Comprehensive genomic analysis of multidrug-resistant Pseudomonas aeruginosa isolates from companion animals in Germany

Pseudomonas (P.) aeruginosa is a highly adaptable, opportunistic nosocomial pathogen that poses significant risks to public health and veterinary medicine. This bacterium carries a wide range of antimicrobial resistance (AMR) determinants and produces various virulence factors that enable it to invade hosts and increase disease severity. Recognised as a One Health pathogen, P. aeruginosa can be isolated from multiple sources, including humans, animals, food, and the environment. Despite its importance in clinical settings, there are still limited genomic and epidemiological data on P. aeruginosa isolates from companion animals in Germany. To address this knowledge gap, we conducted whole-genome sequencing (WGS) of 72 P. aeruginosa isolates collected in 2023 from various companion animals, including dogs, cats, horses, and rabbits, across Germany. Phenotypic antibiotic susceptibility testing (AST) showed that 97.2% of the isolates exhibited a multidrug-resistant (MDR) phenotype. The highest resistance rates were observed for cefotaxime (98.6%), followed by chloramphenicol (93.1%) and trimethoprim/sulfamethoxazole (87.5%). Fosfomycin resistance was observed in 26.4% of the isolates, despite all isolates carrying the fosA gene. All isolates were found to be susceptible to colistin at increased exposure levels. Resistance to imipenem was detected in four dog isolates. Genome analysis revealed various AMR genes associated with resistance to β-lactams, aminoglycosides, sulfonamides, fluoroquinolones, and phenicols. Multiple virulence-associated genes were also identified, including those involved in biofilm formation, adherence, motility, immune modulation, and exotoxin A production. Moreover, 27.8% of the isolates carried plasmid contigs, and one dog isolate harboured a class 1 integron. In silico multilocus sequence typing (MLST) assigned the 72 isolates to 59 distinct sequence types (STs), including five novel STs (ST5165–ST5169). The most frequently identified STs were ST253, ST258, ST395, and ST244, each represented by three isolates. Six high-risk clones (HRCs) of P. aeruginosa, including ST308, ST277, ST244, ST395, ST253, and ST274, were identified in dog (n = 8) and cat (n = 4) isolates. This study underscores the significant genomic diversity of P. aeruginosa circulating among companion animals and emphasises the need to manage this bacterium within a One Health framework.

1 Introduction

Antimicrobial resistance (AMR) is a growing global threat, posing significant challenges across the human, veterinary, and environmental health sectors (Naghavi et al., 2024). Pseudomonas (P.) aeruginosa is a member of the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, P. aeruginosa, and Enterobacter species), which are recognised as major contributors to nosocomial infections and are able to escape from the biocidal effect of antimicrobials (Jadimurthy et al., 2022). The World Health Organization (WHO) has identified carbapenem-resistant P. aeruginosa as a high-priority pathogen that urgently needs the development of novel antimicrobials (WHO, 2024). P. aeruginosa is a rod-shaped, motile, Gram-negative, non-fermentative, non-spore-forming bacterium belonging to the Pseudomonadaceae family (Qin et al., 2022). It is ubiquitously found across diverse environmental habitats and in both human and animal hosts, where it can act as an opportunistic pathogen (Gellatly and Hancock, 2013). With one of the largest bacterial genomes (5.5–7 Mbp), P. aeruginosa exhibits remarkable metabolic versatility, enabling adaptation to diverse ecological niches (Klockgether et al., 2011). Its genome comprises a conserved core genome, shared by nearly all strains, and a variable accessory genome, present in some strains but absent in others (Kung et al., 2010). The accessory genome contains variable genes acquired through horizontal gene transfer, such as carbapenemases. These genes are often found on mobile genetic elements like plasmids and integrons (Botelho et al., 2019). Resistance development in this bacterium results from a combination of intrinsic and acquired mechanisms (Breidenstein et al., 2011). The intrinsic resistance of P. aeruginosa mainly stems from the low permeability of its outer membrane, increased efflux, and the production of antibiotic-inactivating enzymes, such as AmpC β-lactamase (Breidenstein et al., 2011). Acquired resistance arises from horizontal gene transfer and mutations (Breidenstein et al., 2011; Pang et al., 2019). Additionally, P. aeruginosa’s ability to form biofilms allows it to resist antibiotics, evade host immune responses, and survive in harsh environments (Thi et al., 2020). Furthermore, P. aeruginosa produces a variety of virulence factors that play crucial roles in its pathogenic process, promoting bacterial adhesion and colonisation, suppressing host immune responses, and enabling immune evasion (Liao et al., 2022). P. aeruginosa is a major cause of nosocomial infections, including ventilator-associated pneumonia and bloodstream infections, especially among immunocompromised individuals and patients with underlying conditions such as cystic fibrosis (Reynolds and Kollef, 2021; Tümmler, 2022). Certain P. aeruginosa STs, such as ST308, ST244, and ST277, are denominated as ‘high-risk’ due to their widespread presence in hospitals worldwide and their frequent association with MDR/extensively drug-resistant strains (Oliver et al., 2015).

In Germany, several nosocomial outbreaks have been linked to P. aeruginosa infection (Panzig et al., 1999; Pitten et al., 2001; Eckmanns et al., 2008; Schärer et al., 2023; Rath et al., 2024). In companion animals and livestock, it has been associated with various infections, including otitis externa, conjunctivitis, urinary tract infections, mastitis in dairy cows, and endometritis in horses (Haenni et al., 2015; Morris et al., 2017; Sousa et al., 2021; Nielsen et al., 2022a). An assessment by the European Food Safety Authority (EFSA) has identified P. aeruginosa as one of the most important AMR bacteria in the EU for dogs and cats (Nielsen et al., 2022a). The emergence of MDR and carbapenem-resistant strains, along with the co-occurrence of multiple AMR genes in companion animal isolates, has been reported in several studies (Haenni et al., 2017; Hyun et al., 2018). However, comprehensive molecular epidemiological data on P. aeruginosa isolates from companion animals remain limited. This study addresses this gap by using whole-genome sequencing (WGS) to characterize the genotypic, phenotypic, and virulence profiles of P. aeruginosa strains from companion animals in Germany.

2 Materials and methods

2.1 Bacterial isolates and identification

This study analysed 72 P. aeruginosa isolates obtained from IDEXX laboratories in Kornwestheim, Germany. The isolates were recovered in 2023 from companion animals across Germany. Specifically, 23 isolates (32%) originated from North Rhine-Westphalia, 10 (13.9%) from Baden-Wuerttemberg, 9 (12.5%) from Bavaria, 9 (12.5%) from Lower Saxony, 6 (8.3%) from Hesse, and 4 (5.6%) from Schleswig-Holstein. Two isolates (2.8%) each were obtained from the Rhineland-Palatinate, Brandenburg, and Thuringia. Additionally, one isolate (1.4%) originated from each of the following federal states: Saxony, Saxony-Anhalt, Berlin, Bremen, and Saarland (Supplementary Table S1). Each isolate originated from a unique sample obtained from a companion animal at a different isolation site (Table 1). Sample metadata are listed in Supplementary Table S1.

Table 1

Table 1. Host, isolation site, number of isolates, and geographical origin of P. aeruginosa isolates (n = 72) in the current study.

