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. 2026 Jan 21;16:5968. doi: 10.1038/s41598-026-35520-7

Comparative genomics of colistin-nonsusceptible multidrug-resistant Pseudomonas aeruginosa reveals emerging lineages in Thailand

Natnicha Wankaew 1,2, Tantip Arigul 1,2, Worarat Kruasuwan 1,2, Piroon Jenjaroenpun 1,2,3, Thidathip Wongsurawat 1,2,3, Teerasak E-kobon 4, Aekkawat Unahalekhaka 5, Phatcharin Laosena 6, Jiraphan Premsuriya 7,8,✉
PMCID: PMC12901977  PMID: 41559330

Abstract

Pseudomonas aeruginosa is a leading cause of multidrug-resistant (MDR) infections, for which the polymyxin antibiotic colistin has been a treatment of last resort. The emergence of colistin-nonsusceptible strains thus poses a serious therapeutic challenge. In this study, we performed whole-genome sequencing and comparative genomic analysis of 29 colistin-nonsusceptible MDR P. aeruginosa clinical isolates collected from hospitals across Thailand between 2021 and 2022. The genomic analysis identified nine distinct sequence types (STs), including a previously uncharacterized ST (ST5340) closely related to the high-risk international clone ST357. This novel ST was the most prevalent (24%) and all of its isolates exhibited resistance to all tested antibiotics. Comprehensive resistome profiling revealed widespread possession of resistance determinants, including multiple beta-lactamases (e.g., blaNDM-1, blaOXA-10-like, blaOXA-10, blaOXA-846 and blaVEB), aminoglycoside-modifying enzymes, and colistin resistance-associated mutations. SNP-based phylogenetic analysis of 108 Thai P. aeruginosa genomes, including 79 in a public database and those of the 29 newly sequenced isolates, revealed dominant clusters formed by ST5340, ST357, ST654, and ST235. The widespread distribution of ST5340 across multiple regions of Thailand suggests its emergence as a potential epidemic high-risk clone. These findings highlight the genetic diversity and geographical spread of colistin-nonsusceptible P. aeruginosa in Thailand. The emergence of ST5340 as a dominant, extensively drug-resistant lineage underscores the urgent need for continued genomic surveillance to inform infection control and antimicrobial stewardship efforts.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-35520-7.

Keywords: Pseudomonas aeruginosa, Multidrug resistance, Colistin-nonsusceptible, High-risk clone, Genomic surveillance

Subject terms: Bacteriology, Microbial genetics, Pathogens

Introduction

Pseudomonas aeruginosa is a Gram-negative bacterium with a relatively large genome (5.5–7 million base pairs) compared with other bacteria. This expansive genome encodes a diverse array of metabolic pathways, enabling this species to grow in various environments and cause serious infections in humans1. P. aeruginosa is classified as a critical opportunistic pathogen that frequently causes nosocomial infections, particularly ventilator-acquired pneumonia, bloodstream infections, and urinary tract infections2. These infections can be particularly severe in individuals with compromised immune systems, respiratory conditions, or severe conditions such as chronic obstructive pulmonary disease, cystic fibrosis, and burn wounds2,3. Antibiotics used for treating P. aeruginosa infections comprise multiple major classes, including beta-lactams (penicillins, cephalosporins, monobactams, and carbapenems), aminoglycosides, fluoroquinolones and colistin4.

Over the last two decades, P. aeruginosa has become a major public health concern due to its increasing resistance to multiple antibiotics. Strains with broad-spectrum resistance to beta-lactams, aminoglycosides, fluoroquinolones, and carbapenems have been observed globally. Additionally, resistance to colistin, an antibiotic of last resort, has been reported in many countries5,6. It is particularly challenging to treat multidrug-resistant (MDR) P. aeruginosa, defined as non-susceptibility to at least one agent in three or more antimicrobial categories, and extensively drug-resistant (XDR) P. aeruginosa, defined as non-susceptibility to at least one agent in all but two or fewer categories7. Infections caused by MDR or XDR P. aeruginosa are associated with higher rates of morbidity, mortality, surgical intervention, prolonged hospital stays, and increased treatment costs8,9.

P. aeruginosa possesses a highly dynamic and open pangenome, characterized by extensive genomic plasticity and frequent acquisition of mobile genetic elements10. This pangenomic diversity underlies the species’ wide repertoire of antimicrobial resistance mechanisms, including active efflux pumps, biofilm formation, target site mutations and antibiotic-degrading enzymes3,11. Notably, several resistant P. aeruginosa strains produce diverse beta-lactamases such as metallo-beta-lactamases (e.g., NDM) and OXA-type enzymes which mediate resistance to carbapenems and other beta-lactams by hydrolyzing the beta-lactam ring and thereby inactivating the antibiotic. The dissemination of these beta-lactamases is frequently mediated by transposable elements and integrons12. Recent analyses of carbapenem-resistant P. aeruginosa emphasize how genomic plasticity facilitates resistome expansion and the emergence of high-risk clones in hospital settings10.

