Characterization of Pseudomonas kurunegalensis by Whole-Genome Sequencing from a Clinical Sample: New Challenges in Identification
Abstract
1. Introduction
2. Material and Methods
2.1. Sample Collection and Microbiological Processing
2.2. Genomic Sequencing Analysis
2.3. Resistome Characterization
3. Results
3.1. Clinical Case Description
3.2. Antimicrobial Susceptibility Testing and Carbapenemase Detection
3.3. WGS Analysis
3.4. Clinical Outcome
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Declaration of AI and AI-assisted Technologies in the Writing Process
References
- Silby, M.W.; Winstanley, C.; Godfrey, S.A.C.; Levy, S.B.; Jackson, R.W. Pseudomonas genomes: Diverse and adaptable. FEMS Microbiol. Rev. 2011, 35, 652–680. [Google Scholar] [CrossRef] [PubMed]
- Ambreetha, S.; Zincke, D.; Balachandar, D.; Mathee, K. Genomic and metabolic versatility of Pseudomonas aeruginosa contributes to its inter-kingdom transmission and survival. J. Med. Microbiol. 2024, 73, 001791. [Google Scholar] [CrossRef] [PubMed]
- Flores-Vega, V.R.; Partida-Sanchez, S.; Ares, M.A.; Ortiz-Navarrete, V.; Rosales-Reyes, R. High-risk Pseudomonas aeruginosa clones harboring β-lactamases: 2024 update. Heliyon 2025, 11, e41540. [Google Scholar] [CrossRef] [PubMed]
- Lalucat, J.; Gomila, M.; Mulet, M.; Zaruma, A.; García-Valdés, E. Past, present and future of the boundaries of the Pseudomonas genus: Proposal of Stutzerimonas gen. Nov. Syst. Appl. Microbiol. 2022, 45, 126289. [Google Scholar] [CrossRef]
- Mehmood, N.; Saeed, M.; Zafarullah, S.; Hyder, S.; Rizvi, Z.F.; Gondal, A.S.; Jamil, N.; Iqbal, R.; Ali, B.; Ercisli, S.; et al. Multifaceted Impacts of Plant-Beneficial Pseudomonas spp. in Managing Various Plant Diseases and Crop Yield Improvement. ACS Omega 2023, 8, 22296–22315. [Google Scholar] [CrossRef]
- Zeng, Y.; Feng, R.; Huang, C.; Liu, J.; Yang, F. Antibiotic Resistance Genes in Agricultural Soils: A Comprehensive Review of the Hidden Crisis and Exploring Control Strategies. Toxics 2025, 13, 239. [Google Scholar] [CrossRef]
- Kumar, S.; Stecher, G.; Suleski, M.; Hedges, S.B. TimeTree: A Resource for Timelines, Timetrees, and Divergence Times. Mol. Biol. Evol. 2017, 34, 1812–1819. [Google Scholar] [CrossRef]
- Lewandowska, W.; Mahillon, J.; Drewnowska, J.M.; Swiecicka, I. Insight into the phylogeny and antibiotic resistance of Pseudomonas spp. originating from soil of the Białowieża National Park in Northeastern Poland. Front. Microbiol. 2025, 16, 1454510. [Google Scholar] [CrossRef]
- Girard, L.; Lood, C.; Höfte, M.; Vandamme, P.; Rokni-Zadeh, H.; Van Noort, V.; Lavigne, R.; De Mot, R. The Ever-Expanding Pseudomonas Genus: Description of 43 New Species and Partition of the Pseudomonas putida Group. Microorganisms 2021, 9, 1766. [Google Scholar] [CrossRef]
- Documentación y Manuales. Available online: https://www.bruker.com/es/services/user-manuals.html (accessed on 15 May 2025).
- The European Committee on Antimicrobial Susceptibility Testing—EUCAST. Available online: https://www.eucast.org/ (accessed on 15 May 2025).
- Babraham Bioinformatics—FastQC A Quality Control Tool for High Throughput Sequence Data. Available online: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed on 15 May 2025).
- Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef]
- Wick, R.R.; Judd, L.M.; Gorrie, C.L.; Holt, K.E. Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput. Biol. 2017, 13, e1005595. [Google Scholar] [CrossRef] [PubMed]
- Orakov, A.; Fullam, A.; Coelho, L.P.; Khedkar, S.; Szklarczyk, D.; Mende, D.R.; Schmidt, T.S.B.; Bork, P. GUNC: Detection of chimerism and contamination in prokaryotic genomes. Genome Biol. 2021, 22, 178. [Google Scholar] [CrossRef] [PubMed]
- Gurevich, A.; Saveliev, V.; Vyahhi, N.; Tesler, G. QUAST: Quality assessment tool for genome assemblies. Bioinformatics 2013, 29, 1072–1075. [Google Scholar] [CrossRef] [PubMed]
- Seemann, T. Prokka: Rapid prokaryotic genome annotation. Bioinformatics 2014, 30, 2068–2069. [Google Scholar] [CrossRef]
- Lumpe, J.; Gumbleton, L.; Gorzalski, A.; Libuit, K.; Varghese, V.; Lloyd, T.; Tadros, F.; Arsimendi, T.; Wagner, E.; Stephens, C.; et al. GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identification. PLoS ONE 2023, 18, e0277575. [Google Scholar] [CrossRef]
- Ondov, B.D.; Treangen, T.J.; Melsted, P.; Mallonee, A.B.; Bergman, N.H.; Koren, S.; Phillippy, A.M. Mash: Fast genome and metagenome distance estimation using MinHash. Genome Biol. 2016, 17, 132. [Google Scholar] [CrossRef]
- PubMLST—Public Databases for Molecular Typing and Microbial Genome Diversity. Available online: https://pubmlst.org/ (accessed on 25 March 2025).
- Liang, Q.; Liu, C.; Xu, R.; Song, M.; Zhou, Z.; Li, H.; Dai, W.; Yang, M.; Yu, Y.; Chen, H. fIDBAC: A Platform for Fast Bacterial Genome Identification and Typing. Front. Microbiol. 2021, 12, 723577. [Google Scholar] [CrossRef]
- Meier-Kolthoff, J.P.; Göker, M. TYGS is an automated high-throughput platform for state-of-the-art genome-based taxonomy. Nat. Commun. 2019, 10, 2182. [Google Scholar] [CrossRef]
- Olm, M.R.; Brown, C.T.; Brooks, B.; Banfield, J.F. dRep: A tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017, 11, 2864–2868. [Google Scholar] [CrossRef]
- Richter, M.; Rosselló-Móra, R. Shifting the genomic gold standard for the prokaryotic species definition. Proc. Natl. Acad. Sci. USA 2009, 106, 19126–19131. [Google Scholar] [CrossRef]
- Seemann, T. Tseemann/Snippy. 2025. Available online: https://github.com/tseemann/snippy (accessed on 15 May 2025).
- Nguyen, L.T.; Schmidt, H.A.; Von Haeseler, A.; Minh, B.Q. IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies. Mol. Biol. Evol. 2015, 32, 268–274. [Google Scholar] [CrossRef] [PubMed]
- Croucher, N.J.; Page, A.J.; Connor, T.R.; Delaney, A.J.; Keane, J.A.; Bentley, S.D.; Parkhill, J.; Harris, S.R. Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids Res. 2015, 43, e15. [Google Scholar] [CrossRef] [PubMed]
- Letunic, I.; Bork, P. Interactive Tree of Life (iTOL) v6: Recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Res. 2024, 52, W78–W82. [Google Scholar] [CrossRef] [PubMed]
- Shimoyama, Y. ANIclustermap: A Tool for Drawing ANI Clustermap Between All-vs-All Microbial Genomes. 2022. Available online: https://github.com/moshi4/ANIclustermap (accessed on 15 May 2025).
- Software to Predict Resistomes from Protein or Nucleotide Data, Including Metagenomics Data, Based on Homology and SNP Models. GitHub. Available online: https://github.com/arpcard/rgi (accessed on 15 May 2025).
- Seemann, T. Tseemann/Abricate. 2025. Available online: https://github.com/tseemann/abricate (accessed on 15 May 2025).
