequence (ampC, uhpT, mdtG, msbA, qnrS1, sul2, blaTEM-1, tolC, ugd). This is of relevance from a human health perspective, for genes responsible for broad-spectrum β-lactamase production (blaTEM-1) and resistance to AMEG B class antibiotics such as colistin (ugd), fluoroquinolones (qnrS1) and generation 3–4 cephalosporins (tolC). Quinolone resistance has been reported from Hungary several times, both from human and animal origin, which is an emerging concern in public health (Szabó et al., 2008; Szmolka et al., 2011).

No mcr1 gene was found, which is important for resistance to colistin. In another study, where the the resistance gene pool of E. coli in broiler chickens from Hungary was investigated, this mcr1 gene was also absent, probably due to strict regulations (Adorján et al., 2020). However, the emergence of mcr-1 in E. coli has recently been reported in Hungary (Szmolka et al., 2023). This gene has also been recently identified in different E. coli strains from South Africa (Ramatla et al., 2023). The pmrE gene was exclusively chromosomal, although the pmrE (ugd) gene may be a problem due to its mobility, but these were not expressed. The marA gene should be highlighted, as an important regulator gene in the function of the MDR-type acrAB efflux pump system and is also responsible for the downregulation of porin channels through the regulation of the ompF gene. Genes encoding macrolide resistance would only be significant if they were mobile, the same being the case for fosfomycin. The presence of the gyrA gene mutation is not advantageous, even though it is not mobile. Due its coding and mobility, the qnrS1 gene on plasmids and the mobility of the sul2 gene, as well as the presence of the ampC gene may also be the matter of One Health concern.

Conclusions

Our results suggest that the use of genetic analysis to map bacterial resistome does not necessarily indicate phenotypic expression, despite the identification of individual genes. The results of MIC studies reflect the correlation between the emergence of resistance to certain active substances and the presence of respective ARGs.

The frequency of mobile genetic elements generally increases the likelihood of gene spread. The spread of AMR to livestock and to the different products of livestock, as well as to humans coming into contact with animals, is now proven to determine the structure of similar resistances. The most relevant indicator in this respect is E. coli (Luiken et al., 2019; Van Gompel et al., 2020; Tóth et al., 2021). This is exemplified in our study, where we identified the gene TEM-1, which is one of the important broad-spectrum β-lactamases in veterinary and public clinical use. The widespread distribution of several of these AMRGs is due to the fact that their gene is often carried on self-transmissible or mobilizable plasmids, making it capable of rapid horizontal spread, also among different enterobacterial species (Szabó et al., 2008; Szmolka et al., 2011; Perilli et al., 2002) and they are detected all around the world (Chotinantakul et al., 2022; Ghenea et al., 2022; Zhang et al., 2022).

For the precise elucidation of the underlying genes responsible for driving each phenotypically manifested resistance, we recommend conducting further transcriptomic studies. It is also necessary to carry out extensive surveys and to identify and minimise potential sources of risks for the spread of AMR.

Data availibility

Sequences generated in this study are available under 1028257 BioProject ID.

Acknowledgements

Prepared with the professional support of the Doctoral Student Scholarship Program of the Co-operative Doctoral Program of the Ministry of Innovation and Technology Financed from the National Research, Development and Innovation Fund (KDP-1-4/PALY-2021). Supported by Normative Research Funding Committee (NRC), University of Veterinary Medicine Budapest. Project no. RRF-2.3.1-21-2022-00001 has been implemented with the support provided by the Recovery and Resilience Facility (RRF), financed under the National Recovery Fund budget estimate, RRF-2.3.1-21 funding scheme.

References

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    • Export Citation
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    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
    • Export Citation
  • Andrews, S. (2012): FastQC A Quality Control Tool for High Throughput Sequence Data.

  • Bidmos, F. A., Siris, S., Gladstone, C. A. and Langford, P. R. (2018): Bacterial vaccine antigen discovery in the reverse vaccinology 2.0 era: progress and challenges. Front. Immunol. 9.