In Thailand, P. aeruginosa is one of the most common causes of hospital-acquired infections. A 2022 nationwide survey of 68 network hospitals by the National Antimicrobial Resistance Surveillance Center, Thailand (NARST), identified 24,753 cases of P. aeruginosa infection, accounting for 8.8% of all reported bacterial infections. Over 20% of P. aeruginosa strains isolated in the NARST hospital network demonstrated resistance to at least one antipseudomonal antibiotic, with the emergence of colistin-resistant strains first identified in 201613. Independent studies conducted at various Thai hospitals have revealed that over 20% of P. aeruginosa isolates from patients demonstrated MDR or XDR phenotypes14–16. Despite the high prevalence of P. aeruginosa infections in Thailand, limited genome sequencing data on P. aeruginosa clinically isolated in the country have been reported. Moreover, most genomic studies have focused on isolates from single sources, impeding our ability to obtain an accurate overview of the genetic diversity of this bacterium across Thailand17–20.

This study was implemented with the aim of comprehensively characterizing the genomic diversity and molecular features of antimicrobial resistance (AMR) genes in 29 colistin-nonsusceptible MDR P. aeruginosa strains isolated from hospitals across various regions of Thailand in 2021–2022. To place these newly sequenced isolates in a broader national context, we further incorporated 79 publicly available Thai P. aeruginosa genomes, yielding a total dataset of 108 genomes for phylogenetic analyses. This extended analysis enabled the identification of dominant lineages and provided insights into the population structure, regional dissemination, and evolutionary dynamics of P. aeruginosa in Thailand.

Results

Study population and antimicrobial susceptibility profiles

A total of 29 clinical isolates of colistin-nonsusceptible MDR P. aeruginosa, collected across Thailand during 2021–2022, were obtained from the Thai National Institute of Health (Thai NIH). These isolates originated from multiple hospitals across the country that participate in the NARST network, primarily those in the Central region of Thailand (19 out of 29 isolates) (Table 1, Fig. S1). The isolates were derived from a variety of clinical specimens, including urine, blood, sputum, pus, and bodily fluids from Jackson–Pratt (JP) drains, with urine isolates being predominant (20 out of 29 isolates). Antimicrobial susceptibility profiles for 10 antibiotics, representing seven different classes, were evaluated in accordance with Clinical & Laboratory Standards Institute (CLSI) guidelines and are summarized in Table 1. According to CLSI breakpoints, a colistin minimum inhibitory concentration (MIC) of ≤ 2 μg/mL is interpreted as intermediate, and an MIC of ≥ 4 μg/mL is interpreted as resistant, with no susceptible category21. This study focused on colistin-nonsusceptible isolates, which by CLSI definition includes all isolates with a measurable MIC. For the purpose of this study, isolates with a colistin MIC of 2 μg/mL were specifically classified as intermediate, and those with an MIC of ≥ 4 μg/mL were classified as resistant. Isolates with a colistin MIC < 2 μg/mL were not included. The data revealed a high level of resistance across most tested antibiotics. Alongside their status of not being susceptible to colistin, as required for inclusion in this study, all isolates were also resistant to both meropenem and imipenem. Notably, 12 of these isolates exhibited resistance to all tested antibiotics, and these XDR isolates were distributed across all four geographical regions (Central, South, North, and North-East) of Thailand.

Table 1.

Isolation source and antibiotic susceptibility testing of 29 P. aeruginosa isolates.

Isolate Isolation source Province Region Antibiotic susceptibility test
ATM TZP FEP CAZ MEM IPM CIP CN AK CT
PATH-1 Urine Bangkok Central R R R R R R R R R I
PATH-2 Urine Bangkok Central R R R R R R R R R R
PATH-3 Fluids from JP drains Nakhon Pathom Central R I R R R R R R S R
PATH-4 Urine Samut Songkhram Central R R R R R R R R R R
PATH-5 Sputum Nonthaburi Central R I R R R R R S S R
PATH-6 Urine Chonburi Central R I R R R R R R R I
PATH-7 Urine Nakhon Sawan Central S R R R R R S R R I
PATH-9 Blood Nakhon Sawan Central I R R R R R R R R I
PATH-12 Pus Trang South R I R R R R R R S I
PATH-13 Urine Nakhon Sawan Central I R R R R R R R R I
PATH-20 Urine Narathiwat South R R R R R R R R R I
PATH-23 Urine Nakhon Pathom Central R R R R R R R R R R
PATH-24 Urine Phayao North R R R R R R R R R I
PATH-25 Urine Phayao North R R R R R R R R R R
PATH-29 Blood Nakhon Sawan Central S R R R R R R R R I
PATH-31 Urine Nakhon Sawan Central R R R R R R R R R R
PATH-33 Urine Trang South R I R R R R R S S I
PATH-34 Urine Trang South R R R R R R R R R R
PATH-35 Urine Trang South I I R R R R R R R I
PATH-36 Sputum Trang South I I R R R R R S S I
PATH-37 Sputum Chonburi Central R R R R R R R R R R
PATH-41 Urine Nakhon Sawan Central R R R R R R R S R I
PATH-42 Urine Nakhon Sawan Central R R R R R R R R R R
PATH-43 Sputum Nakhon Sawan Central R R R R R R R R R R
PATH-44 Urine Nakhon Sawan Central R I R R R R R R I R
PATH-45 Sputum Nakhon Sawan Central I S S I R R R S S R
PATH-46 Urine Phayao North R R R R R R R R R R
PATH-47 Urine Nakhon Ratchasima North-East R R R R R R R R R R
PATH-48 Urine Bangkok Central R R R R R R R R R R
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Antibiotic classes and abbreviations: Beta-lactam: ATM (aztreonam), TZP (piperacillin–tazobactam), FEP (cefepime), CAZ (ceftazidime), MEM (meropenem), IPM (imipenem). Fluoroquinolones: CIP (ciprofloxacin). Aminoglycosides: CN (gentamicin), AK (amikacin). Polymyxins: CT (colistin). Susceptibility categories: S, susceptible; I, intermediate; R, resistant.