Microbial species identification was performed using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) with UltrafleXtreme instrument (Bruker Daltonics, Bremen, Germany) as previously described (Murugaiyan et al., 2024). Species identification was conducted using the MALDI Biotyper software according to the manufacturer’s instructions. Identification scores ranged from 0 to 3, with log scores ≥ 2.300 considered reliable for species-level identification. The genus and species identity of each isolate was confirmed using WGS data analysed with Kraken2 (v2.0.7_beta) (Wood et al., 2019), with the genus and species identified by the first match with the highest percentage.

2.2 Antibiotic susceptibility testing (AST)

The AST of 72 P. aeruginosa isolates was performed using the broth microdilution method with the automated MICRONAUT-S system (MICRONAUT, MERLIN Diagnostics GmbH, Bornheim-Hersel, Germany), as previously described by Rizk et al. (2024). Antibiotics were rehydrated with a standardized bacterial suspension adjusted to 0.5 McFarland, and 50 μL were inoculated into MICRONAUT-S MDR MRGN screening plates containing predefined antimicrobial concentrations using Mueller–Hinton broth. Plates were incubated at 37 °C for 24 h, after which minimum inhibitory concentrations (MICs) were automatically determined using a photometric reader and MICRONAUT-S software. MIC values were interpreted according to the Clinical and Laboratory Standards Institute (CLSI) guidelines (Lewis et al., 2023). The MICRONAUT-S software automatically categorised isolates as susceptible, susceptible at increased exposure, or resistant. The test panel included eight antibiotic classes, encompassing a total of 16 antibiotics, namely ciprofloxacin, levofloxacin, amikacin, colistin, chloramphenicol, fosfomycin, piperacillin/tazobactam, trimethoprim/sulfamethoxazole, piperacillin, tigecycline, ceftolozane/tazobactam, ceftazidime, ceftazidime/avibactam, cefotaxime, imipenem, and meropenem. Quality was ensured using standard reference strains Escherichia coli (ATCC 25922), P. aeruginosa (ATCC 27853), and Staphylococcus aureus (ATCC 29213).

2.3 WGS and in silico detection of AMR and virulence-associated genes

Genomic DNA was extracted from pure colonies cultured overnight on Columbia blood agar at 37 °C using the High Pure PCR Template Preparation Kit (Roche Diagnostics GmbH, Mannheim, Germany) according to the manufacturer’s instructions. Sequencing libraries were prepared using the Nextera XT DNA Library Preparation Kit (Illumina, Inc., San Diego, CA, USA), and paired-end sequencing was conducted by an Illumina MiSeq sequencer (Illumina Inc., San Diego, CA, USA). The raw sequencing data were analysed using the Linux-based pipeline WGSBAC (v2.2.3, available at: https://gitlab.com/FLI_Bioinfo/WGSBAC, accessed on 15 January 2025), following the methodology outlined by Linde et al. (2020).

The raw data quality assessment was performed using FastQC, available at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Genome assembly was carried out using the Shovill assembler, employing the SPAdes algorithm (Bankevich et al., 2012). Species identification and potential contaminant detection were conducted using Kraken2. To identify virulence-associated genes, ABRIcate was employed in conjunction with the Virulence Factor Database (VFDB) (Chen et al., 2005). For the analysis of AMR genes, ABRIcate was used with the Comprehensive Antibiotic Resistance Database (CARD) (Jia et al., 2017), ResFinder (Zankari et al., 2012), and the AMRFinderPlus tool from NCBI (Feldgarden et al., 2020). MLST was performed using the mlst software (v2.16.1, available at: https://github.com/tseemann/mlst), applying the species-specific scheme for P. aeruginosa from PubMLST as established by Curran et al. (2004). Core-genome single-nucleotide polymorphisms (cgSNPs) were identified using Snippy v4.6.0,¹ with P. aeruginosa PAO1 (GCF_000006765.1) or P. aeruginosa PA14 (GCF_025490475.1) serving as the reference genome. The phylogenetic tree was constructed using RAxML v8.2.12 (Stamatakis, 2014) based on the cgSNP alignment and visualised using Microreact (Argimón et al., 2016).

To compare the isolates with previously reported German strains, the NCBI Short Read Archive (SRA) was searched for paired-read Illumina sequencing data of P. aeruginosa isolates (n = 62) from Germany (accessed on 19 October 2024). To ensure data integrity, the datasets were examined for contamination using ConFinder (Low et al., 2019), and only uncontaminated datasets were used for further analysis. The genomic sequences obtained from the downloaded datasets were included in the cgSNP analysis alongside the isolate collection. Plasmid contigs were detected using Platon version 1.7 (Schwengers et al., 2020). All the isolates were screened for “class 1–3” integrons using in silico PCR² with primers by Zarei-Yazdeli et al. (2018) and Delarampour et al. (2020), and the tool IntegronFinder v2.0.6 (Néron et al., 2022). Pangenome analysis was conducted using Panaroo v1.5.2 (Tonkin-Hill et al., 2020) based on genome annotations using Bakta v1.9.3 (Schwengers et al., 2021).

3 Results

3.1 Bacterial identification and whole genome sequencing data

MALDI-TOF MS identified all isolates as P. aeruginosa at the species level, and this identification was confirmed by WGS. Genome sequencing of all isolates yielded an average of 1,540,439 reads per isolate, with a range of 785,722 – 2,591,506. The isolates’ mean coverage was 66-fold, ranging from 34-fold to 115-fold. Genome assembly revealed genome sizes ranging from 6,204,466 bp to 7,073,023 bp. The average GC content of the sequenced genomes was 66.31%. The mean N50 value for the 72 assembled genomes was 193,539 bp, ranging from 110,797 to 380,199 bp (Supplementary Table S1).

The pangenome analysis identified a total of 12,868 genes, distributed between a core genome (38%; 4,870 core genes) and an accessory genome (62%), which includes 285 soft core genes, 1,139 shell genes, and 6,574 cloud genes, as shown in Figure 1.

Figure 1

Figure 1. Pie chart showing the fractions of the core, soft core, shell, and cloud genes identified among P. aeruginosa isolates in the current study.

3.2 Antibiotic susceptibility testing (AST)

AST analysis revealed variable phenotypic resistance patterns to the tested antibiotics (Table 2). Seventy isolates (97.2%) were multidrug-resistant (MDR) (resistant to at least one antibiotic in three or more different antibiotic classes). The highest AMR rates were observed for cefotaxime (98.6%, n = 71), followed by chloramphenicol (93.1%, n = 67), trimethoprim/sulfamethoxazole (87.5%, n = 63), and fosfomycin (26.4%, n = 19). Four dog isolates (5.6%) were imipenem-resistant. All isolates were susceptible to piperacillin, piperacillin/tazobactam, ceftazidime, ceftazidime/avibactam, and ceftolozane/tazobactam. All isolates were phenotypically susceptible at increased exposure to colistin.

Table 2

Table 2. Antibiotic susceptibility profiles of the P. aeruginosa (n = 72) isolates in the current study.

