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1,049 results for “Pseudomonas”
Antibiotic resistance alters the ability of Pseudomonas aeruginosa to invade the respiratory microbiome
<p>Sequencing data for spontaneous resistant mutants of <em>Pseudomonas aeruginosa </em>PAO1-GFP generated for the preprinted project "Antibiotic resistance alters the ability of Pseudomonas aeruginosa to invade the respiratory microbiome". A total of nine resistant mutants selected on the clinical breakpoint concentrations for meropenem, ciprofloxacin and ceftazidime were sequenced, along with the ancestral GFP-tagged PAO1 background strain (PAO1-GFP). Libraries were sequenced using the Illumina NovaSeq6000 platform using a 250bp paired-end protocol (via microbesNG), submitted for x60 depth sequencing.<br>Preprint for this project found here: https://www.biorxiv.org/content/10.1101/2023.11.14.567137v1</p>
The metabolomics raw data and a supporting statistical analyses data set for publication: Metabolomic analysis revealed the absence of the principal antimicrobial compound of Pseudomonas donghuensis P482, 7-hydroxytropolone, under restricted nutrient conditions.
<p><a href="../api/records/11220997/draft/files/Metabolomic%20analyses%20raw%20files.zip/content" target="_blank" rel="noopener noreferrer">Metabolomic analyses raw files</a>, Compounds analyses, Hierarchical Condition tress and PCA Scores are uploaded.</p>
Aztreonam-induced filamentation in Pseudomonas aeruginosa
<p><strong>Objectives:</strong> The proliferation of metallo-beta-lactamase-producing <em>Pseudomonas aeruginosa</em> represents a significant public health threat. <em>P. aeruginosa</em> can undergo significant phenotypic changes that can drastically impair antibiotic efficacy. This study's objectives were (1) to quantify the time course of killing of VIM-2-producing <em>P. aeruginosa</em> in response to aztreonam-based therapies, and (2) to document the capacity of <em>P. aeruginosa</em> to undergo morphological transformations that facilitate persistence.</p> <p><strong>Methods: </strong>A well-characterized, clinical VIM-2-producing <em>P. aeruginosa </em>was studied in the Hollow Fiber Infection Model (HFIM) over<em> </em>9 days (7 days of active antibiotic therapy, 2 days treatment withdrawal) at a 10<sup>7.5</sup> CFU/mL starting inoculum. HFIM treatment arms included: growth control, aztreonam, ceftazidime/avibactam, aztreonam/ceftazidime/avibactam, polymyxin B, and aztreonam/ceftazidime/avibactam/polymyxin B. In addition, real-time imaging studies were conducted under static conditions to determine the time course of the reversion of persister cells. </p> <p><strong>Results:</strong> A pronounced discrepancy was observed between OD<sub>620 </sub>and bacterial counts obtained from plating methods (hereafter referred to as '<em>OD-count discrepancy</em>'). For aztreonam monotherapy, observed counts were 0 CFU/mL by 120 h. Despite this, there was a significant OD-count discrepancy as compared to the pre-treatment 0h. Between therapy withdrawal at 168h and 216h, all arms with suppressed counts had re-grown to the system carrying capacity. Real-time imaging of the <em>P. aeruginosa</em> filaments after drug removal showed rapid reversion from a long, filamentous phenotype to many individual rods within 2 h.</p> <p><strong>Conclusion:</strong> Managing MBL-producing <em>P. aeruginosa</em> will require a multi-faceted approach, focused on maximizing killing and minimizing proliferation of resistant and persistent subpopulations, which will involve eliminating drug-induced phenotypic transformers.</p>
Figure 10 in Pseudomonas aeruginosa associated pulmonary infections and in vitro amplification virulent rhamnolipid (rhlR) gene
Figure 10. Bands glowing closer to the ladder band with 750 bp are representing PCR products.
Figure 2. Showing P in Pseudomonas aeruginosa associated pulmonary infections and in vitro amplification virulent rhamnolipid (rhlR) gene
Figure 2. Showing P. aeruginosa Growth on Citrimide Agar Plates.
