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1,049 results for “Pseudomonas”
Figure 1 in Effect of calcium on Pseudomonas aeruginasa and Bacillus cereus metabolites
Figure 1. Amylase unit activity of B. cereus (○) and P. aeruginosa (●), grown in NB medium under static conditions at 37 °C.
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.
9Genomes_Pseudomonas
<p>These 9 genomes are associated with the paper entittled "Biocontrol-relevant diversity of wheat-associated <em>Pseudomonas</em>: prevalence of<em> P. sivasensis</em> and identification of the novel species<em> P. arvensis</em> sp. nov. ". The paper is published in PeerJ. Strains are under the patent PAT-20934.</p>
Fig. 3 in Effects of the Secondary Metabolite Producing Pseudomonas fluorescens CHA0 on Soil Protozoa and Bacteria
Fig. 3. Colony forming curves of culturable bacteria in soil microcosms harvested after 1, 7, and 14 days on non-selective agar media. For each harvest event the same plates were counted repeatedly. Statistical significant differences between the treatments at the last counting event of each harvest are indicated by different letters.
Fig. 4 in Effects of the Secondary Metabolite Producing Pseudomonas fluorescens CHA0 on Soil Protozoa and Bacteria
Fig. 4. Abundance of culturable protozoa in the four different soil microcosms. The protozoa were counted by MPN as fast-growing protozoa after 1 week of incubation and as total protozoa after 3 weeks of incubation by inspecting the same plates twice. Significant differences of treatments within each sampling time and incubation time are shown as different small letters above the bars. After one day protozoa was only counted in the control microcosm. Significant differences between the abundance of protozoa in the control microcosm are shown as capital letters. bd: below detection limit of 157 protozoa g–1 dw. nd: not determined.
Fig. 2 in Effects of the Secondary Metabolite Producing Pseudomonas fluorescens CHA0 on Soil Protozoa and Bacteria
Fig. 2. Fate of inoculated P. fluorescence CHA0/gfp1 and P. fluorescens CHA0/pME3424 during incubation in soil microcosms determined as CFU on selective agar media (see Materials and Methods for selective agents). The individual data points for each replicate are shown along with the linear regression line for each strain.
Fig. 1 in Effects of the Secondary Metabolite Producing Pseudomonas fluorescens CHA0 on Soil Protozoa and Bacteria
Fig. 1. Soil respiration measured as accumulated CO 2 during the incubation of microcosms consisting of soil, shredded barley straw and either of three bacterial inoculants: E. aerogenes, P. fluorescens CHA0/gfp1, P. fluorescens CHA0/pME3424. Control treatment did not receive any bacteria.
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>
Data to: Sulfur Amino Acid Status Controls Selenium Methylation in Pseudomonas tolaasii...
<p>data to: Sulfur Amino Acid Status Controls Selenium Methylation in Pseudomonas tolaasii: Identification of a Novel Metabolite from Promiscuous Enzyme Reactions</p> <p>Appl Environ Microbiol 2021 May 26;87(12):e0010421.</p> <p>doi: 10.1128/AEM.00104-21. Epub 2021 May 26.</p> <p> </p>
Pseudomonas veronii Tn-seq analysis of soil and liquid growth conditions
<p>Readme file content</p> <p>The files stored here contain the following material as supplementary and source data for the manuscript in submission</p> <p>Fitness-conditional genes for soil adaptation in the bioaugmentation agent Pseudomonas veronii 1YdBTEX2</p> <p>Marian Morales, Vladimir Sentchilo, Nicolas Carraro, Senka Causevic, Dominique Vuarambon & Jan R. van der Meer</p> <p>Department of Fundamental Microbiology, University of Lausanne, 1015 Lausanne, Switzerland</p> <p><br> %%%%%%%<br> Folder Tn-seq<br> %%%%%%</p> <p>This folder has the scripts, procedures, raw and intermediate data of the Pseudomonas veronii tn-seq libraries Lib1 and Lib2, at time 0 and incubated in liquid, sand or silt microcosms for up to 51 generations of growth.</p> <p>Subfolders are organized to the steps in the analysis, from the initial experimental design, to the sequence mapping, read cleaning, normalization procedures, essentiality analysis, paired (liq-soil) analysis, gene group plots and the COG-KEGG category analysis.</p> <p>Scripts are text-readable for MATLAB, vs 2021b; or BASH-UNIX slurm command line tools. Data files are either .mat (MATLAB readable) or .xlsx (for the major summary steps).</p> <p>Individual procedures and steps are described in Word documents, which then point to scripts in the script folder, output and the data formats.</p> <p>%%%%%%%<br> Community diversity data<br> %%%%%%</p> <p>This folder has the raw fastq- files of the sample reads of the T1 incubated sand or silt-Tn5 library replicates. Four replicates in total, two sequencing data sets which were combined.</p> <p>Folder also has the meta data as to the sample descriptions, and the OTU-taxa output file from Qiime2 analysis.</p> <p> </p>