General features of genomes

The whole-genome sequencing and assembly of 29 MDR P. aeruginosa isolates revealed high-quality genomes, with 15 samples assembled into a single contig. Genome sizes ranged from ~ 6.6 to 7.1 Mb, with consistent GC content (65.8%–66.1%) and high N50 values. Taxonomic analysis confirmed all isolates as P. aeruginosa, with average nucleotide identity values between 98.2% and 99.3% (Supplementary Table S1). Multilocus sequence typing (MLST) revealed nine distinct sequence types (STs). Notably, a previously undescribed ST was discovered and determined to be the most prevalent. Comparative analysis indicated that this novel ST shares six alleles with ST357, except for a single variant at the acsA locus (Supplementary Table S2). The allelic profile of the novel ST (acsA 360, aroE 4, guaA 5, mutL 3, nuoD 1, ppsA 6, trpE 11) was submitted to PubMLST database (https://pubmlst.org/) using isolate PATH-48 as the representative genome. The profile was subsequently assigned as ST5340.

Phylogenetic analysis

Single-nucleotide polymorphism (SNP)-based phylogenetic analysis of the 29 P. aeruginosa isolates, along with three reference genomes (P. aeruginosa PAO1, UCBPP-PA14, and Pseudomonas paraeruginosa PA7), revealed that 17 isolates clustered with UCBPP-PA14 and 11 isolates clustered with PAO1. The PAO1 and UCBPP-PA14 clusters were defined as monophyletic clades containing the corresponding reference genome with bootstrap support ≥ 95%. Interestingly, isolate PATH-2 clustered with PA7, which represents a phylogenetic branch distinct from the PAO1 and PA14 clades (Fig. S2. The SNP-based phylogenetic tree of the 29 isolates, along with PAO1 and UCBPP-PA14, annotated with ST, province, region, and antibiotic resistance pattern, is presented in Fig. 1. In this study, a 300-SNP threshold was applied to define major phylogenetic clusters at the ST level. ST357 and the newly identified ST5340 were clustered within the same phylogenetic clade, with the isolates of ST5340 forming an internal node that splits from ST357 (Fig. S3). Pairwise comparisons between ST5340 and ST357 yielded distances ranging from 36 to 271 SNPs (median = 54, interquartile range = 45–63). These values indicate a close genetic relationship but also distinct clustering, consistent with the designation of ST5340 as a single-locus variant (SLV) of ST357. This phylogenetic group was the most frequently identified lineage and showed resistance to most of the tested antibiotics. ST357 was found in three provinces in the Central and Northern regions, while ST5340 was found in six provinces across the Central, South, and North-East regions. ST357 isolates displayed variability in antibiotic resistance patterns, whereas all ST5340 isolates showed resistance to all tested antibiotics (Fig. 1).

Fig. 1.

Fig. 1

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SNP-based phylogenetic tree of the 29 P. aeruginosa isolates with PAO1 and UCBPP-PA14 reference genomes, annotated with sequence type (ST), province of origin, region of origin, and antibiotic resistance pattern. Antibiotic abbreviations: ATM, aztreonam; TZP, piperacillin–tazobactam; FEP, cefepime; CAZ, ceftazidime; MEM, meropenem; IPM, imipenem; CIP, ciprofloxacin; CN, gentamicin; AK, amikacin; CT, colistin.

Another relatively prevalent lineage identified was ST654, which comprised seven isolates. Six of these originated from Nakhon Sawan (Central region) and one from Narathiwat (Southern region). These isolates consistently exhibited resistance to multiple antibiotics, with the exception of PATH-45, which remained susceptible to four of the tested antibiotics, particularly the aminoglycosides. Meanwhile, ST235 and ST3895 were each represented by three isolates and formed distinct phylogenetic clades (Fig. 1). The ST235 isolates were collected from Bangkok, Nakhon Sawan, and Chonburi within the Central region, while all ST3895 isolates originated from Trang Province in the Southern region. Other STs were less frequently found, each represented by a single isolate. These included ST308 from Trang (Southern region), ST3390 from Bangkok (Central region), and ST233 and ST964 from Nakhon Pathom (Central region). Phylogenetic analysis revealed that ST964 clustered with the ST654 lineage, while ST3390, ST308, and ST233 formed separate clades (Fig. 1).

