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1,049 results for “Pseudomonas”
High prevalence of lipopolysaccharide mutants and R2-Pyocin susceptible variants in Pseudomonas aeruginosa populations sourced from cystic fibrosis lung infections
<p>Chronic, highly antibiotic-resistant infections in cystic fibrosis (CF) lungs contribute to increasing morbidity and mortality. <em>Pseudomonas</em> <em>aeruginosa</em>, a common CF pathogen, exhibits resistance to multiple antibiotics, contributing to antimicrobial resistance (AMR). These bacterial populations display genetic and phenotypic diversity, but it is unclear how this diversity affects susceptibility to bacteriocins. R-pyocins, i.e. bacteriocins produced by <em>P. aeruginosa</em>, are phage-tail-like antimicrobials. R-pyocins have potential as antimicrobials, however, recent research suggests the diversity of <em>P. aeruginosa</em> variants within CF lung infections leads to varying susceptibility to R-pyocins. This variation may be linked to changes in lipopolysaccharide (LPS), acting as the R-pyocin receptor. Currently, it is unknown how frequently R-pyocin-susceptible strains are in chronic CF lung infection, particularly when considering the heterogeneity within these strains. In this study, we tested R2-pyocin susceptibility of 139 <em>P. aeruginosa</em> variants from 17 sputum samples of seven CF patients and analyzed LPS phenotypes. We found that 83% of sputum samples did not have R2-pyocin-resistant variants, while nearly all samples contained susceptible variants. There was no correlation between LPS phenotype and R2-pyocin susceptibility, though we estimate that about 76% of sputum-derived variants lack an O-specific antigen, 40% lack a common antigen, and 24% have altered LPS cores. The absence of a correlation between LPS phenotype and R-pyocin susceptibility suggests LPS packing density may play a significant role in R-pyocin susceptibility among CF variants. Our research supports the potential of R-pyocins as therapeutic agents, as many infectious CF variants are susceptible to R2-pyocins, even within diverse bacterial populations.</p>
Efectos de las mezclas del fármaco cloperidona y los antibióticos gentamicina/azitromicina sobre la formación de biopelícula de Pseudomonas aeruginosa
<p>Pseudomonas aeruginosa es una bacteria oportunista en infecciones nosocomiales, que tiene la capacidad de reducir la actividad de los antibióticos de amplio espectro debido a la formación de biopelícula, llevando a que sea categorizada como una bacteria de prioridad crítica a nivel mundial. La biopelícula está formada por comunidades de bacterias dentro de una matriz extracelular compuesta de proteínas, polisacáridos y ADN extracelular que le confieren protección, por ello se ha considerado como un blanco potencial para el control de P. aeruginosa. En este sentido, el objetivo del presente estudio fue evaluar el efecto de Cloperidona, Gentamicina y Azitromicina, así como de las mezclas binarias de Cloperidona/Gentamicina y Cloperidona/Azitromicina sobre la susceptibilidad y la formación de la biopelícula de P. aeruginosa (PAO1) ATCC BAA-47. Para lo cual, se determinó las concentraciones mínimas inhibitorias (CMI) por la técnica de microdilución para los compuestos y las dos mezclas, así como su efecto en la formación de biopelícula de P. aeruginosa por la técnica de tinción con cristal violeta. A partir de los resultados se determinó que las CMI para inhibir el crecimiento de P aeruginosa de los antibióticos gentamicina y azitromicina fueron de 1 y 31.25 µg/mL, respectivamente, mientras que Cloperidona no mostró efectos inhibitorios sobre el crecimiento a la máxima concentración evaluada de 100 µg/mL. Adicionalmente, para los compuestos, el mayor efecto en la formación de biopelícula a la menor concentración fue para Cloperidona a 0.39 µg/mL, gentamicina a 0.5 µg/mL y azitromicina a 1.95 µg/mL, con porcentajes de inhibición de 56%, 50% y 88%, respectivamente. Para las dos mezclas Cloperidona/Azitromicina y Cloperidona/Gentamicina (concentraciones sub-MIC) ninguna presentó mayores efectos inhibitorios si se compara con el efecto de la gentamicina cuando fue evaluada de manera independiente. Sin embargo, las mezclas Cloperidona/Gentamicina (0.39 µg/mL/ 0.03 µg/mL) y Cloperidona/Azitromicina (0.39 µg/mL/ 0.97 µg/mL) fueron las que mostraron el mayor efecto inhibitorio sobre la biopelícula con porcentajes de 77% y 88%, además de presentar efectos sinérgicos ya que fue posible bajar la concentración de los antibióticos en ambos casos para tener efectos inhibitorios en la formación de biopelícula. Estos resultados permiten concluir que las mezclas de los antibióticos con Cloperidona pueden ser utilizados para la reducción en la formación de la biopelícula, uno de los principales mecanismos que utiliza la bacteria para reducir la acciones de los antibióticos y así ser considerado como estrategia para frenar la resistencia microbiana.</p>