- Robertson, J.; Bessonov, K.; Schonfeld, J.; Nash, J.H.E. Universal whole-sequence-based plasmid typing and its utility to prediction of host range and epidemiological surveillance. Microb. Genom. 2020, 6, e000435. [Google Scholar] [CrossRef]
- Fonseca, E.L.; Vicente, A.C.P. Epidemiology of qnrVC alleles and emergence out of the Vibrionaceae family. J. Med. Microbiol. 2013, 62, 1628–1630. [Google Scholar] [CrossRef]
- Reece, R.J.; Maxwell, A. DNA gyrase: Structure and function. Crit. Rev. Biochem. Mol. Biol. 1991, 26, 335–375. [Google Scholar] [CrossRef]
- Shaw, K.J.; Rather, P.N.; Hare, R.S.; Miller, G.H. Molecular genetics of aminoglycoside resistance genes and familial relationships of the aminoglycoside-modifying enzymes. Microbiol. Rev. 1993, 57, 138–163. [Google Scholar] [CrossRef]
- Ramirez, M.S.; Tolmasky, M.E. Aminoglycoside modifying enzymes. Drug Resist. Updates 2010, 13, 151–171. [Google Scholar] [CrossRef]
- Walsh, T.R.; Toleman, M.A.; Poirel, L.; Nordmann, P. Metallo-β-Lactamases: The Quiet before the Storm? Clin. Microbiol. Rev. 2005, 18, 306–325. [Google Scholar] [CrossRef]
- Ghaly, T.M.; Chow, L.; Asher, A.J.; Waldron, L.S.; Gillings, M.R. Evolution of class 1 integrons: Mobilization and dispersal via food-borne bacteria. PLoS ONE 2017, 12, e0179169. [Google Scholar] [CrossRef]
- Gomila, M.; Peña, A.; Mulet, M.; Lalucat, J.; García-Valdés, E. Phylogenomics and systematics in Pseudomonas. Front. Microbiol. 2015, 6, 214. [Google Scholar] [CrossRef] [PubMed]
- Yamada, A.Y.; De Souza, A.R.; Lima, M.D.J.D.C.; Reis, A.D.; Campos, K.R.; Bertani, A.M.D.J.; de Araujo, L.J.T.; Sacchi, C.T.; Tiba-Casas, M.R.; Camargo, C.H. Co-production of Classes A and B Carbapenemases BKC-1 and VIM-2 in a Clinical Pseudomonas Putida Group Isolate from Brazil. Curr. Microbiol. 2022, 79, 250. [Google Scholar] [CrossRef]
- Tohya, M.; Teramoto, K.; Watanabe, S.; Hishinuma, T.; Shimojima, M.; Ogawa, M.; Tada, T.; Tabe, Y.; Kirikae, T.; Carroll, K.C. Whole-Genome Sequencing-Based Re-Identification of Pseudomonas putida/fluorescens Clinical Isolates Identified by Biochemical Bacterial Identification Systems. Microbiol. Spectr. 2022, 10, e0249121. [Google Scholar] [CrossRef] [PubMed]
- Jiang, S.; Li, Y.; Bi, K.; Yang, S.; Xia, H.; Li, S.; Chen, H.; Li, L. Characterization of a novel multi-resistant Pseudomonas juntendi strain from China with chromosomal blaVIM−2 and a megaplasmid coharboring blaIMP−1−like and blaOXA−1. BMC Genom. 2024, 25, 774. [Google Scholar] [CrossRef] [PubMed]
- Picollo, M.; Ferraro, D.K.; Pérez, G.; Reijtman, V.; Gomez, S.; Garcia, M.E.; Mastroianni, A.; Rosanova, M.T. Bacteriemia por Pseudomonas putida en niños: Serie de casos. Enfermedades Infecc. Microbiol. Clínica 2023, 41, 221–224. [Google Scholar] [CrossRef]
- Avent, M.L.; McCarthy, K.L.; Sime, F.B.; Naicker, S.; Heffernan, A.J.; Wallis, S.C.; Paterson, D.L.; Roberts, J.A.; Khursigara, C.M. Evaluating Mono- and Combination Therapy of Meropenem and Amikacin against Pseudomonas aeruginosa Bacteremia in the Hollow-Fiber Infection Model. Microbiol. Spectr. 2022, 10, e0052522. [Google Scholar] [CrossRef]
