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153 results for “bacteriophage”
Dataset: Single nucleotide switches confer bacteriophage resistance to Pseudomonas protegens
<p>Dataset containing : <br>- csv files : Output file of the SNPs identified in all the phage-resistant variants (C2, C4, C17 and C18).</p> <p>- Excel files : </p> <ul> <li>Raw and pre-analyzed data for the bacterial growth analysis. (<a href="https://zenodo.org/api/records/15696172/draft/files/Bacterial_growth.xlsx/content" target="_blank" rel="noopener noreferrer">Bacterial_growth.xlsx</a>)</li> <li>Raw and pre-analised data for the competition assays (Compatition assays.xlsx). </li> <li>Raw and pre-analised data for the fitness assays in planta (plant experiment.xlsx) </li> <li><span lang="EN-US">Raw data of the phage adsorption assay (phage adsorption assays.xlsx) </span></li> </ul> <p>- Code used for the analysis of all the data.</p> <p>- Image and data pre analysed for the spatial distribution of the bacteria during competition in vitro (drop_competition.rar)</p> <ul> <li>Images of the colonies (GFP and red channel) in bmp format</li> <li>R code used to process and analyse this data (Phage_JV_drop.Rmd)</li> </ul> <p> </p>
Expansion of the global RNA virome reveals diverse clades of bacteriophages
<p>This deposit is intended to contain the various data generated as part of the RNA Virus in MetaTranscriptomes project ("RVMT"). This initial version is released ahead of time, near the time of submission, in hopes of providing a long lasting resource for the general scientific community. Note well - The authors listed in this initial version release are a partial list only. The RNA Virus in MetaTranscriptomes consortium is a project with over 90 researches from various institutions (see below).</p> <p>High-throughput RNA sequencing offers broad opportunities to explore the Earth RNA virome. Mining 5,150 diverse metatranscriptomes uncovered >2.5 million RNA virus contigs. Analysis of >330,000 RNA-dependent RNA polymerases (RdRPs) shows that this expansion corresponds to a 5-fold increase of the known RNA virus diversity. Gene content analysis revealed multiple protein domains previously not found in RNA viruses and implicated in virus-host interactions. Extended RdRP phylogeny supports the monophyly of the five established phyla and reveals two putative additional bacteriophage phyla and numerous putative additional classes and orders. The dramatically expanded phylum <em>Lenarviricota</em>, consisting of bacterial and related eukaryotic viruses, now accounts for a third of the RNA virome. Identification of CRISPR spacer matches and bacteriolytic proteins suggests that subsets of picobirnaviruses and partitiviruses, previously associated with eukaryotes, infect prokaryotic hosts.</p> <p>The RNA Virus in metatranscriptomes consortium:<br> Adrienne B. Narrowe, Alexander J. Probst, Alexander Sczyrba, Annegret Kohler, Armand Séguin, Ashley Shade, Barbara J. Campbell, Björn D. Lindahl, Brandi Kiel Reese, Breanna M. Roque, Chris DeRito, Colin Averill, Daniel Cullen, David A. C. Beck, David A. Walsh, David M. Ward, Dongying Wu, Emiley Eloe-Fadrosh, Eoin L. Brodie, Erica B. Young, Erik A. Lilleskov, Federico J. Castillo, Francis M. Martin, Gary R. LeCleir, Graeme T. Attwood, Hinsby Cadillo-Quiroz, Holly M. Simon, Ian Hewson, Igor V. Grigoriev, James M. Tiedje, Janet K. Jansson, Janey Lee, Jean S. VanderGheynst, Jeff Dangl, Jeff S. Bowman, Jeffrey L. Blanchard, Jennifer L. Bowen, Jiangbing Xu, Jillian F. Banfield, Jody W Deming, Joel E. Kostka, John M. Gladden, Josephine Z Rapp, Joshua Sharpe, Katherine D. McMahon, Kathleen K. Treseder, Kay D. Bidle, Kelly C. Wrighton, Kimberlee Thamatrakoln, Klaus Nusslein, Laura K. Meredith, Lucia Ramirez, Marc Buee, Marcel Huntemann, Marina G. Kalyuzhnaya, Mark P Waldrop, Matthew B Sullivan, Matthew O. Schrenk, Matthias Hess, Michael A. Vega, Michelle A. O’Malley, Monica Medina, Naomi E. Gilbert, Nathalie Delherbe, Olivia U. Mason, Paul Dijkstra, Peter F. Chuckran, Petr Baldrian, Philippe Constant, Ramunas Stepanauskas, Rebecca A. Daly, Regina Lamendella, Robert J Gruninger, Robert M. McKay, Samuel Hylander, Sarah L. Lebeis, Sarah P Esser, Silvia G. Acinas, Steven S. Wilhelm, Steven W. Singer, Susannah S. Tringe, Tanja Woyke, TBK Reddy, Terrence H. Bell, Thomas Mock, Tim McAllister, Vera Thiel, Vincent J. Denef, Wen-Tso Liu, Willm Martens-Habbena, Xiao-Jun Allen Liu, Zachary S. Cooper, Zhong Wang. For the full list of authors and related information, please see the spreadsheet tittle "Table S9 - Consortium coauthorship" available in this collection in the folder named "Tables".</p>
Bacteriophage Bxb1 Structure
<p>Mycobacteriophage Bxb1 that infects Mycobacterium smegmatis. It is useful for the study and treatment of tuberculosis. By Victor Padilla Sanchez, PhD. Website: https://www.drvictorpadillasanchez.com</p>
"Centenarians have a diverse population of gut bacteriophages that may promote healthy lifespan" - Genomes and annotation