Supplementary data for Competition and Virulence in Pseudomonas syringae
<p><strong>Supplementary data 2.1.1</strong></p><p>CSV file describing 2,161 PSSC genomes used in the evaluation of PCR primers in chapter 2.1 and used as reference genomes for classification of isolates at syringae.org in chapter 2.2. file contains RefSeq accession numbers for each genome, T3E family repertoires, and phylogroup and ANI clusters assigned to each genome (metadata.csv) </p><p> </p><p><strong>Supplementary data 2.1.2</strong></p><p>Table describing columns contained in Supplementary data 2.1.1</p><p> </p><p><strong>Supplementary data 2.2</strong></p><p>Excel file with in-silico amplification rates for all 16 primer sets evaluated in chapter 2.1, both overall and by phylogroup.</p><p> </p><p><strong>Supplementary data 2.3</strong></p><p>Newick tree file containing the phylogenetic tree of 2,161 PSSC genomes used throughout chapters 2 and 3, with bootstrap values.</p><p> </p><p><strong>Supplementary data 2.4</strong></p><p>HMM file containing hidden Markov models for all VFOCs identified in chapter 2.1 and 2.2.</p><p> </p><p><strong>Supplementary data 2.5.1</strong></p><p>JSON file describing all canonical T3SS, T3E, and WHOP genes in the 2,161 genomes used in chapter 2.1 and 2.2, as detected by HMMER, with genome accessions as top-level keys. Additional keys found in each file are described in Table 2.2.3.</p><p> </p><p><strong>Supplementary data 2.5.2</strong></p><p>Table containing description of JSON structure for Supplementary data 2.5.1</p><p> </p><p><strong>Supplementary data 2.6.1</strong></p><p>JSON file describing all canonical T3SS, T3E, and WHOP genes in the 2,161 genomes used in chapter 2.1 and 2.2, as detected by HMMER, with protein accessions as top-level keys. Additional keys found in each file are described in Table 2.2.3.</p><p> </p><p><strong>Supplementary data 2.6.2</strong></p><p>Table containing description of JSON structure for Supplementary data 2.6.1</p><p> </p><p><strong>Supplementary data 2.7</strong></p><p>TSV file containing LIN numbers associated with each reference genome, used as input for training classifiers. </p><p> </p><p><strong>Supplementary data 2.8</strong></p><p>FASTA file containing in-silico amplicons generated from primer set CTS_Hwang, used as input for training a Naïve Bayes classifier.</p><p> </p><p><strong>Supplementary data 2.9</strong></p><p>FASTA file containing in-silico amplicons generated from primer set gapA_Hwang, used as input for training a Naïve Bayes classifier. </p><p> </p><p><strong>Supplementary data 2.10</strong></p><p>FASTA file containing in-silico amplicons generated from primer set gyrB_Hwang, used as input for training a Naïve Bayes classifier. </p><p> </p><p><strong>Supplementary data 2.11</strong></p><p>FASTA file containing in-silico amplicons generated from primer set pgi_Yan, used as input for training a Naïve Bayes classifier. </p><p> </p><p><strong>Supplementary data 2.12</strong></p><p>FASTA file containing in-silico amplicons generated from primer set rpoD_Hwang, used as input for training a Naïve Bayes classifier. </p><p> </p><p><strong>Supplementary data 2.13</strong></p><p>Naïve Bayes classifier for primer set CTS_Hwang</p><p> </p><p><strong>Supplementary data 2.14</strong></p><p>Naïve Bayes classifier for primer set gapA_Hwang</p><p> </p><p><strong>Supplementary data 2.15</strong></p><p>Naïve Bayes classifier for primer set gyrB_Hwang</p><p> </p><p><strong>Supplementary data 2.16</strong></p><p>Naïve Bayes classifier for primer set pgi_Yan</p><p> </p><p><strong>Supplementary data 2.17</strong></p><p>Naïve Bayes classifier for primer set rpoD_Hwang</p><p> </p><p><strong>Supplementary data 3.1</strong></p><p>HMM1, representing tailocin tail fibers associated with killing class 1</p><p> </p><p><strong>Supplementary data 3.2</strong></p><p>HMM2, representing tailocin tail fibers associated with killing class 2</p><p> </p><p><strong>Supplementary data 3.3</strong></p><p>HMM3, representing tailocin tail fibers from PSSC strain UB246</p><p> </p><p><strong>Supplementary data 3.4</strong></p><p>Amino acid sequence for WP_044313553.1, representative of type 1a tailocin-associated tail fiber used for protein structure prediction in Supplementary data 3.5</p><p> </p><p><strong>Supplementary data 3.5</strong></p><p>PDB file containing predicted structure of WP_044313553.1, representative of type 1a tailocin-associated tail fiber </p><p> </p><p><strong>Supplementary data 3.6</strong></p><p>Amino acid sequence for WP_122688044.1, representative of type 1b tailocin-associated tail fiber used for protein structure prediction in Supplementary data 3.7</p><p> </p><p><strong>Supplementary data 3.7</strong></p><p>PDB file containing predicted structure of WP_122688044.1, representative of type 1b tailocin-associated tail fiber</p><p> </p><p><strong>Supplementary