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1,049 results for “Pseudomonas”
Draft Genome Sequences of Pseudomonas sp. Strain MWU12-2233, Isolated from a Wild Cranberry Bog in Provincetown, Massachusetts
<p>Annotated genome of Pseudomonas sp. MWU12-2233.</p>
Draft Genome Sequences of Pseudomonas sp strain MWU-15.20650
<p>Annotated genome of Pseudomonas sp. MWU 15-20650.</p>
Draft Genome Sequence of Pseudomonas sp. strain MWU13.2105, isolated from Wild Cranberry Bog in Truro, Massachusetts
<p>Annotated genome of Pseudomonas sp. strain MWU13.2105.</p>
Draft Genome Sequence of Pseudomonas sp. strain MWU13.2100, isolated from Wild Cranberry Bog in Truro, Massachusetts
<p>Annotated genome of Pseudomonas sp. strain MWU13.2100.</p>
Pseudomonas MWU 12.2029
<p>Pseudomonas MWU 12.2029 wild cranberry bogs isolated from Cape Cod, National Seashore</p>
Draft Genome Manuscript for Pseudomonas sp. Strain MWU13.3659 Isolated from Berries Surfaced in Commercial Cranberry Bogs in Massachusetts, USA
<p>Annotated genome of Pseudomonas sp. MWU13.3659</p>
Structural insights into the inhibition site in the phosphorylcholine phosphatase enzyme of Pseudomonas aeruginosa
<p>Inputs for the simulations (MDs and FEP), plus scripts used to obtain results of the manuscript entitled "Structural insights into the inhibition site in the phosphorylcholine phosphatase enzyme of Pseudomonas aeruginosa"</p>
The effects of antibiotic combination treatments on Pseudomonas aeruginosa tolerance evolution and coexistence with Stenotrophomonas maltophilia
<p><em>Pseudomonas aeruginosa</em> bacterium is a common pathogen of Cystic Fibrosis (CF) patients due to its ability to evolve resistance to antibiotics during treatments. While <em>P. aeruginosa</em> resistance evolution is well characterised in monocultures, it is less well understood in polymicrobial CF infections. Here, we investigated how exposure to ciprofloxacin, colistin, or tobramycin antibiotics, administered at sub-MIC doses alone and in combination, shaped the tolerance evolution of <em>P. aeruginosa</em> (PAO1 lab and clinical CF LESB58 strains) in the absence and presence of a commonly co-occurring species, <em>Stenotrophomonas maltophilia</em>. Increases in antibiotic tolerances were primarily driven by the presence of that antibiotic in the treatment. We observed a reciprocal cross-tolerance between ciprofloxacin and tobramycin, and when combined these antibiotics selected increased MICs for all antibiotics. Though the presence of <em>S. maltophilia</em> did not affect the tolerance or the MIC evolution, it drove <em>P. aeruginosa</em> into extinction more frequently in the presence of tobramycin due to its relatively greater innate tobramycin tolerance. In contrast, <em>P. aeruginosa</em> dominated and drove <em>S. maltophilia</em> extinct in most other treatments. Together, our findings suggest that besides driving high-level antibiotic tolerance evolution, sub-MIC antibiotic exposure can alter competitive bacterial interactions, leading to target pathogen extinctions in multi-species communities.</p>
Arms-race and fluctuating-selection dynamics in Pseudomonas aeruginosa bacteria coevolving with phage OMKO1
<p class="MsoNormal">Experimental evolution studies have examined coevolutionary dynamics between bacteria and lytic phages, where two models for antagonistic coevolution dominate: arms-race dynamics (ARD) and fluctuating-selection dynamics (FSD). Here, we tested the ability for <em>Pseudomonas aeruginosa </em>to coevolve with phage OMKO1 during 10 passages in the laboratory; whether ARD versus FSD coevolution occurred; and how coevolution affected a predicted phenotypic trade-off between phage resistance and antibiotic sensitivity. We used a unique "deep" sampling design, where 96 bacterial clones per passage were obtained from the three replicate coevolving communities. Next, we examined phenotypic changes in growth ability, susceptibility to phage attack, and resistance against antibiotics. Results confirmed that the bacteria and phages coexisted throughout the study with one community undergoing ARD while the other two showed evidence for FSD. Surprisingly, only the ARD bacteria demonstrated the anticipated trade-off. Whole genome sequencing revealed that treatment populations of bacteria accrued more <em>de novo</em> mutations, relative to a control bacterial population. Additionally, coevolved