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646 results for “Pseudomonas aeruginosa”
Lab disease outcomes data evaluating how antibiotic tolerant vs. non-tolerant cell-free supernatant from Pseudomonas aeruginosa affects the interaction between a fungal pathogen (Batrachochytrium dendrobatidis) and amphibian (Rana sylvaticus), 2022.
Microbes living on hosts and in the environment can play a key role in helping hosts to combat pathogens. However, antibiotic-induced alterations to microbial metabolite production could disrupt this dynamic. Here, we investigated whether antibiotic tolerance influences the anti-pathogenic properties of host-associated (living on the host; biofilms) and environmental (living in the soil of water column; planktonic) microbes in vitro and in vivo. For our model host and pathogen, we used the amphibian (Rana sylvatica)-Batrachochytrium dendrobatidis (Bd) system. For our model host-associated (biofilm) and environmental (planktonic) microbes, we used four strains of Pseudomonas aeruginosa that vary in their tolerance to antibiotics and their biofilm-forming capabilities: Planktonic, non-antibiotic tolerant (ΔsagS/VC); Planktonic, antibiotic tolerant (ΔsagS::sagS_L154A); Biofilm, non-antibiotic tolerant (ΔsagS::sagS_D105A); Biofilm, antibiotic tolerant (ΔsagS::sagS). We collected cell-free supernatants (CFS) from each strain to examine the effects of metabolites. We conducted four experiments. In our pathogen-only exposures to test direct effects of metabolites on Bd, we exposed Bd zoospores to each P. aeruginosa CFS at six concentrations. After 11 days of growth, we measured relative abundance of Bd across each treatment. In our host-only exposures to test effects of metabolites on host disease outcomes, we placed R. sylvatica tadpoles in individual units containing each P. aeruginosa CFS. After 48 hours, water was changed into clean well water (no CFS). Bd zoospores were immediately added to each experimental unit following the water change. After 5 days of Bd exposure, we measured snout-vent length (SVL), mass, developmental stage, and Bd quantification in the mouthparts using qPCR for each tadpole. In our host-pathogen exposures to test interactive effects of metabolites on hosts in the presence of the pathogen, we conducted the same experiment as above. However, ins
Dataset of "Single-step Purification and Characterization of Pseudomonas aeruginosa Azurin"
<p>Azurin is a small periplasmic blue copper protein. We have created a novel approach for expressing and purifying of azurin in E. coli with high yields and optimal metalation ratio. The azurin sequence was N-terminally fused with a GST tag and protein was purified by single-step affinity chromatography on a GST-trap column. The N-terminal tag was cleaved off by HRV 3C protease and sufficient metalation was endured by incubation with copper sulphate. UV-VIS absorption, mass spectroscopy, and circular dichroism analysis all validated the effective production of azurin, appropriate protein folding and the development of an active site with an associated cofactor. MD simulations verified that incorporation of the N-terminal GPLGS segment does not affect the wild-type azurin structure.</p>
Topologies and structures of the RND transporters MexB, MexF, and MexY of Pseudomonas aeruginosa.
<p>The file contains the topologies and the structures of the RND transporters MexB, MexF, and MexY of Pseudomonas aeruginosa used to identify a common recognition topology in these transporters by means of computer simulations.</p>
PubMLST allele profiles and sequence alignments of 382 carbapenem-resistant Pseudomonas aeruginosa isolates collected from Japanese hospitals in 2019-2020
<p>This dataset provides PubMLST allele profiles, sequence alignments, and Mash distance data used in the study of "Nationwide genome surveillance of carbapenem-resistant Pseudomonas aeruginosa in Japan". </p>
Genome assemblies of 382 carbapenem-resistant Pseudomonas aeruginosa isolates collected from Japanese hospitals in 2019−2020
<p>This dataset provides genome assemblies used in the study of "Nationwide genome surveillance of carbapenem-resistant Pseudomonas aeruginosa in Japan".</p> <p> </p>
Adaptation of Pseudomonas aeruginosa to repeated invasion into a commensal competitor
<p>Sequencing data for pooled isolate samples of <em>Pseudomonas aeruginosa</em> obtained in experimental evolution project "Adaptation of <em>Pseudomonas aeruginosa</em> to repeated invasion into a commensal competitor". To generate sequencing data, the gDNA from 10 isolates for each culture condition was pooled in equimolar ratios and libraries were sequenced using the Illumina NovaSeq6000 platform using a 250 bp paired-end protocol, submitted for x510 depth sequencing. Dataset provides raw sequencing data (untrimmed) for pooled starting culture isolates, monoculture evolved lines, and coculture evolved lines.</p>
Pseudomonas aeruginosa PA14