<p>File-dump associated with the manuscript:</p> <p>"<strong>Centenarians have a diverse population of gut bacteriophages that may promote healthy lifespan" (Not yet published)</strong></p> <p>MGVs refer to the viral genome database in the publication: https://www.nature.com/articles/s41564-021-00928-6 </p> <p> </p> <p>Following uploaded:</p> <p>File 1: VOG Markers in vOTUs/vMAGs and MGV genomes</p> <p>File 2: Viral Tree Newick file with vOTUs/vMAGs and MGV genomes</p> <p>File 3: All vOTUs/vMAGs genomes</p> <p>File 4: Master table annotation of vOTUs/vMAGs</p> <p>File 5: Centenarian bacterial isolate proviruses</p>
Data supporting "Transformer Model Generated Bacteriophage Genomes are Compositionally Distinct from Natural Sequences"
<p>Sequence and composition data supporting doi: <a href="https://doi.org/10.1101/2024.03.19.585716" target="_blank" rel="noopener">10.1101/2024.03.19.585716</a>. Uncompressed file size is ~5.8GB.</p> <p>Data in zip files is organized by sequence provenance (generRNA, natural, or transformer (megaDNA)). Common file types between folders include:</p> <ul> <li>Multi-record fasta file: Sequence data for all sequences of a given provenance. For generRNA sequences, these are found within the `seq` column of file "MFE_distribution_Fig4a.csv"</li> <li>Composition files: Individual sequence level compositional metrics for sliding 120 bp windows. Only structural metrics were used in this study.</li> <li>Genomad: Results from the genomad pipeline (https://portal.nersc.gov/genomad/)</li> <li>Stats: Aggregate statistics for all sequences of a given provenance.</li> </ul> <p>The natural folder also has a metadata file detailing the taxonomy for all natural sequences.<br><br>Figure datasets are the cleaned (sometimes aggregated) datasets that underly specific figures in the manuscript. The figure designations are based on the order in: https://www.biorxiv.org/content/10.1101/2024.03.19.585716v1.</p>
Bacteriophage Lambda Structure at Atomic Resolution
<p>Bacteriophage Lambda structure at atomic resolution. This structure has been constructed in UCSF Chimera software putting together all the structures that compose bacteriophage lambda using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com Website: https://www.drvictorpadillasanchez.com</p>
Rhizogenic Agrobacterium-specific bacteriophages
<p>Raw fastq sequences (Illumina) of the phages isolated in the VIROPLANT project to control hairy roots disease in tomato.</p> <p>ST40-104 = OLIVR1</p> <p>125 = OLIVR2</p> <p>128g = OLIVR3</p> <p>128k = OLIVR4</p> <p>ST57-123 = OLIVR6</p> <p>ST95-126 = OLIVR5</p> <p> </p> <p>Finalised genbank files that were submitted in NCBI are also part of this dataset.</p> <p> </p>
Viroplant Project - Microcosm studies on the effect of bacteriophages used as plant protection products on soil microbial communities
<p>This file contains the description, data and DNA analyses on the effect of bacteriophages with a potential to be used as plant protecction products on the structure and function of soil microbial communities. The objective was to evaluate two different microcsom incubation systems with phages and microbial cells from soil, or soil itself and to analyses in a time dependent manner how the phages affect the natural soil microbiomes. The microbial communities were quantified with qPCR and their diversity analyzed with PCR amplified 16S rRNA gene sequences. Bioinformatic analyses were used to evaluate microbial community responses</p>
Strains used in the paper "Bacteriophage cultivation for commensal human gut bacteria"
<p>Sequences of 16S rRNA genes of 411 strains for taxonomic detection;</p> <p>Genomic sequence of of 42 strains for taxonomic detection;</p> <p>Genomic sequence of Bacteroides fragilis and Parabacteroides merdae strains used for genomic analysis in phage-host range analysis experiments. </p>
Bacteriophage phi29 Structural Model at Atomic Resolution
<p>Bacteriophage phi29 structural model at atomic resolution. This structural model has been constructed in UCSF Chimera software putting together all the structures that compose bacteriophage phi29 using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com</p>
Bacteriophage SPP1 Structural Model at Atomic Resolution
<p>Bacteriophage SPP1 structural model at atomic resolution. This structural model has been constructed in UCSF Chimera software putting together all the structures that compose bacteriophage SPP1 using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com</p>
Bacteriophage T5 Structure at Atomic Resolution
<p>Bacteriophage T5 structure at atomic resolution. This structure has been constructed in UCSF Chimera software putting together all the structures that compose bacteriophage T5 using cryoEM reconstructions and pdb structures updated to October 2025. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com</p>
Bacteriophage T7 Structure at Atomic Resolution.
