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1,017 results for “Antimicrobial”
DRAMMA: A multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data (dataset 2 of 2)
<p>Dataset for the support of a journal publication. </p> <p>Part 2/2 of a dataset used for running and training the ML model.</p> <p>Part 1 is available at <a href="https://doi.org/10.5281/zenodo.14513933">10.5281/zenodo.14513933</a></p> <p>The code associated with this publication is available at: https://github.com/burstein-lab/DRAMMA </p>
DRAMMA: A multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data (dataset 1 of 2)
<p>Dataset for the support of a journal publication. </p> <p>Part 1/2 of a dataset used for running and training the ML model.</p> <div> <div>Part 2 is available at <a href="https://doi.org/10.5281/zenodo.14524613">10.5281/zenodo.14524613</a></div> <div> </div> </div> <p>The code associated with this publication is available at: https://github.com/burstein-lab/DRAMMA </p>
Computational dataset, scripts and models for 'Lipid shape as a membrane activity modulator of a model antimicrobial peptide'
<p>Analysis scripts and computational models used in the manuscript 'Lipid shape as a membrane activity modulator of a model antimicrobial peptide', by Marcin Makowski, Octávio L. Franco, Nuno C. Santos and Manuel N. Melo.</p>
Antimicrobial treatment recommendations for common bacterial infections and syndromes from national/standard treatment guidelines in African Union member states
<p>A compilation of antimicrobial treatment recommendations for common bacterial infections and syndromes for adult and pediatric patients from national/standard treatment guidelines in African Union member states</p>
The lower airways microbiome and antimicrobial peptides in Idiopathic Pulmonary Fibrosis differ from Chronic Obstructive Pulmonary Disease
<p><strong>Background</strong>: The lower airways microbiome and host immune response in chronic pulmonary diseases are incompletely understood. We aimed to investigate possible microbiome characteristics and key antimicrobial peptides and proteins in idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD).</p> <p><strong>Methods</strong>: 12 IPF patients, 12 COPD patients and 12 healthy controls were sampled with oral wash (OW), protected bronchoalveolar lavage (PBAL) and right lung protected sterile brushings (rPSB). The antimicrobial peptides and proteins (AMPs), secretory leucocyte protease inhibitor (SLPI) and human beta defensins 1 and 2 (hBD-1 & hBD-2), were measured in PBAL by enzyme linked immunosorbent assay (ELISA).</p> <p>The V3V4 region of the bacterial 16S rDNA gene was sequenced. Bioinformatic analyses were performed with QIIME 2.</p> <p><strong>Results</strong>: hBD-1 levels in PBAL for IPF were lower compared with COPD. The predominant phylae in IPF were <i>Firmicutes</i>, <i>Bacteroides</i> and <i>Actinobacteria</i>; <i>Proteobacteria</i> were among top three in COPD. Differential abundance analysis at genus level showed significant differences between study groups for less abundant, mostly oropharyngeal, microbes. Alpha diversity was lower in IPF in PBAL compared to COPD (p=0.03) and controls (p=0.01), as well as in rPSB compared to COPD (p=0.02) and controls (p=0.04). Phylogenetic beta diversity showed significantly more similarity for IPF compared with COPD and controls. There were no significant correlations between alpha diversity and AMPs.</p> <p><strong>Conclusions</strong>: IPF differed in microbial diversity from COPD and controls, accompanied by differences in antimicrobial peptides. Beta diversity similarity between OW and PBAL in IPF may indicate that microaspiration contributes to changes in its microbiome<b>.</b></p>
Supplementary material 1 from: Cimarelli L, Giuliodori A, Brandi A, Adamkiewicz K, Spurio R, Fabbretti A (2017) Screening an Archetypal Collection of Microorganisms for the Presence of Unexplored Antimicrobial Compounds. BioDiscovery 20: e10763. https://doi.org/10.3897/biodiscovery.20.e10763
Screening an archetypal collection of microorganisms for the presence of unexplored antimicrobial compounds
Millennial-Scale Microbiome Analysis Reveals Ancient Antimicrobial Resistance Conserved Despite Modern Selection Pressures
<p>This dataset contains metadata and profiled antibiotic resistance genes from ancient metagenome permafrost.</p>
Synergistic antimicrobial mechanism of the ultra-short antimicrobial peptide R3W4V with a tadpole-like conformation
Open the record for dataset details and reuse information.
