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The deficiency of poly-β-1,6-N-acetyl-glucosamine deacetylase trigger A. baumannii to convert to biofilm-independent colistin-tolerant cells
Acinetobacter baumannii is a nosocomial pathogen that can be resistant to antibiotics by rapidly modulating its anti-drug mechanisms. The multidrug-resistant A. baumannii has been considered one of the most threatening pathogens to our society. Biofilm formation and persistent cells within the biofi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9935895/ https://www.ncbi.nlm.nih.gov/pubmed/36797306 http://dx.doi.org/10.1038/s41598-023-30065-5 |
Sumario: | Acinetobacter baumannii is a nosocomial pathogen that can be resistant to antibiotics by rapidly modulating its anti-drug mechanisms. The multidrug-resistant A. baumannii has been considered one of the most threatening pathogens to our society. Biofilm formation and persistent cells within the biofilm matrix are recognized as intractable problems, especially in hospital-acquired infections. Poly-β-1,6-N-acetyl-glucosamine (PNAG) is one of the important building blocks in A. baumannii’s biofilm. Here, we discover a protein phosphoryl-regulation on PNAG deacetylase, AbPgaB1, in which residue Ser411 was phosphorylated. The phosphoryl-regulation on AbPgaB1 modulates the product turnover rate in which deacetylated PNAG is produced and reflected in biofilm production. We further uncovered the PgaB deficient A. baumannii strain shows the lowest level of biofilm production but has a high minimal inhibition concentration to antibiotic colistin and tetracycline. Based on bactericidal post-antibiotic effects and time-dependent killing assays with antibacterial drugs, we claim that the PgaB-deficient A. baumannii converts to colistin-tolerant cells. This study utilizes a biofilm-independent colistin-tolerant model of A. baumannii to further investigate its characteristics and mechanisms to better understand clinical outcomes. |
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