3.3 In silico detection of MLST, AMR, and virulence genes

MLST analysis assigned the isolates to 59 sequence types (STs); of them, five were novel STs (ST5165-ST51699; PubMLST database, accessed on 15 November 2024). The novel STs were isolated from wound and ear swabs of dogs across Rhineland-Palatinate, Lower Saxony, North Rhine-Westphalia, and Bavaria. The predominant STs among all isolates were ST253, ST258, ST395, and ST244, each found in three isolates. These were followed by ST195, ST1033, ST499, ST4694, and ST360, each identified in two isolates. Each of the remaining STs was represented by a single isolate (Supplementary Table S1). MLST analysis furthermore revealed that 12 MDR P. aeruginosa isolates could be assigned to 6 high-risk clones (HRCs) of P. aeruginosa STs. These included ST308 (n = 1), ST244 (n = 3), ST277 (n = 1), ST395 (n = 3), ST253 (n = 3), and ST274 (n = 1). These STs were identified in dog (n = 8) and cat (n = 4) isolates as shown in Table 3. The geographical distribution of the identified STs, including HRCs, in the current study is shown in Figure 2.

Table 3

Table 3. High-risk clones of P. aeruginosa identified in the current study.

Figure 2

Figure 2. Geographical distribution of P. aeruginosa STs, including high-risk clones (shown in bold), identified in companion animal isolates across Germany in this study. Numbers on the map represent the STs detected in each federal state. Figure has been created by www.mapchart.net and Inkscape.

Genome analysis identified 80 AMR genes associated with various resistance mechanisms, including antibiotic inactivation, efflux pumps, antibiotic target alteration, and decreased antibiotic permeability. These genes confer resistance to several antibiotic classes, including β-lactams, aminoglycosides, fluoroquinolones, fosfomycin, and sulfonamides. The number of resistance genes per isolate ranged from 46 to 51, with the highest prevalence observed in a cat isolate from Bavaria (ST640). Thirty-nine efflux pump-associated genes were identified, and 36 of them were present in all isolates. All isolates harboured the catB7 gene conferring resistance to phenicols, the fosA gene associated with fosfomycin resistance, the arnA and basS genes, conferring resistance to peptides, and the bcr-1 gene associated with bicyclomycin resistance. A total of 32 β-lactamase resistance genes were identified. The most prevalent was bla_PDC.374, identified in 65% (47/72) of the isolates, followed by bla_PAO-1, found in 40.3% (29/72) of isolates. The bla_PDC.19a gene was detected once in an HRC cat isolate (ST308), while the aadA7 gene, linked to aminoglycoside resistance, and sul1 associated with sulfonamide resistance, were identified exclusively in a HRC dog isolate (ST253). The fluoroquinolone resistance gene crpP was found in 34 isolates. Five aminoglycoside resistance genes were identified: aph(3′)-IIb, aph(3″)-Ib, aph(3′)-Ia, aph(6)-Id, and aadA7. The aph(3′)-IIb gene was present in all isolates, while aph(3″)-Ib, aph(3′)-Ia, and aph(6)-Id, were detected only in a cat isolate from Bavaria. Although the fosA gene was detected in all isolates, only 30 exhibited phenotypic resistance to fosfomycin. Seven isolates harboured a point mutation (V15I) in the sensor kinase gene pmrB, a component of the pmrAB two-component regulatory system, which can influence colistin susceptibility. However, all isolates remained phenotypically susceptible at increased exposure to colistin (Supplementary Table S1). Further, genome analysis identified 321 virulence-associated genes. The genes associated with effector delivery systems were the most prevalent (n = 111), followed by nutritional/metabolic factors (n = 54), motility (n = 53), adherence (n = 43), biofilm formation (n = 33), immune modulation (n = 24), exotoxin (n = 2), and exoenzyme (n = 1). The number of virulence-associated genes per isolate ranged from 249 to 313, with the highest count (n = 313) observed in an HRC (ST244) isolate from a dog in Hesse. The Type III secretion system (T3SS) effector genes were identified, including exoU, detected in eight isolates, four of which belonged to P. aeruginosa HRCs (ST253 and ST308). The pore-forming toxin gene exlA was detected in four isolates, whereas the toxA gene was present in 63 isolates. Detailed information is provided in Supplementary Table S1.

3.4 Detection of plasmids and integrons

WGS using Platon predicted that 27.8% (20/72; 18 from dogs and 2 from cats) of the isolates harboured a total of 24 plasmid contigs, with the number of contigs varying among isolates (Table 4). The plasmids ranged in size from ~3.3 to 174 kbp. Isolates carrying plasmid contigs belonged to various STs, with 25% (5/20) identified as HRCs (ST277, ST274, ST253, ST244). Further AST analysis showed that 19 of 20 of these isolates demonstrated an MDR phenotype. In addition, all 20 isolates carried 47–50 AMR genes and 249–312 virulence genes per isolate.

Table 4

Table 4. Plasmid contigs distribution among P. aeruginosa (n = 72) isolates in the current study.

In silico PCR analysis identified one isolate from a dog in Lower Saxony that harboured a class 1 integron carrying sul1 (associated with sulfonamide resistance) and aadA7 (associated with aminoglycoside resistance) genes. This isolate also carried plasmid contigs and belonged to the P.aeruginosa HRC ST253.

3.5 Phylogenetic analysis of P. aeruginosa isolates

Core-genome single-nucleotide polymorphism (cgSNP) analysis showed considerable genetic diversity among the isolates. However, two isolates from dogs, one from Lower Saxony (23Y0081) and the other from North Rhine-Westphalia (23Y0008), were genetically identical (cgSNP difference = 0). Both belonged to ST4694 and carried the same number of AMR and virulence genes. Additionally, two further dog isolates from Bavaria (23Y0071) and North Rhine-Westphalia (23Y0077) showed close genetic relatedness (cgSNP difference = 7), were assigned to ST258, and displayed identical AMR profiles along with the same number of AMR and virulence genes. Due to the large number and variety of isolates, separate phylogenetic trees were created to provide a clearer visualisation of the genetic relationships (see Figures 3, 4). Several isolates clustered with P. aeruginosa isolates of human origin across Germany (Supplementary Table S1; Figures 3, 4). For example, a dog isolate from Brandenburg (23Y0066; HRC ST244) showed genetic relatedness to a human isolate from Hesse, differing by 15 cgSNPs. Likewise, a cat isolate from Bavaria (23Y0174; HRC ST395) showed genetic relatedness to a human isolate from Baden-Wuerttemberg, differing by 16 cgSNPs. Another cat isolate from North Rhine-Westphalia (23Y0171; HRC ST395) was genetically related to a human isolate from Baden-Wuerttemberg, differing by 21 SNPs.

Figure 3

Figure 3. Maximum likelihood phylogenetic tree of P. aeruginosa isolates from Germany based on cgSNP. Leaf colors indicate the host. Isolates from this study are shown in bold, MLST sequence types (STs: 3700, 108, 186, 2,123, 3,784, 499, 5,168, 3,160, 5,060, 640, 244, 611, 245, 360, 277, 4,093, 646, 487, 2,408, 242, 676, 1,031, 5,167, 270, 164, 399, 524, 231, 2,451, 569, 1,229, 209, 274, 1,145, 395, 1,033, 155, 2,438, 5,165, 480, 258, 3,323, 189, 4,694, 701, 4,290, 308, 207, 1,146, 1,197, and 253) are indicated and HRCs are highlighted in bold blue. The bar at the bottom indicates base changes per site (BW: Baden-Wuerttemberg, BY: Bavaria, BE: Berlin, BB: Brandenburg, HE: Hesse, NI: Lower Saxony, NW: North Rhine-Westphalia, RP: Rhineland-Palatinate, SL: Saarland, SN: Saxony, ST: Saxony-Anhalt, SH: Schleswig-Holstein, TH: Thuringia).

