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340 results for “plasmid”
Estimating the transfer rates of bacterial plasmids with an adapted Luria–Delbrück fluctuation analysis
<p>This repository accompanies the publication of <strong><em>Estimating the rate of plasmid transfer with an adapted Luria–Delbrück fluctuation analysis</em></strong> by Kosterlitz et. al. to provide a consolidated and accessible way of viewing the source data, figures, and custom programs.</p>
Data from: Polymicrobial Lysis-Hi-C experiments of three bacteria and plasmids derived from HiC-Pro
<p>Microbes interact in biofilms and polymicrobial infections in a spatially structured manner, yet next-generation sequencing approaches generally fail to recover <em>in situ</em> spatial proximity between distinct genotypes in the interactome or eDNA matrix. This study explored a modified Hi-C approach involving an initial lysis phase prior to DNA cross-linking, to test whether adjacent cell chromatin can be cross-linked, anticipating that this could provide a new avenue for the study of spatial-mutational dynamics in structured microbial communities. An artificial polymicrobial mixture of <em>Pseudomonas aeruginosa</em>, <em>Staphylococcus aureus</em>, and <em>Escherichia coli</em> was lysed for 1 to 18 hours, then prepared for Hi-C. A murine biofilm infection model was treated with sonication, mechanical lysis, or chemical lysis before Hi-C. Bioinformatic analyses of resulting Hi-C interspecies chromatin links showed that while microbial species differed from one another, generally lysis significantly increased links between species and increased the distance of Hi-C links within species, while also increasing novel plasmid-chromosome links. The success of this modified lysis-Hi-C protocol in creating extracellular DNA links is a promising first step toward a new lysis-Hi-C-based method to recover genotypic microgeography in polymicrobial communities, with potential future applications in diseases with localized resistance, such as cystic fibrosis lung infections and chronic diabetic ulcers.</p>
Reference plasmids for KleTy and PlasT
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Plasmid sequences for the paper "A FRET based biosensor for measuring Gα13 activation in single cells"
<p>Plasmid sequences for the paper "A FRET based biosensor for measuring Gα13 activation in single cells"</p>
Construction of mutator plasmid, transformation and complementation
New Insight:To get successful complementation and expression of your proteinof interest in H. pylori, it is necessary to grow single colony from the transformants and propagate it to grow pure cultures. Following growth of pure culture expression of complemented protein could be screened. Growing mixed population of H. pylori after transformation may lead to unsuccessful expression of the protein of interest. This may be due to the reason that non expressing bacterial colonies growth over could dominate the actual expressing colonies.
Pathogenic potential and the role of clones and plasmids in beta-lactamase-producing E. coli from chicken faeces in Vietnam
<p>Raw data used to present figures and tables in the manuscript.</p>
States of genome assembly supporting data for complete genome assembly of clinical multidrug resistant Bacteroides fragilis isolates enables comprehensive identification of antimicrobial resistance genes and plasmids.
<p>Assemblies for each isolate and assembly stage is in .gfa and .fasta format.</p> <p>the best SPAdes assembly is also included in the .zip files.</p> <p>1) Unicycler with illumina data and Nanopore data from the first sequencing run, filtered with FiltLong.<br> 2) Unicycler with illumina data and Nanopore data from the first sequencing run, filtered with FiltLong and error corrected with Canu<br> 3) Unicycler with illumina data and Nanopore data from the first and second sequencing run, filtered with FiltLong.<br> 4) manual finshing of assembly 3. <br> Methods are described in the paper and at the github repository (https://github.com/thsyd/bfassembly)</p> <p> </p>
Plasmid sequences for: Recombinant venom proteins in insect seminal fluid reduces female lifespan
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SkewDB: A comprehensive database of GC and 10 other skews for over 28,000 chromosomes and plasmids