To further contextualize the findings, 79 additional clinical P. aeruginosa genomes from Thailand, retrieved from the NCBI Pathogen Detection database, were analyzed alongside the 29 genomes from this study. The SNP-based phylogenetic tree of the combined dataset (n = 108) is presented in Fig. 2. To aid visualization, these genomes retrieved from the database were relabeled as TH1 to TH79. The corresponding accession numbers for these isolates are provided in Supplementary Table S3. Within this combined dataset, a total of 49 STs were classified. ST357, with its related ST5340, was the most frequently identified lineage (16 isolates). Most isolates within this lineage originated from the present study, with three ST357 isolates from the database: one from Bangkok (TH20) and two from Chonburi (TH36 and TH38). Eight ST654 isolates and two ST964 isolates formed a phylogenetic cluster. The database isolates within this cluster included two ST654 isolates from Chonburi (TH32, TH46) and one ST964 isolate from an unspecified province (TH78). ST235 formed a separate cluster, comprising three isolates from this study and five isolates from the database: three from Bangkok (TH6, TH10, TH11), one from Chonburi (TH41), and one from an unspecified province (TH76). A cluster corresponding to ST3895 included three isolates from this study and four database isolates (TH8 and TH49 from Bangkok; TH35 and TH43 from Chonburi). A cluster associated with ST3390 was also found, including one isolate from this study and five database isolates originating from Bangkok (TH5, TH28) and Chonburi (TH29, TH45, TH47). ST233 was represented by only two isolates: PATH-23 from this study and the database isolate TH21 from Bangkok. ST308 was not represented by any database isolates but was represented only by the isolate from the current study.

Fig. 2.

Fig. 2

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SNP-based phylogenetic tree of P. aeruginosa isolates from Thailand. The tree was constructed using 29 isolates from this study along with 79 clinical P. aeruginosa genomes from Thailand retrieved from the NCBI Pathogen Detection database. Reference strains PAO1 and UCBPP-PA14 were included for comparative purposes. Each genome is annotated with its sequence type (ST), isolation year, province of origin, and source information.

In addition, ST244 formed a cluster consisting solely of seven database isolates. These isolates were predominantly from Chonburi, along with one from Bangkok and one from an unspecified province. The remaining database isolates were classified into diverse, low-frequency STs, often forming singleton branches or small clusters distinct from the major lineages (Fig. 2).

Antimicrobial resistance genes

Antimicrobial resistance genes of the 29 isolates sequenced in this study were identified using the CARD, ResFinder, and AMRFinderPlus databases. Owing to the high sequence similarity among certain resistance genes, particularly among beta-lactamase and aminoglycoside resistance gene variants that often differ by only a single amino acid, gene variants were further validated using BLAST analysis. In total, 133 AMR genes were detected, with several genes present in multiple copies (Fig. 3). Most identified genes were encoded on the chromosomes, with the exceptions of blaVEB-14, tet(A), sul1, and qacEdelta1 in isolate PATH-34, which were predicted to be plasmid encoded. Efflux-mediated resistance genes were highly prevalent and conserved across the isolates. All isolates harbored genes encoding resistance-nodulation-cell division (RND) efflux pumps, including mexA-W, muxA-C, opmB-H, oprJ-N, and triA-C (Fig. 3).

Fig. 3.

Fig. 3

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AMR genes detected in 29 P. aeruginosa genomes. Rows represent individual AMR genes, grouped by resistance mechanisms. Columns represent isolate numbers, clustered based on similarity in AMR gene profiles as indicated by the accompanying dendrogram. Sequence types (STs) are displayed alongside the dendrogram.

Genes encoding antibiotic-modifying enzymes were also consistently identified. Several metallo-beta-lactamase genes were detected, with blaPAO present in all isolates. The blaNDM-1 gene was detected in almost all isolates, with multiple gene copies observed in isolates of ST3390 and ST654. A variety of OXA variants were also identified: blaOXA-10-like, blaOXA-10, blaOXA-50 and blaOXA-846 were predominantly found in isolates belonging to ST5340 and ST357. Certain OXA variants exhibited strain specificity, including blaOXA-396, blaOXA-488, blaOXA-486, blaOXA-1127 and blaOXA-1135. Additionally, IMP and VIM variants, including blaIMP-1 and blaVIM-2, were detected in 7 of the 29 isolates (Fig. 3).

Genes encoding extended-spectrum beta-lactamases (ESBLs) and AmpC beta-lactamases were also identified across multiple isolates. Specifically, ST5340 and ST357 isolates harbored both blaVEB-14 and blaPDC-11. Additional variants, including blaVEB-16, blaPDC-3, blaPDC-5′ blaPDC-19a and blaPDC-35 were detected in specific lineages. Furthermore, genes encoding aminoglycoside-modifying enzymes (AMEs), including aac(3), aac(6′), aadA, aph, and ant variants, were identified, with aph(3′ )-IIb detected in all isolates. Notably, aadA1, ant(3″)-IIa, and ant(4′)-IIb were present in all ST5340 isolates and most ST357 isolates. Fluoroquinolone resistance-conferring mutations in gyrA were found in all isolates except PATH-7. Additionally, the rmtB4 gene, which confers aminoglycoside resistance through methylation of the 16S rRNA, was exclusively detected in ST654 isolates (Fig. 3).