Figure 4

Figure 4. Maximum likelihood phylogenetic tree of P. aeruginosa isolates from Germany based on cgSNP. Leaf colors indicate the host. Isolates from this study are labeled in bold, and MLST sequence types (STs: 5169, 3,703, 5,166, 195, 2,477, 2,548, 2,683, and 679) are indicated. The bar at the bottom indicates base changes per site (BW: Baden-Wuerttemberg; BY: Bavaria; BE: Berlin; HB: Bremen; HE: Hesse; NI: Lower Saxony; NW: North Rhine-Westphalia; SN: Saxony; TH: Thuringia).

4 Discussion

The current study provides a comprehensive genomic and phenotypic characterization of 72 P. aeruginosa isolates from various companion animal species across Germany. The isolates were obtained from various sampling sites, predominantly from dog ear infections. This finding is in agreement with previous studies that identified P. aeruginosa as a common cause of otitis in dogs (Nielsen et al., 2022b; Newstead et al., 2025). The isolates were collected from a diagnostic laboratory and, therefore, may not accurately reflect the prevalence, or MDR rate in the general companion animal population. AST analysis revealed a high prevalence of MDR isolates, with 97.2% resistant to at least one antibiotic in three various antibiotic classes. This aligns with prior findings by Nolff et al. (2016), reporting a high prevalence of MDR P. aeruginosa in isolates from open wounds in dogs and cats, in contrast to another study by Feßler et al. (2022), which reported no MDR among P. aeruginosa isolates from dogs and cats. Such discrepancies in MDR prevalence may be influenced by several factors, including prior antibiotic treatment, the spectrum and number of antibiotics tested, and geographic origin. In the current study, we used MICRONAUT-S MDR MRGN screening plates that contain a defined set of antibiotics for all MDRGN bacteria, including antipseudomonal therapy and other antibiotics. These agents were included primarily for resistance profiling and comparative purposes, not for clinical use as antipseudomonal therapy. We observed high resistance rates to cefotaxime, chloramphenicol, and trimethoprim/sulfamethoxazole. The widespread use of these broad-spectrum antibiotics in companion animals may contribute to the elevated resistance rates observed. In contrast, susceptibility to key antipseudomonal agents such as piperacillin/tazobactam and ceftazidime was observed, indicating that effective therapeutic options are still available. Nonetheless, ongoing antimicrobial stewardship is crucial to prevent further resistance development (Bassetti et al., 2022). Carbapenem resistance was detected in four dog isolates from geographically distinct federal states (Lower Saxony, Baden-Wuerttemberg, Saxony-Anhalt, and North Rhine-Westphalia), consistent with a previous study from Germany by Dalponte et al. (2025), which reported one imipenem-resistant isolate among 51 P. aeruginosa isolates from dogs. Carbapenems are a key antibiotic class in human medicine, and are designated by the European Medicines Agency (EMA) as Category A (“Avoid”) and not permitted for routine veterinary use in the European Union (EU) (EMA, 2020). The detection of carbapenem-resistant strains in companion animals is a significant public health concern, and suggests the possible circulation of transferable resistance genes between humans, animals, and the environment (Fernandes et al., 2018; Despotovic et al., 2023).

MLST analysis revealed substantial genetic diversity among isolates, with 59 STs identified, including five novel and six MDR HRCs (ST308, ST244, ST277, ST395, ST253, and ST274). The detection of HRCs in companion animals raises concerns about the potential transmission of P. aeruginosa from humans to these animals. This is supported by cgSNP analysis results, showing genetic relatedness between certain companion animal and human isolates, and consistent with the hypothesis of shared P. aeruginosa populations between humans and animals (Fernandes et al., 2018). HRCs of P. aeruginosa are disseminated in hospital settings worldwide (Oliver et al., 2015) and have also been detected in environmental sources, including drinking water networks (Horikian et al., 2024). In Germany, HRCs have previously been reported in both human clinical isolates and the environment (Table 5).

Table 5

Table 5. Overview of the high-risk P. aeruginosa clones (ST308, ST277, ST244, ST395, ST253, and ST274) identified in the current study and reported in previous studies in Germany.

The presence of AMR genes linked to multiple resistance mechanisms, including antibiotic inactivation, efflux pumps, target alteration, and reduced membrane permeability, underscores the diverse mechanisms contributing to AMR in German P. aeruginosa (Giovagnorio et al., 2023). Although all isolates harboured the fosA gene, conferring resistance to fosfomycin, only 26.4% were phenotypically resistant to fosfomycin. This finding suggests that the fosA gene alone may not be sufficient to confer fosfomycin resistance, and additional regulatory or expression factors might affect fosfomycin susceptibility (Laborda et al., 2022). Chromosomal fosA genes provide high-level resistance to fosfomycin in Gram-negative bacteria (Ito et al., 2017a). However, they do not confer resistance in 73.6% of current P. aeruginosa isolates. Some studies have shown that fosA inhibitors, like sodium phosphonoformate (PPF), can block the activity of chromosomally encoded fosA in certain Gram-negative strains (Ito et al., 2017b). Notably, some of these fosA inhibitors are already approved as antiviral drugs. Exposure to such inhibitors may explain why some strains remain susceptible to fosfomycin despite carrying the fosA gene (Wareth et al., 2022). Similarly, approximately 10% of isolates harboured a point mutation (V15I) in the pmrB gene, which mediates colistin resistance (Bolard et al., 2019); however, all isolates remained phenotypically susceptible at increased exposure to colistin.

The virulence profiling of isolates revealed a wide range of virulence factors. The most prevalent factors were associated with effector delivery systems that secrete proteins promoting immune evasion and supporting bacterial colonisation (Sharma et al., 2017). Among these, the Type III secretion system (T3SS) effector proteins, exoS, exoT, exoY, and exoU were identified, contributing to the disruption of host cell signaling, cytotoxicity, and immune evasion (Sousa et al., 2021). Other important virulence determinants, including the pore-forming toxin exlA, which disrupts host cell membranes (Liao et al., 2022), and the toxA gene, which regulates exotoxin A synthesis and inhibits host protein synthesis (Qin et al., 2022), were also identified. The diversity of virulence factors in P. aeruginosa facilitates adaptation to various host environments, immune evasion, and successful colonisation (Liao et al., 2022). Plasmid analysis revealed that 27.8% of isolates carried plasmid contigs, of which 25% belonged to HRCs. Plasmids facilitate the horizontal transfer of AMR genes among bacterial populations (Odumosu et al., 2016). Furthermore, a class 1 integron carrying two integron-associated genes (aadA7 and sul1) was identified in a single HRC (ST253)- dog isolate, demonstrating that P. aeruginosa from dogs can harbour mobile genetic elements that contribute to the dissemination of antibiotic resistance. This finding aligns with a prior study reporting the same two integron-associated genes in dog isolates (Secker et al., 2025).