Fig. 5 Insect-associated Streptomyces are a in The antimicrobial potential from insect microbiomes of Streptomyces
Fig. 5 Insect-associated Streptomyces are a source of active antimicrobials. a Fractionated extracts from insect microbiomes are active in multiple murine models of drug-resistant infection. Less infective burden is seen in intraperitoneally treated mice after 8 h of infection. Each dot represents a unique fraction in one mouse study. (n = 15, 11, and 8 for C. albicans, E. coli, and P. aeruginosa models, respectively; center, median; box, upper and lower quantiles; whiskers, 1.5× interquartile range. b Most fractions from insect microbiomes show no hemolysis in cell-based assays. Safe indicates no toxicity at>100× concentration associated with efficacy. c The antifungal cyphomycin is produced by Streptomyces isolated from d the fungus-growing ant Cyphomyrmex sp. Photo credit: Alexander Wild e Cyphomycin-containing fractions show potency against the ant pathogen Escovopsis sp. (top left, bottom). f Purified cyphomycin exhibits potency against resistant pathogens. g Mouse candidiasis (C. albicans) models showcase reduced infection and a dose-like response to cyphomycin. Dots indicate individual mice
Fig. 4 Ecology and phylogeny influence biosynthetic potential. a A in The antimicrobial potential from insect microbiomes of Streptomyces
Fig. 4 Ecology and phylogeny influence biosynthetic potential. a A core-genome phylogeny shows evolutionarily distinct lineages of Streptomyces associate with insects (subset shown, see Supplementary Figure 2). BGC similarity to known BGCs highlights the biosynthetic diversity of insect microbiome strains. b Source invariant (blue) and sub-clade invariant (red) BGC families suggest BGC presence is influenced by both source and phylogeny. LC/MS metabolomics revealed MFs that are unique to c source and d phylogeny. e PCA of the metabolomes identified an outlier strain and f MFs that contribute to its uniqueness, including cyphomycin
Fig. 1 Sampling strategy for Streptomyces from insect microbiomes. Streptomyces were isolated from a in The antimicrobial potential from insect microbiomes of Streptomyces
Fig. 1 Sampling strategy for Streptomyces from insect microbiomes. Streptomyces were isolated from a wide range of insects and geographies (1445 insects; 10,178 strains; dot size, insects sampled). Streptomyces production of the antifungal mycangimycin (1) in the Southern Pine Beetle system is shown at right. Cyphomycin (2) is a new antifungal described herein. Photo credits: southern pine beetle - Erich G. Vallery; fungus-growing ant – Alexander Wild
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>
Dataset for External quality assessment (EQA) of Neisseria gonorrhoeae antimicrobial susceptibility testing in primary laboratories in Germany
<p>This dataset is used for the publication "External quality assessment (EQA) of Neisseria gonorrhoeae antimicrobial susceptibility testing in primary laboratories in Germany" and contains data on performed Neisseria gonorrhoeae AMR tests in sentinel Laboratories.</p>
Creating a 7,000 strains genotype-phenotype dataset of E. coli and antimicrobial resistance phenotypes
<h1>Description</h1> <p>This Zenodo repository contains the data (except for the input fastq files available on SRA and intermediary files generated during the variant calling process) and code to recapitulate the study from <a href="https://doi.org/10.57844/arcadia-d2cf-ebe5">https://doi.org/10.57844/arcadia-d2cf-ebe5</a> and the associated GitHub repository, where the code, pipelines, and analysis are described in more detail.</p> <p> </p> <h1>Work summary</h1> <p>In this work, we established a framework for compiling large genotype-phenotype datasets and produced a large-scale dataset of more than 7,000 <em>E. coli</em> strains and antimicrobial resistance phenotypes.</p> <p>We leveraged the genetic information and antimicrobial resistance (AMR) phenotype data available for the bacterium <em>Escherichia coli</em> to construct our dataset and took advantage of the existing knowledge about genetic variations and AMR phenotypes to validate our approach and dataset. We performed variant calling and compiled a genotype-phenotype dataset for more than 7,000 <em>E. coli</em> strains. Briefly, variant calling consists of identifying all genetic variations and their associated genotypes in a population compared to a reference genome. This is performed by aligning sequencing reads for each strain of the population against a reference genome, then identifying polymorphic regions in the population, and finally characterizing variants and their genotypes at each of these polymorphic regions.