<p>Bacteriophage T7 structure at atomic resolution. This structure has been constructed in UCSF Chimera software putting together all the structures that compose bacteriophage T7 using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com</p>
Datasets of the manuscript "Rational design of profile HMMs for sensitive and specific sequence detection with case studies applied to viruses, bacteriophages, and casposons"
<p><strong>DATASETS</strong></p> <p>Rational design of profile HMMs for sensitive and specific sequence detection with case studies applied to viruses, bacteriophages, and casposons</p> <p>Liliane S. Oliveira, Alejandro Reyes, Bas E. Dutilh and Arthur Gruber<sup>*</sup></p> <p>* Correspondence: <a href="mailto:argruber@usp.br">argruber@usp.br</a> (AG); Tel. +55 11 3091 7274</p> <p> </p> <p>Here we provide different data of <em>Microviridae</em>, <em>Flavivirus</em> and casposons used throughout the work:</p> <ul> <li>Microviridae folder <ul> <li>conserved_HMMs – profile HMMs constructed with TABAJARA in Conservation mode for <em>Microviridae</em></li> <li>discriminative_HMMs – profile HMMs constructed with TABAJARA in Discrimination mode for <em>Microviridae</em></li> <li>sequences – different sequence datasets and respective multiple sequence alignments <ul> <li>Microviridae_113-seq_training_set.fasta - 113 VP1 sequences covering diversity of the <em>Microviridae</em> family</li> <li>Microviridae_113-seq.aln – multiple sequence alignment of the 113-protein dataset</li> <li>Microviridae_1836-seq_testset.fasta - 1,836 sequence dataset covering 1,836 sequences of the major capsid protein (VP1) comprising 501 <em>Alpavirinae</em> sequences, 1,040 <em>Gokushovirinae</em> sequences and 295 <em>Pichovirinae</em> sequences</li> <li>Microviridae_1866-seq.aln - multiple sequence alignment of the 1,866-protein <em>Microviridae</em> dataset used in the experiment of Figure 4</li> </ul> </li> </ul> </li> <li>Flavivirus folder <ul> <li>conserved_HMMs – profile HMMs constructed with TABAJARA in Conservation mode for <em>Flavivirus</em></li> <li>discriminative_HMMs – profile HMMs constructed with TABAJARA in Discrimination mode for <em>Flavivirus</em> <ul> <li>full-length – models constructed from full-length protein sequences</li> <li>short - models constructed from selected short alignment blocks of the protein sequences</li> </ul> </li> <li>sequences – different sequence datasets and respective multiple sequence alignments <ul> <li>Flavivirus_127-seq_training_set.fasta - 127 polyprotein sequences covering species diversity of the genus <em>Flavivirus</em></li> <li>Flavivirus_127-seq.aln – multiple sequence alignment of the 127-protein dataset</li> <li>Flavivirus_6364-seq_testset.fasta - 6,364 sequence dataset covering species diversity of <em>Flavivirus</em>, including 3,919 of dengue virus (DENV), 327 of Zika virus (ZIKV), 63 of yellow fever virus (YFV), and the remaining 2,055 sequences covering other available flaviviruses</li> <li>Flavivirus_6364-seq.aln - multiple sequence alignment of the 6,364-protein <em>Flavivirus</em> dataset</li> </ul> </li> </ul> </li> <li>Casposons folder <ul> <li>casposon_generic_HMMs – profile HMMs constructed with TABAJARA in Discrimination mode for the generic detection of all casposons and discrimination from CRISPRs.</li> <li>casposon_family_discriminative_HMMs – profile HMMs constructed with TABAJARA in Discrimination mode for the specific discrimination among casposon families and from CRISPRs.</li> <li>sequences – different sequence datasets and respective multiple sequence alignments <ul> <li>casposons_crisprs.fasta – 106 Cas1 <em>bona fide</em> sequences derived from 52 CRISPRs and 54 casposons</li> <li>casposon_family_discrimination.aln - multiple sequence alignment of 52 <em>bona fide</em> CRISPR and 54 casposon sequences, with appropriate nomenclature to run TABAJARA for the discrimination of each casposon family.</li> <li>casposons_crisprs_discrimination.aln - multiple sequence alignment of 52 <em>bona fide</em> CRISPR and 54 casposon sequences, with appropriate nomenclature to run TABAJARA for discrimination of CRISPRs and casposons.</li> </ul> </li> </ul> </li> </ul>
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>
Data and code from: Engineering bacteriophages through deep mining of metagenomic motifs
Open the record for dataset details and reuse information.