Although genome sizes varied among isolates, the number of AMR and virulence genes did not differ significantly. This variation in genome sizes may be attributed to the accessory genome (Klockgether et al., 2011), which accounted for 62% of the total genome in the current study, consistent with previous studies reporting that the accessory genome represents 75% of the total P. aeruginosa genome (Mosquera-Rendón et al., 2016). Generally, P. aeruginosa harbours a wide range of accessory genes, many of them are acquired by horizontal gene transfer mediated through mobile genetic elements (Freschi et al., 2019). These accessory genes often carry antibiotic resistance and virulence determinants, and such flexibility highlights the ability of P. aeruginosa to adapt to various environments and acquire traits that increase its pathogenic potential and antibiotic resistance (Dechathai et al., 2025).

5 Conclusion

The current study identified high genetic diversity among P. aeruginosa isolates from companion animals across Germany, including five novel STs (ST5165–ST5169) and six MDR high-risk clones. Several isolates showed genetic relatedness to P. aeruginosa strains recovered from humans in Germany; this finding highlights the potential transmission of P. aeruginosa between human and animal populations and emphasises the importance of the One Health approach in studying P. aeruginosa in Germany. Almost all isolates were MDR and carried a wide variety of AMR genes conferring resistance to β-lactams, aminoglycosides, fluoroquinolones, fosfomycin, and sulfonamides. However, a discrepancy between the phenotype and genotype was observed in some isolates.

Carbapenem-resistant strains were detected in four dog isolates from geographically separated federal states, despite carbapenems not being authorized for routine veterinary use in the EU. The presence of a diverse array of virulence factors and mobile genetic elements, such as class 1 integrons and plasmid contigs, in isolates of animal origin highlights the potential horizontal transmission of AMR genes. Overall, these findings underscore the need for continuous surveillance and stringent antimicrobial stewardship in veterinary medicine to mitigate the spread of AMR. Further research is needed to understand the drivers of resistance in animal-origin P. aeruginosa, develop effective prevention and control strategies, and reduce the risk of zoonotic transmission.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.

Ethics statement

Ethical approval was not required for the studies on animals in accordance with the local legislation and institutional requirements because only commercially available established cell lines were used.

Author contributions

MB: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. HB: Methodology, Software, Validation, Writing – review & editing. IS: Methodology, Resources, Writing – review & editing. PAK: Methodology, Resources, Writing – review & editing. HN: Conceptualization, Data curation, Supervision, Validation, Writing – review & editing. LDS: Conceptualization, Data curation, Supervision, Validation, Writing – review & editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Acknowledgments

We thank Dr. Mostafa Y. Abdel-Glil for the plasmid analysis and his support on this work.

Conflict of interest

IS and PAK were employed by the Vet Med Labor GmbH, IDEXX Laboratories, Kornwestheim, Germany.

The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2026.1754860/full#supplementary-material

Supplementary Table S1 | The summary of WGS data, metadata, MLST, AST results, AMR genes, Virulence genes, plasmid analysis, and SNP typing of 72 P. aeruginosa isolates.

Footnotes

¹https://github.com/tseemann/snippy

²https://github.com/egonozer/in_silico_pcr

References

Argimón, S., Abudahab, K., Goater, R. J. E., Fedosejev, A., Bhai, J., Glasner, C., et al. (2016). Microreact: visualizing and sharing data for genomic epidemiology and phylogeography. Microb Genom 2:e000093. doi: 10.1099/mgen.0.000093,

PubMed Abstract | Crossref Full Text | Google Scholar

Bankevich, A., Nurk, S., Antipov, D., Gurevich, A. A., Dvorkin, M., Kulikov, A. S., et al. (2012). SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477. doi: 10.1089/cmb.2012.0021,

PubMed Abstract | Crossref Full Text | Google Scholar

Bassetti, M., Magnè, F., Giacobbe, D. R., Bini, L., and Vena, A. (2022). New antibiotics for gram-negative pneumonia. Eur. Respir. Rev. 31:2022. doi: 10.1183/16000617.0119-2022,

PubMed Abstract | Crossref Full Text | Google Scholar

Bolard, A., Schniederjans, M., Haüssler, S., Triponney, P., Valot, B., Plésiat, P., et al. (2019). Production of Norspermidine contributes to aminoglycoside resistance in pmrAB mutants of Pseudomonas aeruginosa. Antimicrob. Agents Chemother. 63:19. doi: 10.1128/AAC.01044-19,

PubMed Abstract | Crossref Full Text | Google Scholar

Botelho, J., Grosso, F., and Peixe, L. (2019). Antibiotic resistance in Pseudomonas aeruginosa – mechanisms, epidemiology and evolution. Drug Resist. Updat. 44:100640. doi: 10.1016/j.drup.2019.07.002

Crossref Full Text | Google Scholar

Breidenstein, E. B., Fuente-Núñez, C., and Hancock, R. E. (2011). <i>Pseudomonas aeruginosa</i>: all roads lead to resistance. Trends Microbiol. 19, 419–426. doi: 10.1016/j.tim.2011.04.005,

PubMed Abstract | Crossref Full Text | Google Scholar

Chen, L., Yang, J., Yu, J., Yao, Z., Sun, L., Shen, Y., et al. (2005). VFDB: a reference database for bacterial virulence factors. Nucleic Acids Res. 33:D325-8. doi: 10.1093/nar/gki008

Crossref Full Text | Google Scholar

Curran, B., Jonas, D., Grundmann, H., Pitt, T., and Dowson, C. G. (2004). Development of a multilocus sequence typing scheme for the opportunistic pathogen Pseudomonas aeruginosa. J. Clin. Microbiol. 42, 5644–5649. doi: 10.1128/JCM.42.12.5644-5649.2004,

PubMed Abstract | Crossref Full Text | Google Scholar

Dalponte, A., Filor, V., Lübke-Becker, A., Fulde, M., Alter, T., Müsken, M., et al. (2025). Characterization and purification of Pseudomonas aeruginosa phages for the treatment of canine infections. BMC Microbiol. 25:289. doi: 10.1186/s12866-025-04005-4,

PubMed Abstract | Crossref Full Text | Google Scholar

Delarampour, A., Ghalehnoo, Z. R., Khademi, F., and Vaez, H. (2020). Antibiotic resistance patterns and prevalence of class I, II and III Integrons among clinical isolates of Klebsiella pneumoniae. Infez Med. 28, 64–69.

Google Scholar

Dechathai, T., Singkhamanan, K., Yaikhan, T., Chusri, S., Pomwised, R., Wonglapsuwan, M., et al. (2025). Comprehensive genomic analysis of Pseudomonas aeruginosa PSU9449 isolated from a clinical case in Thailand. Antibiotics 14:530. doi: 10.3390/antibiotics14060530,

PubMed Abstract | Crossref Full Text | Google Scholar

Despotovic, M., Nies, L.de, Busi, S. B., and Wilmes, P. 2023 Reservoirs of antimicrobial resistance in the context of one health Curr. Opin. Microbiol. 73:102291 doi: 10.1016/j.mib.2023.102291,

PubMed Abstract | Crossref Full Text | Google Scholar

Eckmanns, T., Oppert, M., Martin, M., Amorosa, R., Zuschneid, I., Frei, U., et al. (2008). An outbreak of hospital-acquired Pseudomonas aeruginosa infection caused by contaminated bottled water in intensive care units. Clin. Microbiol. Infect. 14, 454–458. doi: 10.1111/j.1469-0691.2008.01949.x,

PubMed Abstract | Crossref Full Text | Google Scholar

EMA (2020). European Medicines Agency (EMA). Categorisation of antibiotics for use in animals for prudent and responsible use. https://www.ema.europa.eu/en/documents/report/infographic-categorisation-antibiotics-use-animals-prudent-responsible-use_en.pdf