Mutational analysis of colistin-resistance loci revealed frequent substitutions in ParS, LpxD, LpxH, and LpxO1, with distinct lineage associations. The ParS A13T, LpxD D30N, LpxH I228V, and LpxO1 H187Q were consistently present in ST357 and ST5340 isolates (Table 2). Fisher’s exact tests confirmed that these mutations were significantly enriched among resistant isolates, with odds ratios (OR) and adjusted p-values (q-values) as follows: ParS A13T (OR = 26.4, q = 0.0091), LpxD D30N (OR = 12.1, q = 0.0315), LpxH I228V (OR = 14.4, q = 0.0114), and LpxO1 H187Q (OR = 12.1, q = 0.0158). These findings suggest that colistin resistance in the emerging ST5340 lineage, as well as in ST357, is strongly associated with mutations in lipid A biosynthesis and two-component regulatory pathways. The main findings related to the predominant ST357 and ST5340 lineages are summarized in Fig. 4.

Table 2.

Amino acid substitutions at colistin resistance-associated loci among colistin-nonsusceptible P. aeruginosa isolates.

Isolate ID Sequence type Amino acid at indicated residues Colistin susceptibility*
ParS residue 13 LpxD residue 30 LpxH residue 228 LpxO1 residue 187
PATH-12 3895 A D I H I
PATH-33 3895 A D I H I
PATH-35 3895 A D I H I
PATH-3 964 A D I H R
PATH-9 654 A D I H I
PATH-13 654 A D I H I
PATH-20 654 A D I H I
PATH-29 654 A D I H R
PATH-42 654 A D I H R
PATH-45 654 A D I H I
PATH-1 235 A D I H I
PATH-6 235 A D I H I
PATH-41 235 A D I H I
PATH-36 308 A D V H I
PATH-2 3390 A D V H R
PATH-23 233 A D V H R
PATH-7 357 A N V Q I
PATH-24 357 T N V Q I
PATH-5 357 T N V Q R
PATH-25 357 T N V Q R
PATH-44 357 T N V Q R
PATH-46 357 T N V Q R
PATH-4 5340 T N V Q R
PATH-31 5340 T N V Q R
PATH-34 5340 T N V Q R
PATH-37 5340 T N V Q R
PATH-43 5340 T N V Q R
PATH-47 5340 T N V Q R
PATH-48 5340 T N V Q R
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Reference residues (PAO1) are ParS A13, LpxD D30, LpxH I228, and LpxO1 H187; deviations are shown in bold. *R = resistant (MIC ≥ 4 μg/mL), I = intermediate (MIC = 2 μg/mL).

Fig. 4.

Fig. 4

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Summary of key genomic findings for the predominant ST357 and ST5340 lineages, including sequence type distribution, antimicrobial resistance phenotype, antimicrobial resistance genes, and mutations significantly associated with colistin resistance. Statistical associations were evaluated using Fisher’s exact test with FDR correction; q < 0.05 was considered significant.

Discussion

This study provides a comprehensive genomic analysis of 29 colistin-nonsusceptible MDR P. aeruginosa strains collected across Thailand between 2021 and 2022. The analysis revealed phylogenetic relationships among these strains and their employment of a broad spectrum of antimicrobial resistance mechanisms. The identification of nine distinct STs, including a previously uncharacterized one (ST5340 closely related to ST357), demonstrated the genomic diversity and evolutionary dynamics of MDR P. aeruginosa in clinical settings in Thailand. ST357 and its related ST5340 represented the most prevalent lineage, identified in multiple provinces across all regions of Thailand. ST357 is recognized as a high-risk clone that has been identified internationally, and it is frequently associated with MDR or XDR phenotypes and hospital outbreaks in multiple countries22–24. Colistin-resistant ST357 isolates have also been reported in previous studies25. In addition to the isolates newly analyzed in this study, three previously reported Thai isolates from the NCBI Pathogen Detection database were identified as ST357: one from Bangkok (collected in 2013) and two from Chonburi (collected in 2017)17,18. This highlights that ST357 has long been circulating within Thai healthcare settings. Notably, these isolates from earlier studies were reported as MDR but remained susceptible to colistin17,18. Thus, the emergence of colistin-nonsusceptible ST357 isolates in the present study may reflect the recent emergence of this critical resistance characteristic. Furthermore, the ST357 isolates used in this study demonstrated a diversity of antibiotic resistance phenotypes, which may indicate their adaptation over time. The novel ST5340 was closely genetically related to ST357, differing only at the acsA locus. Phylogenetic analysis based on core-genome SNPs revealed that isolates belonging to ST5340 formed a phylogenetic subclade, separate from ST357. This indicates that this novel ST may have recently emerged from a common ancestor with ST357 and since undergone an independent evolutionary trajectory. Unlike ST357, all ST5340 isolates exhibited resistance to all tested antibiotics. This pattern suggests that ST5340’s recent evolutionary divergence from ST357 was potentially driven by localized selective pressures26. A key finding of this work is the widespread emergence of the ST5340 lineage across multiple parts of Thailand, indicating that it is a high-risk clone with the potential to reach epidemic proportions and thus requires close monitoring.

