The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Antibiotic resistance in Pseudomonas aeruginosa – Mechanisms, epidemiology and evolution
Abstract
Antibiotics are powerful drugs used in the treatment of bacterial infections. The inappropriate use of these medicines has driven the dissemination of antibiotic resistance (AR) in most bacteria. Pseudomonas aeruginosa is an opportunistic pathogen commonly involved in environmental- and difficult-to-treat hospital-acquired infections. This species is frequently resistant to several antibiotics, being in the "critical" category of the WHO's priority pathogens list for research and development of new antibiotics. In addition to a remarkable intrinsic resistance to several antibiotics, P. aeruginosa can acquire resistance through chromosomal mutations and acquisition of AR genes. P. aeruginosa has one of the largest bacterial genomes and possesses a significant assortment of genes acquired by horizontal gene transfer (HGT), which are frequently localized within integrons and mobile genetic elements (MGEs), such as transposons, insertion sequences, genomic islands, phages, plasmids and integrative and conjugative elements (ICEs). This genomic diversity results in a non-clonal population structure, punctuated by specific clones that are associated with significant morbidity and mortality worldwide, the so-called high-risk clones. Acquisition of MGEs produces a fitness cost in the host, that can be eased over time by compensatory mutations during MGE-host coevolution. Even though plasmids and ICEs are important drivers of AR, the underlying evolutionary traits that promote this dissemination are poorly understood. In this review, we provide a comprehensive description of the main strategies involved in AR in P. aeruginosa and the leading drivers of HGT in this species. The most recently developed genomic tools that allowed a better understanding of the features contributing for the success of P. aeruginosa are discussed.
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“Intrinsic resistance in most strains arises from low outer membrane permeability, the production of enzymes that inactivate antimicrobials such as the AmpC β-lactamase, and antimicrobial efflux pumps139,147,149.”
From Pseudomonas aeruginosa: ecology, evolution, pathogenesis and antimicrobial susceptibility (2025) · cites this paper as [139]
In the citing paper
“Notable sequence types (STs), classified based on multilocus sequence typing138, include widespread clones such as clone C (ST17) and PA14 (ST253), alongside epidemic clones associated with cystic fibrosis, such as the Liverpool Epidemic Strain (LES, ST146)139–143.”
From Pseudomonas aeruginosa: ecology, evolution, pathogenesis and antimicrobial susceptibility (2025) · cites this paper as [139]
In the citing paper
“The antimicrobial resistance mechanisms of P. aeruginosa are multifaceted, encompassing intrinsic, adaptive and acquired forms139,147,148 (Fig. 3a).”
From Pseudomonas aeruginosa: ecology, evolution, pathogenesis and antimicrobial susceptibility (2025) · cites this paper as [139]
In the citing paper
“Acquired resistance is gained through horizontal gene transfer (HGT) or mutations that lead to enhanced efflux pump activity149, reduced antibiotic uptake, hyperproduction of β-lactamases and alterations to antibiotic targets139,147.”
From Pseudomonas aeruginosa: ecology, evolution, pathogenesis and antimicrobial susceptibility (2025) · cites this paper as [139]
In the citing paper
“P. aeruginosa that was isolated from European populations with a combined resistance was 12.9%.9”
From Pseudomonas aeruginosa: pathogenesis, virulence factors, antibiotic resistance, interaction with host, technology advances and emerging therapeutics (2022) · cites this paper as [9]
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Cites
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Global increase and geographic convergence in antibiotic consumption between 2000 and 2015
Klein, E. Y., Van Boeckel, T. P., Martinez, E. M., Pant, S., Gandra, S., Levin, S. A., Goossens, H., & Laxminarayan, R. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717295115 · Cited by 3,232
Antibiotic resistance is ancient
D’Costa, V. M., King, C. E., Kalan, L., Morar, M., Sung, W. W. L., Schwarz, C., Froese, D., Zazula, G., Calmels, F., Debruyne, R., et al. (2011) · Nature · doi:10.1038/nature10388 · Cited by 2,720
Extensive impact of non-antibiotic drugs on human gut bacteria
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Sampling the Antibiotic Resistome
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Functional characterization of the antibiotic resistance reservoir in the human microflora
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Cited by
Qin, S., Xiao, W., Zhou, C., Pu, Q., Deng, X., Lan, L., Liang, H., Song, X., & Wu, M. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-01056-1 · Cited by 1,703
Pseudomonas aeruginosa: An Audacious Pathogen with an Adaptable Arsenal of Virulence Factors
Jurado-Martín, I., Sainz-Mejías, M., & McClean, S. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22063128 · Cited by 698
Microbiome Engineering: Synthetic Biology of Plant-Associated Microbiomes in Sustainable Agriculture
Ke, J., Wang, B., & Yoshikuni, Y. (2021) · Trends in Biotechnology · doi:10.1016/j.tibtech.2020.07.008 · Cited by 406
Pseudomonas aeruginosa: ecology, evolution, pathogenesis and antimicrobial susceptibility
Letizia, M., Diggle, S. P., & Whiteley, M. (2025) · Nature Reviews Microbiology · doi:10.1038/s41579-025-01193-8 · Cited by 231
Verdial, C., Serrano, I., Tavares, L., Gil, S., & Oliveira, M. (2023) · Biomedicines · doi:10.3390/biomedicines11041221 · Cited by 71
Metadata
- Authors
- Botelho, J., Grosso, F., & Peixe, L.
- Venue
- Drug Resistance Updates
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 44
- Pages
- 100640
- ISSN
- 1368-7646 (ISSN-L)
- OpenAlex
- W2961017809
- Cited by
- 550
- References
- 16
- FWCI
- 13.39
- Type
- journal article
- Language
- en
- Open access
- closed
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- Botelho, J., Grosso, F., & Peixe, L. (2019). Antibiotic resistance in Pseudomonas aeruginosa – Mechanisms, epidemiology and evolution. Drug Resistance Updates. https://doi.org/10.1016/j.drup.2019.07.002
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