Figure 5 in Pseudomonas aeruginosa associated pulmonary infections and in vitro amplification virulent rhamnolipid (rhlR) gene
Figure 5. Shows production of Pyocyanin (Left) and Pyoverdin (Right) pigments in LB broth
Figure 3 in Pseudomonas aeruginosa associated pulmonary infections and in vitro amplification virulent rhamnolipid (rhlR) gene
Figure 3. Showing β-Hemolysis by P. aeruginosa in Blood Agar Test.
Figure 7 in Pseudomonas aeruginosa associated pulmonary infections and in vitro amplification virulent rhamnolipid (rhlR) gene
Figure 7. Resistance of P. aeruginosa having no inhibition zone around antibiotic disc
Attempted Synthesis of the Pseudomonas aeruginosa Metabolite 2-Benzyl-4(1H)-quinolone and Formation of 3-Methylamino-2-(2-nitrobenzoyl)-4H-naphthalen-1-one as an Unexpected Product - NMR Data
<p>This archive contains raw 1H/13C FIDs and associated data in Bruker-specific format that can be viewed with Bruker’s TopSpin or other appropriate NMR processing software. The subfolders are named in accordance with the compound numbering in the associated research paper (Attempted Synthesis of the Pseudomonas aeruginosa Metabolite 2-Benzyl-4(1H)-quinolone and Formation of 3-Methylamino-2-(2-nitrobenzoyl)-4H-naphthalen-1-one as an Unexpected Product).</p> <p>Correspondence: angelov@uni-plovdiv.bg</p> <p> </p>
PlzR regulates type IV pili assembly in Pseudomonas aeruginosa via PilZ binding - ColabFold models
<p>This dataset contains the ColabFold models from the article 'PlzR regulates type IV pili assembly in <em>Pseudomonas aeruginosa</em> via PilZ binding' by Hendrix, H. <em>et al</em> (Nat. Comm., doi: <span><a href="https://doi.org/10.1038/s41467-024-52732-5" target="_blank" rel="noopener">10.1038/s41467-024-52732-5</a></span>).</p> <p>The dataset consists of two zips (a_PlzR_PilZ_models.zip, b_PlzR_PilZ_PilB_models.zip), which contain the direct output of the different ColabFold runs for the PlzR:PilZ protein complex, and the PlzR:PilZ:PilB protein complex, respectively. </p> <p>All models were predicted using ColabFold's AlphaFold2_mmseqs2 v1.2 notebook. For PlzR:PilZ models, models were generated with relaxation and templates enabled and 3, 6 and 48 recycles (folders PA2560_PilZ_auto_rec3_AMBER_templ_4e025.result, PA2560_PilZ_auto_rec6_AMBER_templ_4e025.result and PA2560_PilZ_auto_rec48_AMBER_templ_4e025.result, respectively). For PlzR:PilZ:PilB models, models were generated with templates enabled and 3 recycles (folder PA2560_PilZ_PilB_auto_rec3_templ_6ea29.result). For PlzR:PilZ:PilB models with only the first 200 amino acids of PilB modeled, models were generated with relaxation and templates enabled and 3 and 6 recycles (folders PA2560_PilZ_PilB200_auto_rec3_AMBER_templ_cd74d.result and PA2560_PilZ_PilB200_auto_rec6_AMBER_templ_cd74d.result, respectively).</p>
Interaction of N-3-oxododecanoyl homoserine lactone with transcriptional regulator LasR of Pseudomonas aeruginosa: Insights from molecular docking and dynamics simulations
<p>Dataset and supplementary files of the research: Interaction of N-3-oxododecanoyl homoserine lactone with transcriptional regulator LasR of Pseudomonas aeruginosa: Insights from molecular docking and dynamics simulations (https://doi.org/10.1101/121681)</p> <p>- Supporting Information</p> <p>- Input: Parameters and initial structures</p> <p>- Output: Trajectories, Docking poses</p> <p>Gromacs (multi-core with CUDA) was used for the simulations.</p> <p>Autodock Vina, FlexAid and rDock were used for molecular docking.</p>
The Pseudomonas aeruginosa T6SS delivers a periplasmic toxin that disrupts bacterial cell morphology - 6H56
<p>VgrG2b X-ray diffraction data for PDB entry 6H56.</p> <p>=> VgrG2b_p3: Native protein, one dataset</p> <p>=> VgrG2b_SeMet: Se-Met protein, four datasets</p> <p>Data were collected at beamline I02 of Diamond Light Source on 24 Jun 2013 (VgrG2b_p3) and at beamline I04 of Diamond Light Source on 17 Apr 2014 (VgrG2b_SeMet).</p>
Uncovering the structure and function of Pseudomonas aeruginosa periplasmic proteins by an in silico approach
<p><strong>Caprari_et_al_models_and_docking: </strong>Predicted three-dimensional structures and docking simulations results described in the manuscript "Uncovering the structure and function of Pseudomonas aeruginosa periplasmic proteins by an in silico approach" by Caprari et al. More informations are given in the "README.txt" file.</p>