The relative prevalence of members of the ST654 cluster and its closely related ST964 highlights this lineage as another major contributor to the MDR burden. The findings from a previous study and the current investigation indicate that ST654 is predominantly distributed in Thailand’s Central region, particularly in Nakhon Sawan province18. ST654 is also a high-risk, carbapenem-resistant clone identified in multiple countries, where colistin-nonsusceptible isolates have been reported27–29. ST235 is another high-risk clone that was found in this study. The results, along with those of previous studies, demonstrate that this ST is primarily found in the Central region of Thailand17,18. Globally, this lineage is associated with epidemic spread and failure of clinical treatment, and is frequently linked to XDR phenotypes, including colistin resistance30–32. Meanwhile, two other globally distributed high-risk clones, ST233 and ST308, were identified at low frequencies in this study33–36. This low prevalence suggests either that to date these STs have been less able to spread in Thailand or possibly that they have only recently been introduced into the country. Given that these STs exhibit MDR phenotypes, particularly carbapenem resistance, they have the potential to disseminate more widely despite their low occurrence33–36. In addition to the global high-risk clones, clusters of rare STs, including ST3895 and ST3390, were identified here. In this study, ST3895 was found in the Southern region of Thailand, whereas previous studies identified it in the Central region17,18. This suggests possibly regional dissemination or persistence of this lineage. In contrast, ST3390 was detected exclusively in the central region, suggesting that it likely has limited potential to spread17. Notably, while previous studies identified the global high-risk clone ST244 in Thailand, it was not found in this study17,18. This absence is likely due to the study’s inclusion criteria, which focused exclusively on colistin-nonsusceptible isolates.

The extensive repertoire of AMR genes identified across the isolates emphasizes the diverse mechanisms that P. aeruginosa has developed to resist antibiotics. Efflux pump systems, particularly RND family members such as MexAB-OprM, MexCD-OprJ, MexEF-OprN, and MuxABC-OpmB, were prominent. These systems play critical roles in intrinsic resistance and are often upregulated in MDR strains37,38. Their widespread presence in all STs supports their role in baseline resistance levels and the ability of these strains to adapt under antibiotic selective pressure in healthcare settings39. Beta-lactamase production was also a major resistance mechanism observed in the isolates. Notably, the carbapenemase gene blaNDM-1 was detected in nearly all isolates, with certain strains of ST3390 and ST654 carrying multiple copies of it. These findings, consistent with global trends, highlight the widespread dissemination of NDM-producing P. aeruginosa40–42. Various OXA-type beta-lactamases including blaOXA-10-like, blaOXA-10, blaOXA-846, and lineage-specific variants such as blaOXA-396, blaOXA-486, and blaOXA-488 add an additional layer of complexity to the exhibited beta-lactam resistance. In addition, specific ESBL genes, such as blaVEB-14 and blaVEB-16, were exclusively found in ST5340, ST357, and ST235. This aligns with previous findings highlighting the roles of these genes in mediating resistance to cephalosporins and carbapenems42. Furthermore, it has been reported that variants of the P. aeruginosa AmpC beta-lactamase blaPDC (e.g., blaPDC-11, blaPDC-35) make additional contributions to broad-spectrum beta-lactam resistance43.

Aminoglycoside resistance genes, including members of the aac(3), aac(6’), aadA, aph, and ant families, were highly prevalent across the isolates, consistent with their well-established roles in mediating resistance among clinical P. aeruginosa strains 44. Notably, rmtB4, a 16S rRNA methyltransferase gene, was detected exclusively in ST654. This is significant because this gene confers high-level resistance to all clinically relevant aminoglycosides and has been increasingly reported in Asia45–47. Interestingly, the isolate PATH-45, a member of ST654, lacked rmtB4 and was susceptible to aminoglycosides, supporting the role of this gene in mediating aminoglycoside resistance. In contrast, PATH-20, the only ST654 isolate found in the Southern region, also lacked rmtB4 but was still highly resistant to aminoglycosides. This resistance was likely mediated by the exclusive presence of multiple aminoglycoside-modifying enzymes, including aac(6’), aadA, aph, and ant gene variants in this isolate.

For colistin resistance, no known plasmid-mediated colistin resistance genes, such as the mcr family, were detected in any isolate using AMR databases48. Therefore, we further examined chromosomal loci known to be involved in colistin resistance, including the lipid A biosynthesis genes (lpxA, lpxD, lpxH, lpxK, lpxL, lpxO1, lpxO2) and the two-component regulatory system genes (pmrAB, phoPQ, parRS). Mutations in these loci can modify lipid A structure or alter regulatory signaling pathways, leading to reduced colistin binding 25,49,50. The prevalence of ParS A13T, LpxD D30N, LpxH I228V, and LpxO1 H187Q mutations in ST5340 and ST357 underscores the presence of lineage-specific mutations that are linked to colistin resistance in Thailand. The significant associations revealed by Fisher’s exact tests support the biological relevance of these mutations and suggest that they are not randomly distributed across susceptible and resistant isolates. The particularly strong association of ParS A13T (OR = 26.4) highlights the role of regulatory signaling in driving resistance phenotypes 49. While mcr genes were absent, the chromosomal mutations identified here likely drive colistin resistance in these high-risk clones. The presence of these variants across two closely related sequence types (ST357 and ST5340) suggests that selective pressures may have facilitated their fixation and dissemination51. Future work including colistin-susceptible isolates as comparators and functional assays will be essential to validate the direct contributions of these mutations to the resistance phenotype.