data 3.8</strong></p><p>Amino acid sequence for WP_005768002.1, representative of type 2 tailocin-associated tail fiber used for protein structure prediction in Supplementary data 3.9</p><p> </p><p><strong>Supplementary data 3.9</strong></p><p>PDB file containing predicted structure of WP_005768002.1, representative of type 2 tailocin-associated tail fiber</p><p> </p><p><strong>Supplementary data 3.10</strong></p><p>Amino acid sequence for WP_024674765.1, representative of type 3 tailocin-associated tail fiber used for protein structure prediction in Supplementary data 311</p><p> </p><p><strong>Supplementary data 3.11</strong></p><p>PDB file containing predicted structure of WP_024674765.1, representative of type 3 tailocin-associated tail fiber</p><p> </p><p><strong>Supplementary data 3.12</strong></p><p>Amino acid sequence for WP_198721597.1, representative of RSA1-like prophage-associated tail fiber used for protein structure prediction in Supplementary data 3.13</p><p> </p><p><strong>Supplementary data 3.13</strong></p><p>PDB file containing predicted structure of WP_198721597.1, representative of RSA1-like prophage-associated tail fiber</p><p> </p><p><strong>Supplementary data 3.14</strong></p><p>CSV file containing HMM1 and HMM2 genomic screen results, with accession numbers and identities of tail fibers detected in each genome.</p><p> </p><p><strong>Supplementary data 3.15</strong></p><p>CSV file containing HMM3 genomic screen results, with copy number of tail fibers detected in each genome and the phylogroup the genome belongs to</p>
Study to Evaluate Arikayce™ in CF Patients With Chronic Pseudomonas Aeruginosa Infections
ClinicalTrials.gov study NCT01315678. IPD Sharing: Not stated. Countries: 18. Publications: 1.
Dose Escalation Study of KB001 in Cystic Fibrosis Patients Infected With Pseudomonas Aeruginosa
ClinicalTrials.gov study NCT00638365. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Aztreonam for Inhalation Solution vs Tobramycin Inhalation Solution in Patients With Cystic Fibrosis & Pseudomonas Aeruginosa
ClinicalTrials.gov study NCT00757237. IPD Sharing: Not stated. Countries: 13. Publications: 1.
Aztreonam Lysine for Pseudomonas Infection Eradication Study
ClinicalTrials.gov study NCT01375049. IPD Sharing: Not stated. Countries: 9. Publications: 1.
Effort to Prevent Nosocomial Pneumonia Caused by Pseudomonas Aeruginosa in Mechanically Ventilated Subjects
ClinicalTrials.gov study NCT02696902. IPD Sharing: Not stated. Countries: 14. Publications: 1.
Extension Study of Liposomal Amikacin for Inhalation in Cystic Fibrosis (CF) Patients With Chronic Pseudomonas Aeruginosa (Pa) Infection
ClinicalTrials.gov study NCT01316276. IPD Sharing: Not stated. Countries: 17. Publications: 1.
Phase 3 Study of Aztreonam for Inhalation Solution (AZLI) in a Continuous Alternating Therapy Regimen for the Treatment of Chronic Pseudomonas Aeruginosa Infection in Patients With CF
ClinicalTrials.gov study NCT01641822. IPD Sharing: Not stated. Countries: 1. Publications: 1.
OPTIMIZing Treatment for Early Pseudomonas Aeruginosa Infection in Cystic Fibrosis
ClinicalTrials.gov study NCT02054156. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Safety/Tolerability Study of Arikayce™ in Cystic Fibrosis Patients With Chronic Infection Due to Pseudomonas Aeruginosa
ClinicalTrials.gov study NCT00558844. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Ceftolozane-Tazobactam for Directed Treatment of Pseudomonas Aeruginosa Bacteremia and Pneumonia in Patients With Hematological Malignancies and Hematopoietic Stem Cell Transplantation
ClinicalTrials.gov study NCT04673175. IPD Sharing: NO. Countries: 1. Publications: 7.
Study of Aztreonam for Inhalation in Children With Cystic Fibrosis and New Infection of the Airways by Pseudomonas Aeruginosa Bacteria
ClinicalTrials.gov study NCT03219164. IPD Sharing: YES. Countries: 12. Publications: 1.
Data from: Life in the cystic fibrosis upper respiratory tract influences competitive ability of the opportunistic pathogen Pseudomonas aeruginosa
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Data from: Plant pathogenic bacterium Ralstonia solanacearum can rapidly evolve tolerance to antimicrobials produced by Pseudomonas biocontrol bacteria
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Aztreonam-induced filamentation in Pseudomonas aeruginosa
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Pivotal role of O-antigenic polysaccharide display in the sensitivity against phage tail-like particles in environmental Pseudomonas kin competition
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Ecology drives the evolution of diverse social strategies in Pseudomonas aeruginosa
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Heterogenous susceptibility to R-pyocins in populations of Pseudomonas aeruginosa sourced from cystic fibrosis lungs
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