Experimental data for Pseudomonas aeruginosa from experimental evolution under different bottleneck sizes and antibiotic selection pressures
<p>We here combined evolution experiments with genomic and genetic analyses to assess whether bottleneck size and antibiotic-induced selection influences the evolutionary path to resistance in pathogenic<i> Pseudomonas aeruginosa</i>, one of the most problematic opportunistic human pathogens. Two sets of evolution experiments were performed across 16 transfers, in which either the aminoglycoside gentamicin or the fluoroquinolone ciprofloxacin were used as antibiotic. The evolutionary response was studied using counts of bacterial cells at the end of each transfer period (i.e., yield) or by calculating the growth rate from regular optical density measurement during the respective transfer periods. For the populations at the end of the evolution experiments, we also determined their antibiotic resistance with the help of standardized dose response curves. Moreover, we performed whole genome sequencing to assess the frequency of variants, which emerged and spread during the evolution experiments. We further focused on variants in two specific genes, which were selectively favoured in the gentamicin evolution experiments, and assessed their relative fitness using competition experiments. We found that antibiotic resistance is favoured under high antibiotic selection and weak bottlenecks, but also under low antibiotic selection and severe bottlenecks. We found that the absence of high resistance under low selection and weak bottlenecks is caused by the spread of low-resistance variants with high competitive fitness under these conditions. We conclude that bottlenecks in combination with drug-induced selection are currently neglected key determinants of pathogen evolution and antibiotic treatment outcome.</p>
Ecology drives the evolution of diverse social strategies in Pseudomonas aeruginosa
<p><span>Bacteria often cooperate by secreting molecules that can be shared as public goods between cells. Because the production of public goods is subject to cheating by mutants that exploit the good without contributing to it, there has been great interest in elucidating the evolutionary forces that maintain cooperation. However, little is known on how bacterial cooperation evolves under conditions where cheating is unlikely of importance. Here we use experimental evolution to follow changes in the production of a model public good, the iron-scavenging siderophore pyoverdine, of the bacterium <i>Pseudomonas aeruginosa</i>. After 1200 generations of evolution in nine different environments, we observed that cheaters only reached high frequency in liquid medium with low iron availability. Conversely, when adding iron to reduce the cost of producing pyoverdine, we observed selection for pyoverdine hyper-producers. Similarly, hyper-producers also spread in populations evolved in highly viscous media, where relatedness between interacting individuals is increased. Whole-genome sequencing of evolved clones revealed that hyper-production is associated with mutations involving genes encoding quorum-sensing communication systems, while cheater clones had mutations in the iron-starvation sigma factor or in pyoverdine biosynthesis genes. Our findings demonstrate that bacterial social traits can evolve rapidly in divergent directions, with particularly strong selection for increased levels of cooperation occurring in environments where individual dispersal is reduced, as predicted by social evolution theory. Moreover, we establish a regulatory link between pyoverdine production and quorum-sensing, showing that increased cooperation with respect to one trait (pyoverdine) can be associated with the loss (quorum-sensing) of another social trait.Bacteria often cooperate by secreting molecules that can be shared as public goods between cells. Because the production of public goods is subject to cheating by mutants that exploit the good without contributing to it, there has been great interest in elucidating the evolutionary forces that maintain cooperation. However, little is known on how bacterial cooperation evolves under conditions where cheating is unlikely of importance. Here we use experimental evolution to follow changes in the production of a model public good, the iron-scavenging siderophore pyoverdine, of the bacterium <i>Pseudomonas aeruginosa</i>. After 1200 generations of evolution in nine different environments, we observed that cheaters only reached high frequency in liquid medium with low iron availability. Conversely, when adding iron to reduce the cost of producing pyoverdine, we observed selection for pyoverdine hyper-producers. Similarly, hyper-producers also spread in populations evolved in highly viscous media, where relatedness between interacting individuals is increased. Whole-genome sequencing of evolved clones revealed that hyper-production is associated with mutations involving genes encoding quorum-sensing communication systems, while cheater clones had mutations in the iron-starvation sigma factor or in pyoverdine biosynthesis genes. Our findings demonstrate that bacterial social traits can evolve rapidly in divergent directions, with particularly strong selection for increased levels of cooperation occurring in environments where individual dispersal is reduced, as predicted by social evolution theory. Moreover, we establish a regulatory link between pyoverdine production and quorum-sensing, showing that increased cooperation with respect to one trait (pyoverdine) can be associated with the loss (quorum-sensing) of another social trait.</span></p>
EvoMining genomic and enzyme databases for Actinobacteria, Cyanobacteria, Pseudomonas and Archaea
<p>Databases for EvoMining 2.0</p> <p>Genomic DB is a collection of genomes of a certain taxonomical group, functionally annotated by RAST.</p> <p>Enzyme-DB</p> <p>Actinobacteria</p> <p>Cyanobacteria</p> <p>Pseudomonas</p> <p>Archaea</p> <p>SampleData</p>
Dataset: Live cell dynamics of production, explosive release and killing activity of phage tail-like weapons for Pseudomonas kin exclusion
<p>This dataset is related to "Live cell dynamics of production, explosive release and killing activity of phage tail-like weapons for Pseudomonas kin exclusion" and contains the raw data obtained.</p>
Figure 18: Theoretical Prediction of Meropenem Resistance in Pseudomonas aeruginosa (2020-2030)
<p><strong>Figure 18: Theoretical Prediction of Meropenem Resistance in Pseudomonas aeruginosa (2020-2030) </strong></p>
Data of "High-speed cryo-microscopy proves that ice-nucleating proteins of Pseudomonas syringae trigger freezing at hydrophobic interfaces"
<p>Raw data of a study titled <strong><em>"High-speed cryo-microscopy proves that ice-nucleating proteins of Pseudomonas syringae trigger freezing at hydrophobic interfaces"</em></strong>.</p> <p>The <strong>onset_locations.zip</strong> folder contains all analyzed images which are screenshots from the cryo-microscopic videos. Raw screenshots and evaluated images are included in two sub-folders per samples. The sample description is the name of the folders.</p> <p>The <strong>ice_propogation_velocity.zip</strong> folder contains all images that were used for the evaluation of the propagation velocity of ice. Every sample folder contains the original spot detection image, one image at a later time point, the subtracted image, and one image with the measured distance of the ice front indicated as white scale bar.</p> <p>The <strong>Results_(ice_propagation_velocity).xlsx</strong> contains the results from the velocity calculations, the <strong>Results_(surface tension).xlsx</strong> contains the evaluated surface tension values and the <strong>Results_(temperatures and locations).xlsx</strong> file contains all evaluated freezing locations (polar coordinates) and temperatures of all analyzed samples.</p>
Data files and taxonomic classifiers for Pseudomonas syringae classification and virulence factor prediction
<p>PSSC.tree : core-genome tree of 2,161 high quality <em>Pseudomonas syringae</em> genomes</p> <p>metadata.csv: A CSV file containing taxonomic data, type strain designations, phylogroups as assigned in this study, LIN clusters assigned for classification purposes, presence/absence of key virulence factors, and metadata found in each genome’s Biosample record for all genomes found in PSSC.tree</p> <p>CLASSIFIER_xxx: QIIME 2 classifier artifacts trained on amplicons generated from primer sets indicated in file name</p> <p>xxx_VFOC.JSON: HMMER results for T3SS and effectors and WHOP genes, structured with both genome and gene product accession numbers as primary key, depending on file</p> <p> </p> <p> </p>
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