Google Scholar

Feldgarden, M., Brover, V., Haft, D. H., Prasad, A. B., Slotta, D. J., Tolstoy, I., et al. (2020). Erratum for Feldgarden et al., "validating the AMRFinder tool and resistance gene database by using antimicrobial resistance genotype-phenotype correlations in a collection of isolates". Antimicrob. Agents Chemother. 64:20. doi: 10.1128/AAC.00361-20,

PubMed Abstract | Crossref Full Text | Google Scholar

Fernandes, M. R., Sellera, F. P., Moura, Q., Carvalho, M. P. N., Rosato, P. N., Cerdeira, L., et al. (2018). Zooanthroponotic transmission of drug-resistant Pseudomonas aeruginosa, Brazil. Emerg. Infect. Dis. 24, 1160–1162. doi: 10.3201/eid2406.180335,

PubMed Abstract | Crossref Full Text | Google Scholar

Feßler, A. T., Scholtzek, A. D., Schug, A. R., Kohn, B., Weingart, C., Hanke, D., et al. (2022). Antimicrobial and biocide resistance among canine and feline Enterococcus faecalis, Enterococcus faecium, Escherichia coli, Pseudomonas aeruginosa, and Acinetobacter baumannii isolates from diagnostic submissions. Antibiotics 11:152. doi: 10.3390/antibiotics11020152,

PubMed Abstract | Crossref Full Text | Google Scholar

Freschi, L., Vincent, A. T., Jeukens, J., Emond-Rheault, J.-G., Kukavica-Ibrulj, I., Dupont, M.-J., et al. (2019). The Pseudomonas aeruginosa Pan-genome provides new insights on its population structure, horizontal gene transfer, and pathogenicity. Genome Biol. Evol. 11, 109–120. doi: 10.1093/gbe/evy259,

PubMed Abstract | Crossref Full Text | Google Scholar

Gellatly, S. L., and Hancock, R. E. W. (2013). Pseudomonas aeruginosa: new insights into pathogenesis and host defenses. Pathog. Dis. 67, 159–173. doi: 10.1111/2049-632X.12033,

PubMed Abstract | Crossref Full Text | Google Scholar

Giovagnorio, F., Vito, A.de, Madeddu, G., Parisi, S. G., and Geremia, N. 2023 Resistance in Pseudomonas aeruginosa: a narrative review of antibiogram interpretation and emerging treatments Antibiotics 12:621 doi: 10.3390/antibiotics12111621,

PubMed Abstract | Crossref Full Text | Google Scholar

Haenni, M., Bour, M., Châtre, P., Madec, J.-Y., Plésiat, P., and Jeannot, K. (2017). Resistance of animal strains of Pseudomonas aeruginosa to Carbapenems. Front. Microbiol. 8:1847. doi: 10.3389/fmicb.2017.01847,

PubMed Abstract | Crossref Full Text | Google Scholar

Haenni, M., Hocquet, D., Ponsin, C., Cholley, P., Guyeux, C., Madec, J.-Y., et al. (2015). Population structure and antimicrobial susceptibility of Pseudomonas aeruginosa from animal infections in France. BMC Vet. Res. 11:9. doi: 10.1186/s12917-015-0324-x,

PubMed Abstract | Crossref Full Text | Google Scholar

Horikian, A., Jeanvoine, A., Amarache, A., Tourtet, M., Ory, J., Boulestreau, H., et al. (2024). High-risk clones of Pseudomonas aeruginosa contaminate the drinking water networks of French cities. NPJ Clean Water 7:323. doi: 10.1038/s41545-024-00323-8

Crossref Full Text | Google Scholar

Hyun, J.-E., Chung, T.-H., and Hwang, C.-Y. (2018). Identification of VIM-2 metallo-β-lactamase-producing Pseudomonas aeruginosa isolated from dogs with pyoderma and otitis in Korea. Vet. Dermatol. 29, 186–e68. doi: 10.1111/vde.12534,

PubMed Abstract | Crossref Full Text | Google Scholar

Ito, R., Mustapha, M. M., Tomich, A. D., Callaghan, J. D., McElheny, C. L., Mettus, R. T., et al. (2017a). Widespread fosfomycin resistance in gram-negative bacteria attributable to the chromosomal fosA gene. MBio 8:e00749-17.

Google Scholar

Ito, R., Tomich, A. D., McElheny, C. L., Mettus, R. T., Sluis-Cremer, N., and Doi, Y. (2017b). Inhibition of fosfomycin resistance protein FosA by phosphonoformate (foscarnet) in multidrug-resistant gram-negative pathogens. Antimicrob. Agents Chemother. 61:e01424-17.

Google Scholar

Jadimurthy, R., Mayegowda, S. B., Nayak, S. C., Mohan, C. D., and Rangappa, K. S. (2022). Escaping mechanisms of ESKAPE pathogens from antibiotics and their targeting by natural compounds. Biotechnol. Rep. (Amst.) 34:e00728. doi: 10.1016/j.btre.2022.e00728,

PubMed Abstract | Crossref Full Text | Google Scholar

Jia, B., Raphenya, A. R., Alcock, B., Waglechner, N., Guo, P., Tsang, K. K., et al. (2017). CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res. 45, D566–D573. doi: 10.1093/nar/gkw1004,

PubMed Abstract | Crossref Full Text | Google Scholar

Klockgether, J., Cramer, N., Wiehlmann, L., Davenport, C. F., and Tümmler, B. (2011). Pseudomonas aeruginosa genomic structure and diversity. Front. Microbiol. 2:150. doi: 10.3389/fmicb.2011.00150,

PubMed Abstract | Crossref Full Text | Google Scholar

Kocer, K., Boutin, S., Moll, M., and Nurjadi, D. (2024). Investigation of cefiderocol resistance prevalence and resistance mechanisms in carbapenem-resistant Pseudomonas aeruginosa, Germany 2019-21. JAC-Antimicrobial Resistance 6:dlae183. doi: 10.1093/jacamr/dlae183,

PubMed Abstract | Crossref Full Text | Google Scholar

Kung, V. L., Ozer, E. A., and Hauser, A. R. (2010). The accessory genome of Pseudomonas aeruginosa. Microbiol. Mol. Biol. Rev. 74, 621–641. doi: 10.1128/MMBR.00027-10,

PubMed Abstract | Crossref Full Text | Google Scholar

Laborda, P., Martínez, J. L., and Hernando-Amado, S. (2022). Convergent phenotypic evolution towards fosfomycin collateral sensitivity of Pseudomonas aeruginosa antibiotic-resistant mutants. Microb. Biotechnol. 15, 613–629. doi: 10.1111/1751-7915.13817,

PubMed Abstract | Crossref Full Text | Google Scholar

Lewis, I. I., and James, S. (2023). Clinical and Laboratory Standards Institute. Performance standards for antimicrobial susceptibility testing, 33rd ed,Clinical and Laboratory Standards Institute. 2023, 9781684401703. https://cir.nii.ac.jp/crid/1970867909852046740

Google Scholar

Liao, C., Huang, X., Wang, Q., Yao, D., and Lu, W. (2022). Virulence factors of Pseudomonas aeruginosa and Antivirulence strategies to combat its drug resistance. Front. Cell. Infect. Microbiol. 12:926758. doi: 10.3389/fcimb.2022.926758,