    • Search Google Scholar
    • Export Citation
  • Boerlin, P., Travis, R., Gyles, C. L., Reid-Smith, R., Janecko, N., Lim, H., Nicholson, V., McEwen, S. A., Friendship, R. and Archambault, M. (2005): Antimicrobial resistance and virulence genes of Escherichia coli isolates from swine in Ontario. Appl. Environ. Microbiol. 71, 67536761.

    • Search Google Scholar
    • Export Citation
  • Chan, E. Y. (2009): Next-generation sequencing methods: impact of sequencing accuracy on SNP discovery. Methods Mol. Biol. 578, 95111.

    • Search Google Scholar
    • Export Citation
  • Chotinantakul, K., Chusri, P. and Okada, S. (2022): Detection and characterization of ESBL-producing Escherichia coli and additional co-existence with mcr genes from river water in northern Thailand. PeerJ 10, e14408.

    • Search Google Scholar
    • Export Citation
  • Clinical and Laboratory Standards Institute (2018): CLSI. Methods for Dilution Antimicrobial Susceptibility Tests for Bacteria that Grow Aerobically. Wayne, PA, Clinical and Laboratory Standards Institute.

    • Search Google Scholar
    • Export Citation
  • Cohen, S. P., McMurry, L. M. and Levy, S. B. (1988): marA locus causes decreased expression of OmpF porin in multiple-antibiotic-resistant (Mar) mutants of Escherichia coli. J. Bacteriol. 170, 54165422.

    • Search Google Scholar
    • Export Citation
  • El-Attar, L. M. R., Scott, S., Goh, S. and Good, L. (2012): A pestivirus DNA vaccine based on a non-antibiotic resistance Escherichia coli essential gene marker. Vaccine 30, 17021709.

    • Search Google Scholar
    • Export Citation
  • EUCAST, T. (2015): European Committee on Antimicrobial Susceptibility Testing, Breakpoint Tables for Interpretation of MICs and Zone Diameters. Version 5.0, 2015.

    • Search Google Scholar
    • Export Citation
  • Freeman, Z. N., Dorus, S. and Waterfield, N. R. (2013): The KdpD/KdpE two-component system: integrating K+ homeostasis and virulence. PLoS Pathog. 9, e1003201.

    • Search Google Scholar
    • Export Citation
  • Ghenea, A. E., Zlatian, O. M., Cristea, O. M., Ungureanu, A., Mititelu, R. R., Balasoiu, A. T., Vasile, C. M., Salan, A.-I., Iliuta, D., Popescu, M., Udriștoiu, A.-L. and Balasoiu, M. (2022): TEM,CTX-M,SHV genes in ESBL-producing Escherichia coli and Klebsiella pneumoniae isolated from clinical samples in a county clinical emergency hospital Romania-predominance of CTX-M-15. Antibiotics (Basel) 11, 503.

    • Search Google Scholar
    • Export Citation
  • Gunn, J. S., Lim, K. B., Krueger, J., Kim, K., Guo, L., Hackett, M. and Miller, S. I. (1998): PmrA-PmrB-regulated genes necessary for 4-aminoarabinose lipid A modification and polymyxin resistance. Mol. Microbiol. 27, 11711182.

    • Search Google Scholar
    • Export Citation
  • Gurevich, A., Saveliev, V., Vyahhi, N. and Tesler, G. (2013): QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 10721075.

    • Search Google Scholar
    • Export Citation
  • Hata, M., Suzuki, M., Matsumoto, M., Takahashi, M., Sato, K., Ibe, S. and Sakae, K. (2005): Cloning of a novel gene for quinolone resistance from a transferable plasmid in Shigella flexneri 2b. Antimicrob. Agents Chemother. 49, 801803.

    • Search Google Scholar
    • Export Citation
  • Hernández-Fillor, R. E., Brilhante, M., Marrero-Moreno, C. M., Baez, M., Espinosa, I. and Perreten, V. (2021): Characterization of third-generation cephalosporin-resistant Escherichia coli isolated from pigs in Cuba using next-generation sequencing. Microb. Drug Resist. 27, 10031010.