bacteria presented mutations in genes for biosynthesis of flagella, type-IV pilus and lipopolysaccharide, with three mutations fixing contemporaneously with the occurrence of the phenotypic trade-off in the ARD-coevolved bacteria. Our study demonstrates that both ARD and FSD coevolution outcomes are possible in a single interacting bacteria-phage system, and that occurrence of predicted phage-driven evolutionary trade-offs may depend on the genetics underlying evolution of phage-resistance in bacteria. These results are relevant for the ongoing development of lytic phages, such as OMKO1, in personalized treatment of human patients, as an alternative to antibiotics. </p>
Complete dataset for the publication "Phage Paride can kill dormant, antibiotic-tolerant cells of Pseudomonas aeruginosa by direct lytic replication"
<p>This dataset enlists all individual datapoints shown in the publication "Phage Paride can kill dormant, antibiotic-tolerant cells of Pseudomonas aeruginosa by direct lytic replication" (https://doi.org/10.1038/s41467-023-44157-3). For detailed information regarding the individual contributions please check the "Author information" section in the publication. </p>
Movies of Pseudomonas aeruginosa twitching
<p><strong><span>Movie S1. </span></strong><span>Bacteria twitching in the confined condition. It can be clearly seen that some of the bacteria exhibit obvious twitching movements while others are stationary. The playback speed of the video is 10 times the actual speed.</span></p> <p><strong><span>Movie S2. </span></strong><span>Bacteria twitching in the free condition. The playback speed of the video is 10 times the actual speed.</span></p>
MD simulation data for Pseudomonas aeruginosa TonB-CTD, Amber ff99SB-ILDN, tip4p, 298K, Gromacs
<p>MD simulation data for Pseudomonas aeruginosa TonB-CTD. Simulated with Amber ff99SB-ILDN force field, tip4p water model at 298K, Gromacs software package.</p> <p>Simulation reported in "Rotational dynamics of proteins from spin relaxation times and molecular dynamics simulations", Ollila et al. Submitted (2017).</p>
MD simulation data for Pseudomonas aeruginosa TonB-CTD, Amber ff99SB-ILDN, OPC4, 310K, Gromacs
<p>MD simulation data for Pseudomonas aeruginosa TonB-CTD. Simulated with Amber ff99SB-ILDN force field, OPC4 water model at 310K, Gromacs software package.</p> <p>Simulation reported in "Rotational dynamics of proteins from spin relaxation times and molecular dynamics simulations", Ollila et al. Submitted (2017).</p>
MD simulation data for Pseudomonas aeruginosa TonB-CTD, Amber ff99SB-ILDN, tip4p, 310K, Gromacs
<p>MD simulation data for Pseudomonas aeruginosa TonB-CTD. Simulated with Amber ff99SB-ILDN force field, tip4p water model at 310K, Gromacs software package.</p> <p>Simulation reported in "Rotational dynamics of proteins from spin relaxation times and molecular dynamics simulations", Ollila et al. Submitted (2017).</p>
MD simulation data for Pseudomonas aeruginosa TonB-CTD, Amber ff99SB-ILDN, tip3p, 298K, Gromacs
<p>MD simulation data for Pseudomonas aeruginosa TonB-CTD. Simulated with Amber ff99SB-ILDN force field, tip3p water model at 298K, Gromacs software package.</p> <p>Simulation reported in "Rotational dynamics of proteins from spin relaxation times and molecular dynamics simulations", Ollila et al. Submitted (2017).</p>
[PART 3] A twist of fate: the helix-turn-helix motif in Pseudomonas aeruginosa ExsA can allosterically stabilize the ligand-binding domain
<p>1273- ExsA - Dimer1 without DNA - alphaFold compact - 230ns/day 10x500ns<br>*1274- ExsA - Dimer2 without DNA - xtal extended (like 1026) - 10x1µs (not everything is 1 µs)<br>1275- ExsA - Dimer3 without DNA - made by hand / 150ns/day (reps 1-5 broken, reps 6-10 correct)<br>*1276- ExsA- monomer - Site1-PexoT - 240 ns/day - 10x1µs (some replicas have pieces missing)<br>*1277- ExsA- monomer - Site2-PexoT - 10x1µs<br>*1278- ExsA - Dimer2 with broken DNA - xtal extended (like 1026) - 5x200ns?</p>
[PART 2] A twist of fate: the helix-turn-helix motif in Pseudomonas aeruginosa ExsA can allosterically stabilize the ligand-binding domain
<p>1273- ExsA - Dimer1 without DNA - alphaFold compact <br>1274- ExsA - Dimer2 without DNA - xtal extended <br>1275- ExsA - Dimer3 without DNA - made by hand (reps 1-5 broken, reps 6-10 correct)<br>1276- ExsA- monomer - Site1-PexoT <br>1277- ExsA- monomer - Site2-PexoT<br>1278- ExsA - Dimer2 with broken DNA</p>