The dominance of ST357 and the newly characterized ST5340, which exhibit extensive resistance to multiple antibiotics and are distributed across all regions of Thailand, underscores the urgency of implementing enhanced national surveillance and targeted antimicrobial stewardship strategies to limit the spread of these high-risk clones52,53. Although ST357 and ST5340 share a highly similar genomic background, they display distinct phenotypic resistance profiles, with ST5340 exhibiting resistance to all antibiotics tested. Notably, the AMR genes identified through the current databases were insufficient to fully explain the observed resistance phenotypes. In P. aeruginosa, resistance can also be mediated by mechanisms such as efflux pump overexpression or regulatory mutations, which are not captured by simple gene detection37,49. Thus, there is a need for further investigation into the underlying resistance mechanisms in order to explain the observed phenotypic differences. Future studies integrating multi-omics approaches are essential to elucidate the regulatory networks, expression dynamics, and adaptive mechanisms that contribute to the emergence and dissemination of resistance phenotypes in high-risk P. aeruginosa lineages.

This study benefits from high-quality hybrid genome sequencing, which enabled detailed characterization of resistome profiles and phylogenetic relationships. Integrating newly sequenced isolates with publicly available Thai P. aeruginosa genomes also provided valuable national context for understanding the distribution and spread of high-risk clones. However, the study is limited by the relatively small number of colistin-nonsusceptible isolates, the absence of colistin-susceptible comparator isolates, and the lack of functional validation experiments. Future studies with larger sample sizes and experimental validation will be important to strengthen and confirm these observations.

Conclusion

This study provides genomic insights into colistin-nonsusceptible MDR P. aeruginosa isolates in Thailand, highlighting the emergence of ST5340, a novel high-risk clone closely related to ST357. This lineage exhibited resistance to all tested antibiotics and was widely distributed across the country’s regions. The findings highlight the urgent need for continued genomic surveillance and targeted infection control strategies to mitigate the threat posed by emerging high-risk clones in healthcare settings.

Methods

P. aeruginosa strains

Colistin-nonsusceptible MDR P. aeruginosa isolates collected during 2021–2022 were provided by the Thai NIH. These clinical isolates originated from various hospitals participating in the NARST surveillance program. Bacterial stock cultures were stored in 40% glycerol at − 80 °C until use.

Antibiotic susceptibility testing

Antibiotic susceptibility testing was performed using the disc diffusion method for all antibiotics except colistin, for which the MIC was determined using the broth microdilution technique. Both tests were performed in accordance with the CLSI M07, 11th edition guideline54. The antibiotics tested covered four major antimicrobial classes including beta-lactams (piperacillin–tazobactam, ceftazidime, cefepime, aztreonam, imipenem, meropenem), aminoglycosides (gentamicin, amikacin), fluoroquinolones (ciprofloxacin), and polymyxins (colistin). P. aeruginosa ATCC®27,853 was used as the quality control strain54. Antibiotic susceptibility was interpreted according to CLSI M100, 33rd edition guidelines21. For colistin, an MIC of 2 μg/mL was classified as intermediate, and an MIC ≥ 4 μg/mL was classified as resistant.

DNA extraction

P. aeruginosa strains from stock cultures were streaked onto Luria–Bertani (LB) agar plates and incubated at 37 °C overnight. Total DNA was extracted using Genomic DNA Purification Kits (Zymo Research, USA) in accordance with the manufacturer’s protocol, with the following modifications: Colonies from the entire culture plate were used instead of cells from liquid culture, and the bead beating time was reduced to 3 min55. The quantity and purity of the extracted DNA were measured using a Nanodrop spectrophotometer (Thermo Fisher Scientific, USA).

Whole-genome sequencing and bioinformatic analysis

The purified DNA from 29 P. aeruginosa isolates was aliquoted for short-read and long-read sequencing. Short-read sequencing was performed by Novogene (Singapore) using the Illumina NovaSeq 6000 as the sequencing platform with a 150-bp read length for the paired-end reads. For long-read sequencing, the library was prepared from 50 ng of input DNA using the Rapid Sequencing gDNA—Barcoding kit (SQK-RBK114.96) (Oxford Nanopore Technologies, UK). The barcoded library was loaded into the R10.4.1 flow cell (Oxford Nanopore Technologies, UK) and sequenced using PromethION P2 Solo with the default settings. Guppy v6.0.1 was used for base calling and quality control56.