- Mahmoud, H.; Zakaria, S.; Kishk, R.; Al-Amir, A. Effect of Meropenem-Colistin and Meropenem-Amikacin Combinations against Carbapenem-Resistant Pseudomonas aeruginosa Isolates in Suez Canal University Hospitals. Microbes Infect. Dis. 2021, 2, 308–316. [Google Scholar] [CrossRef]
- Böhm, L.; Schaller, M.E.; Balczun, C.; Krüger, A.; Schummel, T.; Ammon, A.; Klein, N.; Helbing, D.L.; Eming, R.; Fuchs, F. A Case of Pseudomonas straminea Blood Stream Infection in an Elderly Woman with Cellulitis. Infect. Dis. Rep. 2024, 16, 699–706. [Google Scholar] [CrossRef]
Disk Diffusion Method | Microscan Panel | ||||
---|---|---|---|---|---|
Antibiotic (Disc Content Mcg) | Zone Diameter (mm) | Zone Diameter Breakpoint (mm) R< | MIC (mg/L) | MIC Breakpoint (mg/L) R> | Interpretation |
Ceftazidime (10) | 12 | 17 | 32 | 8 | R |
Cefepime (30) | 17 | 21 | >8 | 8 | R |
Ceftazidime/avibactam (P. aeruginosa criteria) (10-4) | 12 | 17 | >4 | 8 | R |
Ceftolozane/tazobactam (P. aeruginosa criteria) (30-10) | 6 | 23 | >8 | 4 | R |
Cefiderocol (P. aeruginosa criteria) (30) | 28 | 22 | ≤0.016 | 2 | S |
Piperacillin-tazobactam (30-6) | 12 | 18 | >16 | 16 | R |
Aztreonam (30) | 21 | 18 | 16 | 16 | I |
Imipenem (10) | 6 | 20 | >4 | 4 | R |
Meropenem (10) | 6 | 18 | >8 | 2 | R |
Ciprofloxacin (5) | 6 | 26 | >1 | 0.5 | R |
Levofloxacin (5) | 6 | 18 | >1 | 2 | R |
Tobramycin (10) | 13 | 18 | >4 | 2 | R |
Amikacin (30) | 26 | 15 | ≤8 | 16 | S |
Colistin | ≤2 | 4 | S |
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Badenas-Alzugaray, D.; Valour, L.; Tristancho-Baró, A.; Núñez-Medina, R.; Milagro-Beamonte, A.M.; Torres-Manrique, C.; Gilaberte-Angós, B.; López-Calleja, A.I.; Rezusta-López, A. Characterization of Pseudomonas kurunegalensis by Whole-Genome Sequencing from a Clinical Sample: New Challenges in Identification. Reports 2025, 8, 104. https://doi.org/10.3390/reports8030104
Badenas-Alzugaray D, Valour L, Tristancho-Baró A, Núñez-Medina R, Milagro-Beamonte AM, Torres-Manrique C, Gilaberte-Angós B, López-Calleja AI, Rezusta-López A. Characterization of Pseudomonas kurunegalensis by Whole-Genome Sequencing from a Clinical Sample: New Challenges in Identification. Reports. 2025; 8(3):104. https://doi.org/10.3390/reports8030104
Chicago/Turabian StyleBadenas-Alzugaray, David, Laura Valour, Alexander Tristancho-Baró, Rossi Núñez-Medina, Ana María Milagro-Beamonte, Carmen Torres-Manrique, Beatriz Gilaberte-Angós, Ana Isabel López-Calleja, and Antonio Rezusta-López. 2025. "Characterization of Pseudomonas kurunegalensis by Whole-Genome Sequencing from a Clinical Sample: New Challenges in Identification" Reports 8, no. 3: 104. https://doi.org/10.3390/reports8030104
APA StyleBadenas-Alzugaray, D., Valour, L., Tristancho-Baró, A., Núñez-Medina, R., Milagro-Beamonte, A. M., Torres-Manrique, C., Gilaberte-Angós, B., López-Calleja, A. I., & Rezusta-López, A. (2025). Characterization of Pseudomonas kurunegalensis by Whole-Genome Sequencing from a Clinical Sample: New Challenges in Identification. Reports, 8(3), 104. https://doi.org/10.3390/reports8030104