PubMed Abstract | Crossref Full Text | Google Scholar

Linde, J., Homeier-Bachmann, T., Dangel, A., Riehm, J. M., Sundell, D., Öhrman, C., et al. (2020). Genotyping of Francisella tularensis subsp. holarctica from hares in Germany. Microorganisms 8:932. doi: 10.3390/microorganisms8121932,

PubMed Abstract | Crossref Full Text | Google Scholar

Low, A. J., Koziol, A. G., Manninger, P. A., Blais, B., and Carrillo, C. D. (2019). ConFindr: rapid detection of intraspecies and cross-species contamination in bacterial whole-genome sequence data. PeerJ 7:e6995. doi: 10.7717/peerj.6995,

PubMed Abstract | Crossref Full Text | Google Scholar

Martak, D., Gbaguidi-Haore, H., Meunier, A., Valot, B., Conzelmann, N., Eib, M., et al. (2022). High prevalence of Pseudomonas aeruginosa carriage in residents of French and German long-term care facilities. Clin. Microbiol. Infect. 28, 1353–1358. doi: 10.1016/j.cmi.2022.05.004,

PubMed Abstract | Crossref Full Text | Google Scholar

Morris, D. O., Davis, M. F., Palmeiro, B. S., O'Shea, K., and Rankin, S. C. (2017). Molecular and epidemiological characterization of canine Pseudomonas otitis using a prospective case-control study design. Vet. Dermatol. 28, 118–e25. doi: 10.1111/vde.12347,

PubMed Abstract | Crossref Full Text | Google Scholar

Mosquera-Rendón, J., Rada-Bravo, A. M., Cárdenas-Brito, S., Corredor, M., Restrepo-Pineda, E., and Benítez-Páez, A. (2016). Pangenome-wide and molecular evolution analyses of the Pseudomonas aeruginosa species. BMC Genomics 17:45. doi: 10.1186/s12864-016-2364-4,

PubMed Abstract | Crossref Full Text | Google Scholar

Murugaiyan, J., Palpandi, K., Das, V., and Kumar, A. (2024). Rapid species differentiation and typing of Acinetobacter baumannii. Ger. J. Vet. Res. 4, 37–48. doi: 10.51585/gjvr.2024.3.0096

Crossref Full Text | Google Scholar

Naghavi, M., Vollset, S. E., Ikuta, K. S., Swetschinski, L. R., Gray, A. P., Wool, E. E., et al. (2024). Global burden of bacterial antimicrobial resistance 1990–2021: a systematic analysis with forecasts to 2050. Lancet 404, 1199–1226. doi: 10.1016/S0140-6736(24)01867-1,

PubMed Abstract | Crossref Full Text | Google Scholar

Néron, B., Littner, E., Haudiquet, M., Perrin, A., Cury, J., and Rocha, E. P. C. (2022). IntegronFinder 2.0: identification and analysis of Integrons across Bacteria, with a focus on antibiotic resistance in Klebsiella. Microorganisms 10:700. doi: 10.3390/microorganisms10040700,

PubMed Abstract | Crossref Full Text | Google Scholar

Newstead, L., Smith-Zaitlik, T., Kelly, C., Roberts, E., Street, S., and Paterson, G. K. (2025). Genomic characterization of Pseudomonas aeruginosa from canine otitis highlights the need for a one health approach to this opportunistic pathogen. Microb Genom 11:1407. doi: 10.1099/mgen.0.001407,

PubMed Abstract | Crossref Full Text | Google Scholar

Nielsen, S. S., Alvarez, J., Bicout, D. J., Calistri, P., Canali, E., Drewe, J. A., et al. (2022a). Assessment of listing and categorisation of animal diseases within the framework of the animal health law (regulation (EU) no 2016/429): infection with equine Herpesvirus-1. EFSA J. 20:e07036. doi: 10.2903/j.efsa.2022.7036

Crossref Full Text | Google Scholar

Nielsen, S. S., Bicout, D. J., Calistri, P., Canali, E., Drewe, J. A., Garin-Bastuji, B., et al. (2022b). Assessment of listing and categorisation of animal diseases within the framework of the animal health law (regulation (EU) no 2016/429): antimicrobial-resistant Pseudomonas aeruginosa in dogs and cats. EFSA J. 20:e07310. doi: 10.2903/j.efsa.2022.7310

Crossref Full Text | Google Scholar

Nolff, M. C., Reese, S., Fehr, M., Dening, R., and Meyer-Lindenberg, A. (2016). Assessment of wound bio-burden and prevalence of multi-drug resistant bacteria during open wound management. J. Small Anim. Pract. 57, 255–259. doi: 10.1111/jsap.12476

Crossref Full Text | Google Scholar

Odumosu, B. T., Ajetunmobi, O., Dada-Adegbola, H., and Odutayo, I. (2016). Antibiotic susceptibility pattern and analysis of plasmid profiles of Pseudomonas aeruginosa from human, animal and plant sources. Springerplus 5:1381. doi: 10.1186/s40064-016-3073-9,

PubMed Abstract | Crossref Full Text | Google Scholar

Oliver, A., Mulet, X., López-Causapé, C., and Juan, C. (2015). The increasing threat of Pseudomonas aeruginosa high-risk clones. Drug Resist. Updat. 21-22, 41–59. doi: 10.1016/j.drup.2015.08.002,

PubMed Abstract | Crossref Full Text | Google Scholar

Pang, Z., Raudonis, R., Glick, B. R., Lin, T.-J., and Cheng, Z. (2019). Antibiotic resistance in Pseudomonas aeruginosa: mechanisms and alternative therapeutic strategies. Biotechnol. Adv. 37, 177–192. doi: 10.1016/j.biotechadv.2018.11.013

Crossref Full Text | Google Scholar

Panzig, B., Schröde r, G., Pitten, F.-A., and Gründling, M. (1999). A large outbreak of multiresistant Pseudomonas aeruginosa strains in North-Eastern Germany. J. Antimicrob. Chemother. 43, 415–418. doi: 10.1093/jac/43.3.415

Crossref Full Text | Google Scholar

Pitten, F. A., Panzig, B., Schröder, G., Tietze, K., and Kramer, A. (2001). Transmission of a multiresistant Pseudomonas aeruginosa strain at a German university hospital. J. Hosp. Infect. 47, 125–130. doi: 10.1053/jhin.2000.0880,

PubMed Abstract | Crossref Full Text | Google Scholar

Qin, S., Xiao, W., Zhou, C., Pu, Q., Deng, X., Lan, L., et al. (2022). Pseudomonas aeruginosa: pathogenesis, virulence factors, antibiotic resistance, interaction with host, technology advances and emerging therapeutics. Signal Transduct. Target. Ther. 7:199. doi: 10.1038/s41392-022-01056-1,

PubMed Abstract | Crossref Full Text | Google Scholar

Rath, A., Kieninger, B., Fritsch, J., Caplunik-Pratsch, A., Blaas, S., Ochmann, M., et al. (2024). Whole-genome sequencing reveals two prolonged simultaneous outbreaks involving Pseudomonas aeruginosa high-risk strains ST111 and ST235 with resistance to quaternary ammonium compounds. J. Hosp. Infect. 145, 155–164. doi: 10.1016/j.jhin.2024.01.009,