    • Search Google Scholar
    • Export Citation
  • Hyatt, D., Chen, G.-L., Locascio, P. F., Land, M. L., Larimer, F. W. and Hauser, L. J. (2010): Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119.

    • Search Google Scholar
    • Export Citation
  • Johansson, M. H. K., Bortolaia, V., Tansirichaiya, S., Aarestrup, F. M., Roberts, A. P. and Petersen, T. N. (2021): Detection of mobile genetic elements associated with antibiotic resistance in Salmonella enterica using a newly developed web tool: MobileElementFinder. J. Antimicrob. Chemother. 76, 101109.

    • Search Google Scholar
    • Export Citation
  • Krawczyk, P. S., Lipinski, L. and Dziembowski, A. (2018): PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. Nucleic Acids Res. 46, e35.

    • Search Google Scholar
    • Export Citation
  • Krueger, F., James, F., Ewels, P., Afyounian, E. and Schuster-Boeckler, B. (2021): FelixKrueger/TrimGalore: v0.6.7 - DOI via Zenodo. Zenodo.

    • Search Google Scholar
    • Export Citation
  • Li, D., Liu, C.-M., Luo, R., Sadakane, K. and Lam, T.-W. (2015): MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 16741676.

    • Search Google Scholar
    • Export Citation
  • Lister, P. D., Wolter, D. J. and Hanson, N. D. (2009): Antibacterial-resistant Pseudomonas aeruginosa: clinical impact and complex regulation of chromosomally encoded resistance mechanisms. Clin. Microbiol. Rev. 22, 582610.

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    • Export Citation
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Senior editors

Editor-in-Chief: Ferenc BASKA

Editorial assistant: Szilvia PÁLINKÁS

 

Editorial Board

  • Mária BENKŐ (Acta Veterinaria Hungarica, Budapest, Hungary)
  • Gábor BODÓ (University of Veterinary Medicine, Budapest, Hungary)
  • Béla DÉNES (University of Veterinary Medicine, Budapest Hungary)
  • Edit ESZTERBAUER (Veterinary Medical Research Institute, Budapest, Hungary)
  • Hedvig FÉBEL (National Agricultural Innovation Centre, Herceghalom, Hungary)
  • László FODOR (University of Veterinary Medicine, Budapest, Hungary)
  • János GÁL (University of Veterinary Medicine, Budapest, Hungary)
  • Balázs HARRACH (Veterinary Medical Research Institute, Budapest, Hungary)
  • Peter MASSÁNYI (Slovak University of Agriculture in Nitra, Nitra, Slovak Republic)
  • Béla NAGY (Veterinary Medical Research Institute, Budapest, Hungary)
  • Tibor NÉMETH (University of Veterinary Medicine, Budapest, Hungary)
  • Zsuzsanna NEOGRÁDY (University of Veterinary Medicine, Budapest, Hungary)
  • Dušan PALIĆ (Ludwig Maximilian University, Munich, Germany)
  • Alessandra PELAGALLI (University of Naples Federico II, Naples, Italy)
  • Kurt PFISTER (Ludwig-Maximilians-University of Munich, Munich, Germany)
  • László SOLTI (University of Veterinary Medicine, Budapest, Hungary)
  • József SZABÓ (University of Veterinary Medicine, Budapest, Hungary)
  • Péter VAJDOVICH (University of Veterinary Medicine, Budapest, Hungary)
  • János VARGA (University of Veterinary Medicine, Budapest, Hungary)
  • Štefan VILČEK (University of Veterinary Medicine in Kosice, Kosice, Slovak Republic)
  • Károly VÖRÖS (University of Veterinary Medicine, Budapest, Hungary)
  • Herbert WEISSENBÖCK (University of Veterinary Medicine, Vienna, Austria)
  • Attila ZSARNOVSZKY (Szent István University, Gödöllő, Hungary)