<p>GC skew denotes the relative excess of G nucleotides over C nucleotides on the leading versus the lagging replication strand of eubacteria. While the effect is small, typically around 2.5%, it is robust and pervasive. GC skew and the analogous TA skew are a localized deviation from Chargaff's second parity rule, which states that G and C, and T and A occur with (mostly) equal frequency even within a strand.</p> <p>Most bacteria also show the analogous TA skew. Different phyla show different kinds of skew and differing relations between TA and GC skew.<br> This article introduces an open access database (https://skewdb.org) of GC and 10 other skews for over 28,000 chromosomes and plasmids. Further details like codon bias, strand bias, strand lengths and taxonomic data are also included.</p> <p>The SkewDB database can be used to generate or verify hypotheses. Since the origins of both the second parity rule, as well as GC skew itself, are not yet satisfactorily explained, such a database may enhance our understanding of microbial DNA.</p>
Data files 2: A plasmid-based E. coli gene expression system with cell-to-cell variation below the extrinsic noise limit
<p>This zip archive contains flow cytometry data used to generate the figures in the submitted manuscript "A plasmid-based Escherichia coli gene expression system with cell-to-cell variation below the extrinsic noise limit" (PONE-D-17-11810). Text files within the archive describe how to open files and which data corresponds to manuscript figures.</p>
Spatial structure and benefits to hosts allow plasmids with and without post-segregational killing (PSK) systems to coexist
<p>The code for the numerical analysis and parametric walks for both the single strain and specialization model for the paper "Spatial structure and benefits to hosts allow plasmids with and without post-segregational killing (PSK) systems to coexist "</p> <p>Programs Need: Python, Jyupter notebooks and WinZip (to unzip the package).</p> <p>The Data files: The Data files are in .OUT meaning they can be overwritten if the code is run again, so if one wishes to examine the outputs, copy them into another folder or create a second copy of this folder to investigae</p> <p> </p> <p>Numerical Analysis</p> <p>The numerical analysis was conducted using the ode() function and ’lsoda’ method in Scientific Python (vers. 0.19.0). The ’lsoda’ method was used because both the single-strain model and specialization model are numerically ’stiff’. Parameters for ’lsoda’ were set to their default values, except for runs that characterized all outcomes of the model (both coexistence and exclusion); here, numerical tolerance parameters atol and was lowered to 10−9. The default tolerance values (which are both equal to 10−5) were used for runs that specifically sought coexistence of PSK+ and PSK- plasmids. These larger tolerances may miss some points of coexistence, but allow for faster numerical simulation, which was necessary given that a large number of parameter sets needed to be studied to find points of coexistence. In all cases numerical solutions were checked that they successfully completed the full time interval, which in our case was 109 time steps. Numerical analysis also used the numpy library (vers. 1.12.1) and the Python environment was vers. 3.6.1. Python scripts of the numerical systems are provided as further Supplementary Materials, as well as Juypter Notebooks that allow for the examination of single parameter sets.</p> <p> </p> <p>Parameteric Walks </p> <p>To assess whether points of coexistence of PSK- and PSK+ are mutationally connected, we took one point of coexistence for each of the single-strain and specialization models and subjected them to a parameter walk. A parameter walk consist of starting at a point of coexistence and running the simulations again with parameters randomly perturbed from their initial value. In particular, the parameter walk was either unbiased or biased. For an unbiased walk, a parameter was perturbed uniformly to up to 10% above or 10% below its current value. For a biased walk, a parameter was uniformly perturbed 1% above and 10% below, or 10% above and 1% below its current value. If a random perturbation of parameter resulted in a new coexistence point, that set of parameters was taken as the current set and perturbed again.</p>
Data for: Host-specific plasmid evolution explains the variable spread of clinical antibiotic-resistance plasmids