Data from: Pseudomonas putida and Pseudomonas fluorescens species group recovery from human homes varies seasonally and by environment
By shedding light on variation in time as well as in space, long-term biogeographic studies can help us define organisms' distribution patterns and understand their underlying drivers. Here we examine distributions of Pseudomonas in and around 15 human homes, focusing on the P. putida and P. fluorescens species groups. We describe recovery from 10,941 samples collected during up to 8 visits per home, occurring on average 2.6 times per year. We collected a mean of 141 samples per visit, from sites in most rooms of the house, from the surrounding yards, and from human and pet occupants. We recovered Pseudomonas in 9.7% of samples, with the majority of isolates being from the P. putida and P. fluorescens species groups (approximately 62% and 23% of Pseudomonas samples recovered respectively). Although representatives of both groups were recovered from every season, every house, and every type of environment sampled, recovery was highly variable across houses and samplings. Whereas recovery of P. putida group was higher in summer and fall than in winter and spring, P. fluorescens group isolates were most often recovered in spring. P. putida group recovery from soils was substantially higher than its recovery from all other environment types, while higher P. fluorescens group recovery from soils than from other sites was much less pronounced. Both species groups were recovered from skin and upper respiratory tract samples from healthy humans and pets, although this occurred infrequently. This study indicates that even species that are able to survive under a broad range of conditions can be rare and variable in their distributions in space and in time. For such groups, determining patterns and causes of stochastic and seasonal variability may be more important for understanding the processes driving their biogeography than the identity of the types of environments in which they can be found.
A novel mechanism that maintains outer membrane lipid asymmetry in Pseudomonas aeruginosa
<p>Newick file the PA2800 phylogenetic tree in "A novel mechanism that maintains outer membrane lipid asymmetry in Pseudomonas aeruginosa"</p>
Interactions between metabolism and growth can determine the co-existence of Staphylococcus aureus and Pseudomonas aeruginosa
<p>Most bacteria exist and interact within polymicrobial communities. These interactions produce unique compounds, increase virulence and augment antibiotic resistance. One community associated with negative healthcare outcomes consists of <em>Pseudomonas aeruginosa</em> and <em>Staphylococcus aureus</em>. When co-cultured, virulence factors secreted by <em>P. aeruginosa</em> reduce metabolism and growth in <em>S. aureus</em>. When grown in vitro, this allows <em>P. aeruginosa</em> to drive <em>S. aureus</em> toward extinction. However, when found <em>in vivo</em>, both species can co-exist. Previous work has noted that this may be due to altered gene expression or mutations. However, little is known about how the growth environment could influence the co-existence of both species. Using a combination of mathematical modeling and experimentation, we show that changes to bacterial growth and metabolism caused by differences in the growth environment can determine the final population composition. We found that changing the carbon source in growth media affects the ratio of ATP to growth rate for both species, a metric we call absolute growth. We found that as a growth environment increases the absolute growth for one species, that species will increasingly dominate the co-culture. This is due to interactions between growth, metabolism, and metabolism-altering virulence factors produced by <em>P. aeruginosa</em>. Finally, we show that the relationship between absolute growth and the final population composition can be perturbed by altering the spatial structure in the community. Our results demonstrate that differences in growth environment can account for conflicting observations regarding the co-existence of these bacterial species in the literature, provides support for the intermediate disturbance hypothesis, and may offer a novel mechanism to manipulate polymicrobial populations.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.