Genomic and phenotypic signatures of bacteriophage coevolution with the phytopathogen Pseudomonas syringae
Open the record for dataset details and reuse information.
Nearly complete structure of DT57C bacteriophage reveals unusual architecture of head-to-tail interface and lateral tail fibers
<p><strong>Supplementary materials for "Nearly complete structure of DT57C bacteriophage reveals unusual architecture of head-to-tail interface and lateral tail fibers", Ayala et al.</strong></p><p>The dataset contains the molecular dynamics trajectories for three systems: TTMP tail protein ring along with the adjacent outermost LtfA-LtfC ring (1) and the HCP ring with (2) or without (3) the TCP ring. Each simulation was run in triplicate.</p>
Data from: Bacteriophage infection and killing of intracellular Mycobacterium abscessus
<p><em>Mycobacterium</em> <em>abscessus</em> is a nontuberculous mycobacterium (NTM) that contributes to the decline and death of patients with lung diseases such as cystic fibrosis and other muco-obstructive airway diseases. <em>M. abscessus</em> is challenging to treat due to its extensive antibiotic resistance and ability to survive inside mammalian cells. An alternative to antibiotics is the therapeutic use of bacteriophages (phages). There are recent cases of phage therapy being used to treat <em>M. abscessus</em> infections in people under compassionate-use conditions. However, little is known about the ability of phages to kill bacteria, such as <em>M. abscessus</em>, that reside in an intracellular environment. Here, we used <em>M. abscessus</em> strains and phages from recent phage therapy cases to determine if phages can enter mammalian cells and if they can infect and kill intracellular <em>M. abscessus</em>. Using fluorescence microscopy, we demonstrate phage uptake by macrophages and lung epithelial cells, and we further demonstrate phage infection of intracellular <em>M. abscessus</em> with fluorescent reporter phages. Transmission electron microscopy was additionally used to image phage infection of intracellular <em>M. abscessus</em>. Together, these findings provide the first visualizations of phage-<em>M. abscessus</em> interactions in an intracellular environment. Finally, we show that phage treatment can significantly reduce the intracellular burden of <em>M. abscessus</em> in a manner that depends on both the specific phage and mammalian cell type involved. These results demonstrate the potential to use phage therapy to treat intracellular bacteria, specifically <em>M. abscessus</em>, while also highlighting the importance of prescreening phage therapy candidates for activity in an intracellular environment.</p>
Supplemental Files for manuscript "Prenatal Transmission of Bacteriophage DNA in Humans"
<p>This repository includes the following files:</p> <p>1) Maternal_UCB_Phage_Analysis.RMD<br>This is an R markdown file to reproduce the analysis in the manuscript “Prenatal Transmission of Bacteriophage DNA in Humans”. It depends on the following files:</p> <p>2) BLAST_Processing.R<br>R script used for the processing of BLAST outputs. The outputs of this analysis for our two cohorts and negative controls with each phage database are provided as R objects. <br> a. POPE_AllPhage_Stats.RDS<br> b. POPE_GPD_Stats.RDS<br> c. Witt_AllPhage_Stats.RDS<br> d. Witt_GPD_Stats.RDS<br> e. Neg_AllPhage_Stats.RDS<br> f. Neg_GPD_Stats.RDS</p> <p>3) Clean_Data_Structures.R<br>This script includes 3 sections to generate the data structures used in subsequent analyses. </p> <p>4) Phage_Plots_Tables.R<br>This script includes 3 sections to reproduce the figures and tables from the manuscript “Prenatal Transmission of Bacteriophage DNA in Humans”. </p> <p>5) POPE_index.csv<br>This CSV file contains the metadata for the POPE cohort.</p> <p>6) SummaryMetaData.csv<br>This CSV file contains the metadata for the Witt cohort as relayed by the study authors from the original publication of this dataset.</p> <p>7) README.txt which summarizes the above as well as provides links to other relevant data.</p> <p>These scripts can be used to re-process our sequencing data or to process and interpret additional datasets.</p>
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