Barcoding populations of Pseudomonas fluorescens SBW25
<p>In recent years evolutionary biologists have developed increasing interest in the use of barcoding strategies to study eco-evolutionary dynamics of lineages within evolving populations and communities. Although barcoded populations can deliver unprecedented insight into evolutionary change, barcoding microbes presents specific technical challenges. Here, strategies are described for barcoding populations of the model bacterium Pseudomonas fluorescens SBW25, including the design and cloning of barcoded regions, preparation of libraries for amplicon sequencing, and quantification of resulting barcoded lineages. In so doing, we hope to aid the design and implementation of barcoding methodologies in a broad range of model and non-model organisms. In here we deposit, the raw and processed data we have used during our study.</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>
Dataset for Phosphite as an Engineered Niche for Pseudomonas veronii in a Synthetic Soil Bacterial Community
<p>Files containing the source data and analyses used in the manuscript "Phosphite as an Engineered Niche for <em>Pseudomonas veronii </em>in a Synthetic Soil Bacterial Community".</p> <p> </p> <p>Clara Bailey (1), Philip Gwyther (2), Senka Čaušević (2), Brandon L. Greene (1), and Jan Roelof van der Meer (2)</p> <p>1) Department of Chemistry and Biochemistry, University of California, Santa Barbara, Santa Barbara, California, United States</p> <p>2) Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland</p> <p> </p> <p>This dataset contains 16S rRNA gene amplicon sequencing data (in the form of an abundance table, "abund.csv" and combined with CFU counts in "abund_cfu.csv"), toluene quantification data, and CFU counts. All data analysis, statistical tests, and generated figures are contained in the relevant .R script. Refer to README files for data tables as well as the README section at the header of the R script. The dataset has been updated from version 1 to include manuscript revisions, and updated calculations and figures. </p> <p> </p>
Data from: Strain identity effects contribute more to Pseudomonas community functioning than strain interactions
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Genomic and phenotypic signatures of bacteriophage coevolution with the phytopathogen Pseudomonas syringae
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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>
Data from: Plant pathogenic bacterium Ralstonia solanacearum can rapidly evolve tolerance to antimicrobials produced by Pseudomonas biocontrol bacteria
<p>Soil-borne plant pathogens significantly threaten crop production due to lack of effective control methods. One alternative to traditional agrochemicals is microbial biocontrol, where pathogen growth is suppressed by naturally occurring bacteria that produce antimicrobial chemicals. However, it is still unclear if pathogenic bacteria can evolve tolerance to biocontrol antimicrobials and if this could constrain the long-term efficacy of biocontrol strategies. Here we used an <em>in vitro</em> experimental evolution approach to investigate if the phytopathogenic <em>Ralstonia solanacearum </em>bacterium, which causes bacterial wilt disease, can evolve tolerance to antimicrobials produced by <em>Pseudomonas</em> bacteria. We further asked if tolerance was specific to pairs of <em>R. solanacearum</em> and <em>Pseudomonas</em> strain and certain antimicrobial compounds produced by <em>Pseudomonas</em>. We found that while all <em>R. solanacearum</em> strains could initially be inhibited by <em>Pseudomonas</em> strains, this inhibition decreased following successive subculturing with or without <em>Pseudomonas</em> supernatants. Using separate tolerance assays, we show that the majority of <em>R. solanacearum </em>strains evolved increased tolerance to multiple <em>Pseudomonas</em> strains. Mechanistically, evolved tolerance was most likely linked to reduced susceptibility to orfamide lipopeptide antimicrobials secreted by <em>Pseudomonas</em> strains in our experimental conditions. Some levels of tolerance also evolved in the control treatments, which was likely correlated response due to adaptations to the culture media. Together, these results suggest that plant-pathogenic bacteria can rapidly evolve increased tolerance to bacterial antimicrobial compounds, which could reduce the long-term efficacy of microbial biocontrol.</p>
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Allen Brain Atlas
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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.