PubMed Abstract | Crossref Full Text | Google Scholar

Reynolds, D., and Kollef, M. (2021). The epidemiology and pathogenesis and treatment of Pseudomonas aeruginosa infections: an update. Drugs 81, 2117–2131. doi: 10.1007/s40265-021-01635-6,

PubMed Abstract | Crossref Full Text | Google Scholar

Rizk, A. M., Elsayed, M. M., Abd El Tawab, A. A., Elhofy, F. I., Soliman, E. A., Kozytska, T., et al. (2024). Phenotypic and genotypic characterization of resistance and virulence in Pseudomonas aeruginosa isolated from poultry farms in Egypt using whole genome sequencing. Vet. Microbiol. 292:110063. doi: 10.1016/j.vetmic.2024.110063,

PubMed Abstract | Crossref Full Text | Google Scholar

Rosenboom, I., Oguz, S., Lüdemann, I. M., Ringshausen, F. C., Rademacher, J., Sedlacek, L., et al. (2023). Pseudomonas aeruginosa population genomics among adults with bronchiectasis across Germany. ERJ Open Res 9:2023. doi: 10.1183/23120541.00156-2023,

PubMed Abstract | Crossref Full Text | Google Scholar

Schärer, V., Meier, M.-T., Schuepbach, R. A., Zinkernagel, A. S., Boumasmoud, M., Chakrakodi, B., et al. (2023). An intensive care unit outbreak with multi-drug-resistant Pseudomonas aeruginosa – spotlight on sinks. J. Hosp. Infect. 139, 161–167. doi: 10.1016/j.jhin.2023.06.013,

PubMed Abstract | Crossref Full Text | Google Scholar

Schwengers, O., Barth, P., Falgenhauer, L., Hain, T., Chakraborty, T., and Goesmann, A. (2020). Platon: identification and characterization of bacterial plasmid contigs in short-read draft assemblies exploiting protein sequence-based replicon distribution scores. Microb Genom 6:398. doi: 10.1099/mgen.0.000398,

PubMed Abstract | Crossref Full Text | Google Scholar

Schwengers, O., Jelonek, L., Dieckmann, M. A., Beyvers, S., Blom, J., and Goesmann, A. (2021). Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification. Microb Genom 7:685. doi: 10.1099/mgen.0.000685,

PubMed Abstract | Crossref Full Text | Google Scholar

Secker, B., Shaw, S., Hobley, L., and Atterbury, R. J. (2025). Genomic and phenotypic characterisation of Pseudomonas aeruginosa isolates from canine otitis externa reveals high-risk sequence types identical to those found in human nosocomial infections. Front. Microbiol. 16:1526843. doi: 10.3389/fmicb.2025.1526843,

PubMed Abstract | Crossref Full Text | Google Scholar

Sharma, A. K., Dhasmana, N., Dubey, N., Kumar, N., Gangwal, A., Gupta, M., et al. (2017). Bacterial virulence factors: secreted for survival. Indian J. Microbiol. 57, 1–10. doi: 10.1007/s12088-016-0625-1,

PubMed Abstract | Crossref Full Text | Google Scholar

Sousa, T., Hébraud, M., Dapkevicius, M. L. N. E., Maltez, L., Pereira, J. E., Capita, R., et al. (2021). Genomic and metabolic characteristics of the pathogenicity in Pseudomonas aeruginosa. Int. J. Mol. Sci. 22:892. doi: 10.3390/ijms222312892,

PubMed Abstract | Crossref Full Text | Google Scholar

Stamatakis, A. (2014). RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313. doi: 10.1093/bioinformatics/btu033,

PubMed Abstract | Crossref Full Text | Google Scholar

Thi, M. T. T., Wibowo, D., and Rehm, B. H. A. (2020). Pseudomonas aeruginosa Biofilms. Int. J. Mol. Sci. 21:671. doi: 10.3390/ijms21228671,

PubMed Abstract | Crossref Full Text | Google Scholar

Tonkin-Hill, G., MacAlasdair, N., Ruis, C., Weimann, A., Horesh, G., Lees, J. A., et al. (2020). Producing polished prokaryotic pangenomes with the Panaroo pipeline. Genome Biol. 21:180. doi: 10.1186/s13059-020-02090-4,

PubMed Abstract | Crossref Full Text | Google Scholar

Tümmler, B. (2022). What makes Pseudomonas aeruginosa a pathogen? Adv. Exp. Med. Biol. 1386, 283–301. doi: 10.1007/978-3-031-08491-1_10

Crossref Full Text | Google Scholar

Wareth, G., Linde, J., Hammer, P., Pletz, M. W., Neubauer, H., and Sprague, L. D. (2022). WGS-based phenotyping and molecular characterization of the Resistome, Virulome, and plasmid replicons in Klebsiella pneumoniae isolates from powdered Milk produced in Germany. Microorganisms 10:564. doi: 10.3390/microorganisms10030564,

PubMed Abstract | Crossref Full Text | Google Scholar

Weber, C., Schultze, T., Göttig, S., Kessel, J., Schröder, A., Tietgen, M., et al. (2022). Antimicrobial activity of Ceftolozane-Tazobactam, ceftazidime-avibactam, and Cefiderocol against multidrug-resistant Pseudomonas aeruginosa recovered at a German university hospital. Microbiol Spectr 10:e0169722. doi: 10.1128/spectrum.01697-22,

PubMed Abstract | Crossref Full Text | Google Scholar

Wendel, A. F., Malecki, M., Mattner, F., Xanthopoulou, K., Wille, J., Seifert, H., et al. (2022). Genomic-based transmission analysis of carbapenem-resistant Pseudomonas aeruginosa at a tertiary care Centre in Cologne (Germany) from 2015 to 2020. JAC Antimicrob Resist 4:dlac057. doi: 10.1093/jacamr/dlac057,

PubMed Abstract | Crossref Full Text | Google Scholar

WHO (2024). WHO updates list of drug-resistant bacteria most threatening to human health. Geneva: WHO.

Google Scholar

Willmann, M., Bezdan, D., Zapata, L., Susak, H., Vogel, W., Schröppel, K., et al. (2015). Analysis of a long-term outbreak of XDR Pseudomonas aeruginosa: a molecular epidemiological study. J. Antimicrob. Chemother. 70, 1322–1330. doi: 10.1093/jac/dku546,

PubMed Abstract | Crossref Full Text | Google Scholar

Wood, D. E., Lu, J., and Langmead, B. (2019). Improved metagenomic analysis with kraken 2. Genome Biol. 20:257. doi: 10.1186/s13059-019-1891-0,

PubMed Abstract | Crossref Full Text | Google Scholar

Zankari, E., Hasman, H., Cosentino, S., Vestergaard, M., Rasmussen, S., Lund, O., et al. (2012). Identification of acquired antimicrobial resistance genes. J. Antimicrob. Chemother. 67, 2640–2644. doi: 10.1093/jac/dks261,

PubMed Abstract | Crossref Full Text | Google Scholar

Zarei-Yazdeli, M., Eslami, G., Zandi, H., Kiani, M., Vogel, W., Barzegar, K., et al. (2018). Prevalence of class 1, 2 and 3 integrons among multidrug-resistant Pseudomonas aeruginosa in Yazd, Iran. Iran J Microbiol. 10, 300–306.

Google Scholar