This is one of the Wormbiome database archive files.<br>This entry includes all the genome annotation files related to Pseudomonas aeruginosa PA14, a\(n\) Gammaproteobacteria.<br>The Wormbiome collection is an online database dedicated to centralizing all the information related to bacteria associated with C. elegans. More information on <a href="https://bitbucket.org/the-samuel-lab/wbm_scripts/src/master/DOCS/Annotations_output.md" target="_blank" rel="noopener noreferrer">the documentation page</a>.<br><br>
The evolution of antimicrobial peptide resistance in Pseudomonas aeruginosa is severely constrained by random peptide mixtures
<p><span>The prevalence of antibiotic-resistant pathogens has become a major threat to public health, requiring swift initiatives for discovering new strategies to control bacterial infections. Hence, antibiotic stewardship and rapid diagnostics, but also the development, and prudent use, of novel effective antimicrobial agents are paramount. Ideally, these agents should be less likely to select for resistance in pathogens than currently available conventional antimicrobials. The usage of antimicrobial Peptides (AMPs), key components of the innate immune response, and combination therapies, have been proposed as strategies to diminish the emergence of resistance.</span></p> <p><span>Herein, we investigated whether newly developed random antimicrobial peptide mixtures (RPMs) can significantly reduce the risk of resistance evolution <em>in vitro</em> to that of single sequence AMPs, using the ESKAPE pathogen <em>Pseudomonas aeruginosa</em> (<em>P. aeruginosa</em>) as a model Gram-negative bacterium. Infections of this pathogen are difficult to treat due the inherent resistance to many drug classes, enhanced by the capacity to</span><span> form biofilms. </span><em><span>P. aeruginosa</span></em><span> was experimentally evolved in the presence of AMPs or RPMs, subsequentially assessing the extent of resistance evolution and cross-resistance/collateral sensitivity between treatments. Furthermore, the fitness costs of resistance on bacterial growth were studied, and whole-genome sequencing used to investigate which mutations could be candidates for causing resistant phenotypes. Lastly, changes in the pharmacodynamics of the evolved bacterial strains were examined.</span></p> <p><span>Our findings suggest that using RPMs bears a much lower risk of resistance evolution compared to AMPs and mostly prevents cross-resistance development to other treatments, while maintaining (or even improving) drug sensitivity. This strengthens the case for using random cocktails of AMPs in favour of single AMPs, against which resistance evolved <em>in vitro</em>, providing an alternative to classic antibiotics worth pursuing.</span></p>
Figure 6 in Blue and red light photoemitters as approach to inhibit Staphylococcus aureus and Pseudomonas aeruginosa growth
Figure 6. Percentage of growth inhibition induced by blue light on bacteria inoculated on saline solution or nutrient rich BHI broth. *Statistically significant difference using Mann-Whitney U test (p <0.05) between blue light exposed S. aureus in saline solution and BHI broth.
Figure 5 in Blue and red light photoemitters as approach to inhibit Staphylococcus aureus and Pseudomonas aeruginosa growth
Figure 5. Effect of blue and red light on S. aureus e P. aeruginosa diluted in BHI nutrient rich medium applied for a period of 3 hours. *Statistically significant difference using Mann-Whitney U test (p <0.05) between blue light exposed and control groups.
Figure 3 in Blue and red light photoemitters as approach to inhibit Staphylococcus aureus and Pseudomonas aeruginosa growth
Figure 3. Determination of the influence of glass or polystyrene plate on the antimicrobial effect of red or blue light in S. aureus and P. aeruginosa cultures. *Statistically significant difference using Mann-Whitney U test (p <0.05) between blue light exposed group and control groups.
Figure 4 in Blue and red light photoemitters as approach to inhibit Staphylococcus aureus and Pseudomonas aeruginosa growth
Figure 4. Effect of blue and red light on S. aureus e P. aeruginosa diluted in saline solution (0.9% NaCl) applied for a period of 3 hours. *Statistically significant difference using Mann-Whitney U test (p <0.05) between blue light exposed group and control groups.
Figure 2 in Blue and red light photoemitters as approach to inhibit Staphylococcus aureus and Pseudomonas aeruginosa growth
Figure 2. Diameter of S.aureus and P. aeruginosa surviving colonies after exposure to blue and red light for 6 hours and incubated for 24 hours (A) and 48 hours (B). *Statistically significant difference using Mann-Whitney U test (p <0.05) for independent samples.
Figure 2 in Phenotypic and molecular characterization of fluoroquinolone resistant Pseudomonas aeruginosa isolates in Palestine
Figure 2. Median-joining network of GyrA (A), ParC (B) and ParE (C) of the haplotypes of fluoroquinolone resistant P. aeruginosa isolates. Each haplotype is represented by a circle. The asterisk (*) denotes the founder haplotype. The size of circle is relative to haplotype frequency. Bars indicate the number of nucleotide substitutions for GyrA (A), ParC (B) and ParE (C) sequences from fluoroquinolone resistant P. aeruginosa isolates recovered in Palestine.