All downstream genomic analyses were performed in silico (dry lab) using established bioinformatic tools. For the short reads, quality checks were conducted with FastQC57. Adapters were removed and low-quality reads (quality score: Q ≤ 30) were filtered out using Fastp v0.23.258. The raw sequencing reads obtained from ONT were quality-checked, adapter-trimmed using Porechop v0.2.4, and filtered with Filtlong v0.2.1 to retain only reads longer than 1,000 bp and with Q above 10. The resulting reads were evaluated using NanoPlot v1.38.056. De Novo assembly of both short-read and ONT long-read sequences was carried out using Unicycler v0.4.859. The completeness and contamination of the assembled genome were then evaluated with CheckM v1.2.160, and plasmid typing was performed using MOB-suite v3.1.561. Genomic features were assessed using QUAST v5.0.262, and plasmid contigs were validated by searching the PLSDB63 and NCBI databases. All bacterial chromosomes were identified by aligning them with all marker genes in the GTDB-Tk database (R214). The average nucleotide identity and alignment fraction were computed using GTDB-Tk v2.1.164. MLST based on seven housekeeping genes (acsA, aroE, guaA, mutL, nuoD, ppsA and trpE) was conducted using MLST v2.23.0 (https://github.com/tseemann/mlst).

Phylogenetic analysis was performed on 29 assembled genomes and three reference genomes: PAO1 (NC_002516.2), UCBPP-PA14 (NC_008463.1) and PA7 (NC_009656.1), widely used in comparative studies65. Core SNPs, including substitutions, deletions, and insertions, were identified using Snippy v4.6.0 (https://github.com/tseemann/snippy). Maximum likelihood phylogenetic inference was conducted using RAxML-NG v1.0.166 with the GTR + G model and 1,000 bootstrap replicates. The final tree was visualized using iTOL v667. For comparative analysis, the 29 assembled genomes and the two reference genomes (PAO1 and UCBPP-PA14) were analyzed alongside 79 previously published Thai clinical P. aeruginosa genomes (retrieved from NCBI Pathogen Detection on March 26, 2025). RAxML-NG was again used for phylogenetic inference under the GTR + G model with 1,000 bootstraps using core SNPs from Snippy, and trees were annotated in iTOL v6. AMR genes were identified using CARD (v3.2.5, RGI v6.0.2)68, ResFinder (v4.0, database version 2024–12-15)69, and AMRFinderPlus (v4.0.19, database version 2024–04-29)70, with a minimum identity threshold of 90% (CARD and ResFinder) or 80% (AMRFinderPlus). Gene variants were further validated using BLAST analysis71. Mutational analysis of colistin-resistance loci was performed by extracting amino acid sequences of pmrAB, phoPQ, parRS and lipid A biosynthesis genes (lpxA-D, lpxH, lpxK, lpxL, lpxO1 and lpxO2) from assembled genomes. Amino acid residues were aligned against the PAO1 reference genome and compared among isolates. A schematic overview of the complete methodological workflow is provided in Fig. S4.

Statistical analysis

Associations between amino acid substitutions in colistin resistance–associated loci and phenotypic resistance categories were evaluated using Fisher’s exact test. Multiple testing correction was performed using the Benjamini–Hochberg false discovery rate (FDR) method72. Statistical analyses were conducted in R (v4.3.1), and mutations with an adjusted q-value < 0.05 were considered statistically significant.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (1.2MB, pdf)

Acknowledgements

This work was supported by Chulabhorn Royal Academy. The authors thank the staff and researchers at the Department of Medical Sciences, Thailand for their assistance and valuable contributions throughout the study. The computing facilities were supported by Mahidol University and the Office of the Ministry of Higher Education, Science, Research and Innovation under the Reinventing University project: The Center of Excellence in AI-Based Medical Diagnosis (AI-MD) sub-project, NSTDA Supercomputer Center (ThaiSC), and the National e-Science Infrastructure Consortium. The authors also thank Tom Buckle from Scribendi for editing a draft of this manuscript.

Author contributions

NW: Experimentation, Formal analysis, Writing—review & editing of the manuscript. TA: Experimentation, Formal analysis, Review & editing of the manuscript. WK: Experimentation, Formal analysis, Review & editing of the manuscript. PJ: Conceptualization, Methodology, Review & editing of the manuscript. TW: Conceptualization, Methodology, Review & editing of the manuscript. TE: Conceptualization, Methodology, Review & editing of the manuscript. AU: Conceptualization, Methodology, Review & editing of the manuscript. PL: Experimentation, Review & editing of the manuscript. JP: Fund acquisition and Project management, Conceptualization, Formal analysis, Writing—review & editing of the manuscript.

Funding

JP was funded by Chulabhorn Royal Academy: Project ID PSCM2566/001. PJ and TW were partially supported by National Research Council of Thailand (NRCT): Project ID N42A660897.

Data availability

Whole-genome sequence data for the isolates analyzed in this study are available under BioProject accession number PRJNA1257535 at the NCBI database.

Declarations

Competing interests

The authors declare no competing interests.

Ethics statement

This study was approved by the Chulabhorn Royal Academy’s Research Committee. The study used de-identified clinical isolates collected as part of routine national antimicrobial resistance surveillance, and no patient-identifying information was included. The requirement for informed consent was waived by the committee. All methods were performed in accordance with the relevant guidelines and regulations.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (1.2MB, pdf)

Data Availability Statement

Whole-genome sequence data for the isolates analyzed in this study are available under BioProject accession number PRJNA1257535 at the NCBI database.

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