<p><span>Antibiotic resistance encoded on plasmids is a pressing global health problem. Predicting which plasmids spread in the long term remains very challenging, even though some key parameters influencing plasmid stability have been identified, such as plasmid growth costs and horizontal transfer rates. Here, we show these parameters evolve in a strain-specific way among clinical plasmids and bacteria, and this occurs rapidly enough to alter the relative likelihoods of different bacterium-plasmid combinations spreading. We used experiments with <em>Escherichia</em> <em>coli</em> and antibiotic-resistance plasmids isolated from patients, paired with a mathematical model, to track long-term plasmid stability (beyond antibiotic exposure). Explaining variable stability across six bacterium-plasmid combinations required accounting for evolutionary changes in plasmid-stability traits, whereas initial variation of these parameters </span><span>was a relatively poor predictor of long-term outcomes</span><span>. Evolutionary trajectories were specific to particular bacterium-plasmid combinations, as evidenced by genome sequencing and genetic manipulation. This revealed epistatic (here, strain-dependent) effects of key genetic changes affecting horizontal plasmid transfer. Several genetic changes involved mobile elements and pathogenicity islands. Rapid strain-specific evolution can thus outweigh ancestral phenotypes as a predictor of plasmid stability. Accounting for strain-specific plasmid evolution in natural populations could improve our ability to anticipate and manage successful bacterium-plasmid combinations.</span></p>
Fig. 2 in Agrocinopine C, a Ti-plasmid-coded enzyme-product, is a 2-O, 6-O linked phosphodiester of D-Glucose and sucrose
Fig. 2. Diffuse reflectance mid infra-red spectrum of agrocinopine C. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4 in Agrocinopine C, a Ti-plasmid-coded enzyme-product, is a 2-O, 6-O linked phosphodiester of D-Glucose and sucrose
Fig. 4. Bioassay detection of agrocinopine C. The left plate shows detection of agrocinopine C in the pH 1.7, anionic paper electrophoretogram segment marked C, by its induction of antibiotic activity in the normally insensitive K478 (syn. A281) overlay, from a source of agrocin 84 produced by the chloroformsterilised, colony of R. rhizogenes strain K84 grown at the centre of both plates. The radius of the normal toxic concentration range of agrocin 84 extends approximately to the location of the paper squares. The right plate demonstrates that glucose 2-phosphate (square A) induces no detectable sensitivity to agrocin 84, glucose 6-phosphate (square B) no detectable sensitivity and purified agrocinopine C (C) induces a very strong uptake of the antibiotic. It is noteworthy that unlike agrocinopine A, which extends the inhibition zone perimeter (Ellis and Murphy, 1981; Ryder et al., 1984) beyond the squares, agrocinopine C only induces sensitivity inwards towards the original agrocin 84 producer colony.
Fig. 3 in Agrocinopine C, a Ti-plasmid-coded enzyme-product, is a 2-O, 6-O linked phosphodiester of D-Glucose and sucrose
Fig. 3. Relative electrophoretic mobilities (RmDNBS) of ribose-5-P (triangle; pKa1 =1.32, pKa2 =6.58), agrocinopine C (circle) and agrocinopine D (square) between pH 1.7 and 10.
Fig. 5 in Agrocinopine C, a Ti-plasmid-coded enzyme-product, is a 2-O, 6-O linked phosphodiester of D-Glucose and sucrose
Fig. 5. (a) 31P NMR signals for agrocinopine C, α/β pyranosyl anomers (-2.80 ppm), α/β furanosyl anomers (-3.24 ppm) and the aldehyde anomer and/or its hydrate (-4.03 ppm). No evidence for a detectable amount of the hydrated aldehyde as a separate 6th signal was observed in these 31P NMR observations. Peaks at -3.09 and -3.12 ppm are not correlated to any agrocinopine C protons by 31P gHMBC. (b) Agrocinopine C31P NMR signals collapse to a singlet after borohydride reduction (-1.55 ppm).
Fig. 6. Structural relationships for agrocinopine C in Agrocinopine C, a Ti-plasmid-coded enzyme-product, is a 2-O, 6-O linked phosphodiester of D-Glucose and sucrose
Fig. 6. Structural relationships for agrocinopine C and its various anomeric equilibria. The presence of the aldehyde anomer and/or its hydrate and α/β pyranosyl and α/β furanosyl anomers is consistent with the three signals detected in the 31P NMR and five anomeric proton signals observed in the 1H NMR (Table 2, Table S1). (ChemBioDraw, 2014).
A Safety and Immunogenicity Study of a Plasmid DNA Prime and MVA Boost Vaccine in HIV-1 Infected Adults on ART
ClinicalTrials.gov study NCT01378156. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Study of HGF Via Plasmid Vector to Improve Perfusion in Critical Limb Ischemia
ClinicalTrials.gov study NCT00060892. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Phase I Study on Evaluating the Safety, Tolerability, Pharmacokinetic Characteristics and Preliminary Efficacy of SXRN Plasmid DNA Technique in Patients With Advanced Solid Tumors
ClinicalTrials.gov study NCT06736275. IPD Sharing: YES. Countries: 1. Publications: 10.
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International Brain Laboratory public data
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