Figure 3 in Phenotypic and molecular characterization of fluoroquinolone resistant Pseudomonas aeruginosa isolates in Palestine
Figure 3. Nucleotide variation positions of GyrA (A), ParC (B) and ParE (C) genes among the studied fluoroquinolone resistant P. aeruginosa isolates according to the references from GenBank. Parsimony informative sites are shaded in light grey, while InDels are shaded in dark gray.
Figure 1 in Phenotypic and molecular characterization of fluoroquinolone resistant Pseudomonas aeruginosa isolates in Palestine
Figure 1. Molecular phylogenetic analysis by Maximum Likelihood method based on the GyrA (A), ParC (B) and ParE (C) sequence from fluoroquinolone resistant P. aeruginosa isolated in Palestine. Reference sequences retrieved from Genbank for the GyrA (A), ParC (B) and ParE (C) genes were denoted by asterisks (*). Sequences from Palestine and reference sequences were used to construct the phylogenetic tree. Evolutionary analyses were conducted in MEGA6.
Raw data for infections of AirGels with Pseudomonas aeruginosa (DOI: 10.1371/journal.pbio.3002209)
<p>This data repository contains the following items:</p> <ol> <li>Imaging data (zip file) containing tiff files for the images displayed in the manuscript as well as the movies from the supporting information.</li> <li>Source data used in plots (zip file) containing spreadsheets with the data that was used to generate the plots in the paper.</li> <li>The single-cell RNA sequencing raw data (R1 and R2 fastq files) from 3 pooled AirGels.</li> </ol>
Pseudomonas aeruginosa predicted prophages from publicly available genomes
<p>Through the Genome Information by Organism section of the NCBI Genome database, <em>P. aeruginosa</em> bacterial genomic assemblies were downloaded (September 2020). Genome quality was assessed totaling 5,383 genomes total. All 5,383 genomes were then entered into VirSorter v.1 (https://github.com/simroux/VirSorter). The data set provided here includes all category 1 and category 4 predicted prophage sequences.</p>
Personalized aerosolised bacteriophage treatment of a chronic lung infection due to multidrug-resistant Pseudomonas aeruginosa
<p>Bacteriophage therapy has been suggested as an alternative or complementary strategy for the treatment of multidrug resistant (MDR) bacterial infections. Here, we report the favourable clinical evolution of a 41-year-old male patient with a Kartagener syndrome complicated by a life-threatening MDR <em>Pseudomonas aeruginosa </em>infection, who was treated successfully with iterative aerosolized phage treatments specifically directed against the patient’s isolate. We followed the longitudinal evolution of both phage and bacterial loads during and after phage administration in respiratory samples. Phage titres in consecutive sputum samples showed <em>in patient</em> phage replication. Phenotypic analysis and whole genome sequencing of sequential bacterial isolates revealed a clonal, but phenotypically diverse population of hypermutator strains. The MDR phenotype in the collected isolates was multifactorial and mainly due to spontaneous chromosomal mutations. All isolates recovered after phage treatment remained phage susceptible. These results demonstrate that clinically significant improvement is achievable by personalised phage therapy even in the absence of complete eradication of <em>P. aeruginosa</em> lung colonization.</p>
Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics
<p>Datasets for manuscript "Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics"</p> <p><strong>Metadata.zip</strong></p> <ol> <li><strong>phenotypes.txt: </strong>tabular file containing binary resistance phenotypes based on CLSI guidelines, where the rows are the isolates and the columns correspond to different drugs. Resistance : 1, susceptibility: 0, missing: intermediate resistant</li> </ol> <p><strong>Features_gpa_exp_snps.zip </strong></p> <p>We provide the processed molecular data as Numpy compressed files (npz.). You can use the Numpy load method to read in these tables https://docs.scipy.org/doc/numpy/reference/generated/numpy.load.htm. The row (strains_list) and column labels (feature_lists) are stored separately.</p> <ol> <li><strong>genexp</strong>: gene expression table directory <ul> <li>genexp_feature_vect.npz: The feature matrix in the numpy format</li> <li>genexp_feature_list.txt: The columns of the feature matrix (features)</li> <li>genexp_strains_list.txt: The rows of the feature matrix (isolates)</li> </ul> </li> <li><strong>gpa: </strong>gene presence/absence table directory <ul> <li>gpa_feature_vect.npz: The feature matrix in the numpy format</li> <li>gpa_feature_list.txt: The columns of the feature matrix (features)</li> <li>gpa_strains_list.txt: The rows of the feature matrix (isolates)</li> </ul> </li> <li><strong>snps: </strong>SNPs table directory <ul> <li>snps_feature_vect.npz: The feature matrix in the numpy format</li> <li>snps_feature_list.txt: The columns of the feature matrix (features)</li> <li>snps_strains_list.txt: The rows of the feature matrix (isolates)</li> </ul> </li> </ol>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.