ACTA VETERINARIA HUNGARICA
Institute for Veterinary Medical Research
Centre for Agricultural Research
Hungarian Academy of Sciences
P.O. Box 18, H-1581 Budapest, Hungary
Phone: (36 1) 287 7073 (ed.-in-chief) or (36 1) 467 4081 (editor)

E-mail: acta.veterinaria@univet.hu (ed.-in-chief)

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2022  
Web of Science  
Total Cites
WoS
972
Journal Impact Factor 0.900
Rank by Impact Factor

Veterinary Sciences 95/143

Impact Factor
without
Journal Self Cites
0.900
5 Year
Impact Factor
1.1
Journal Citation Indicator 0.47
Rank by Journal Citation Indicator

Veterinary Sciences 103/170

Scimago  
Scimago
H-index
38
Scimago
Journal Rank
0.277
Scimago Quartile Score

Veterinary (miscellaneous) Q2

Scopus  
Scopus
Cite Score
1.9
Scopus
CIte Score Rank
General Veterinary 76/186 (59th PCTL)
Scopus
SNIP
0.475

2021  
Web of Science  
Total Cites
WoS
1040
Journal Impact Factor 0,959
Rank by Impact Factor Veterinary Sciences 103/144
Impact Factor
without
Journal Self Cites
0,876
5 Year
Impact Factor
1,222
Journal Citation Indicator 0,48
Rank by Journal Citation Indicator Veterinary Sciences 106/168
Scimago  
Scimago
H-index
36
Scimago
Journal Rank
0,313
Scimago Quartile Score Veterinary (miscellaneous) (Q2)
Scopus  
Scopus
Cite Score
1,7
Scopus
CIte Score Rank
General Veterinary 79/183 (Q2)
Scopus
SNIP
0,610

2020  
Total Cites 987
WoS
Journal
Impact Factor
0,955
Rank by Veterinary Sciences 101/146 (Q3)
Impact Factor  
Impact Factor 0,920
without
Journal Self Cites
5 Year 1,164
Impact Factor
Journal  0,57
Citation Indicator  
Rank by Journal  Veterinary Sciences 93/166 (Q3)
Citation Indicator   
Citable 49
Items
Total 49
Articles
Total 0
Reviews
Scimago 33
H-index
Scimago 0,395
Journal Rank
Scimago Veterinary (miscellaneous) Q2
Quartile Score  
Scopus 355/217=1,6
Scite Score  
Scopus General Veterinary 73/183 (Q2)
Scite Score Rank  
Scopus 0,565
SNIP  
Days from  145
submission  
to acceptance  
Days from  150
acceptance  
to publication  
Acceptance 19%
Rate

 

2019  
Total Cites
WoS
798
Impact Factor 0,991
Impact Factor
without
Journal Self Cites
0,897
5 Year
Impact Factor
1,092
Immediacy
Index
0,119
Citable
Items
59
Total
Articles
59
Total
Reviews
0
Cited
Half-Life
9,1
Citing
Half-Life
9,2
Eigenfactor
Score
0,00080
Article Influence
Score
0,253
% Articles
in
Citable Items
100,00
Normalized
Eigenfactor
0,09791
Average
IF
Percentile
42,606
Scimago
H-index
32
Scimago
Journal Rank
0,372
Scopus
Scite Score
335/213=1,6
Scopus
Scite Score Rank
General Veterinary 62/178 (Q2)
Scopus
SNIP
0,634
Acceptance
Rate
18%

 

Acta Veterinaria Hungarica
Publication Model Hybrid
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Acta Veterinaria Hungarica
Language English
Size A4
Year of
Foundation
1951
Volumes
per Year
1
Issues
per Year
4
Founder Magyar Tudományos Akadémia
Founder's
Address
H-1051 Budapest, Hungary, Széchenyi István tér 9.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 0236-6290 (Print)
ISSN 1588-2705 (Online)

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