</p> <p>We have generated a dataset that successfully revealed significant genetic diversity and identified 2.4 million variants. By focusing on non-silent variants within genes associated with AMR, we confirmed the dataset's accuracy. </p> <p>We hope this study is a foundational resource for conducting large-scale genotype-phenotype studies that will offer valuable insights for genetics investigations, informing the development of treatments and prevention strategies for AMR. This resource is invaluable for microbiologists and epidemiologists seeking to understand AMR mechanisms and improve genotype-phenotype predictions in pathogenic <em>E. coli</em> outbreaks. Additionally, it's of particular interest to geneticists and evolutionary biologists, providing a dataset to develop strategies for studying genetic interactions and broader applications in phenotype-phenotype predictions and phylogenetic research.</p> <h1>Data organization</h1> <p>Data are organized in the compressed folder. Briefly, they’re divided into two main folders.</p> <p>The first folder, <strong>dataset_generation</strong>, includes the code and information necessary to build the genotype dataset and perform the variant calling. It covers major steps like the generation of the reference pangenome used for variant calling, the variant calling pipeline applied to each of the 7,000 strains, the filtering of false positive variants, and the annotation of the variants. </p> <p>The second section, <strong>dataset_analysis</strong>, includes the code and information used to process and analyze the dataset and generate figures for the Pub (https://doi.org/10.57844/arcadia-d2cf-ebe5). It includes the preliminary analysis of AMR phenotypes within the population and the analysis of variants regarding known AMR phenotypes.</p> <p> </p> <h1>Files description</h1> <p>The following table provides a list and description of the different files and their locations.</p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>Location</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>variant_calling_pipeline</td> <td>dataset_generation/scripts/</td> <td>Snakefile: performs variant calling from raw paired-end sequencing files and generate one vcf.gz file per sample</td> </tr> <tr> <td>snakemake_ECOR72_annotation</td> <td>Snakefile: performs Prokka annotation on inputs whole genome fastq files</td> </tr> <tr> <td>ECOR72_and_DP_threshold_analysis.Rmd</td> <td>R markdown: analyses the coverage of known present and absebt loci in the ECOR population</td> </tr> <tr> <td>average_coverage_41.csv</td> <td>dataset_generation/data/dp_threshold/</td> <td>Pangenome loci read coverage information for 40 ECOR strains</td> </tr> <tr> <td>average_coverage_last32.csv</td> <td>Pangenome loci read coverage information for 32 ECOR strains</td> </tr> <tr> <td>whole_pan_ecor_presence_absence.csv</td> <td>Reformated pangenome loci presence-absence in ECOR strains</td> </tr> <tr> <td>pangenome_genomes_SRA_GCA.csv</td> <td>Correspondance table between ECOR72 strains genome names and raw sequencing files SRA accession number</td> </tr> <tr> <td>index_loci_pangenome_good.txt</td> <td>List of indexed positions in the pangenome</td> </tr> <tr> <td>list_ecor_txtfiles.txt</td> <td>List of txt files (containing the DP information per nucleotide) to use - This corresponds to the files for each 72 ECOR strains</td> </tr> <tr> <td>ECOR72_SRA_and_assembly_accessions.csv</td> <td>dataset_generation/data/</td> <td>List of Genome accession number and the SRA accession number of the associated sequencing files for the 72 ECOR strains</td> </tr> <tr> <td>sample_list_SRA.csv</td> <td>List of SRA accession numbers of the E. coli strains used for variant calling</td> </tr> <tr> <td>gene_presence_absence.csv</td> <td>dataset_generation/results/pangenome_cds/</td> <td>Roary output of presence-absence of the pangenome cds loci in the ECOR72 strains</td> </tr> <tr> <td>genes.gff</td> <td>Annotation file of the pangenome cds sequences (Prokka output)</td> </tr> <tr> <td>pangenome_cds.fa</td> <td>Roary output cds_pangenome sequencing file</td> </tr> <tr> <td>summary_statistics.txt</td> <td>Roary statistics output of creations of the cds pangenome</td> </tr> <tr> <td>roary_output</td> <td>Roary output folder</td> </tr> <tr> <td>IGR_presence_absence.csv</td> <td>dataset_generation/results/pangenome_igr/</td> <td>Piggy output of presence-absence of the pangenome igr loci in the ECOR72 strains</td> </tr> <tr> <td>pangenome_igr.fasta</td> <td>Piggy output igr_pangenome sequencing file</td> </tr> <tr> <td>piggy_output</td> <td>Piggy output folder</td> </tr> <tr> <td>whole_pangenome.fasta</td> <td>dataset_generation/results/pangenome_whole/</td> <td>whole pangenome sequences</td> </tr> <tr> <td>annot_summary_filtered.html</td> <td>dataset_generation/results/vcf/</td> <td>Summary of snpEff annotations</td> </tr> <tr> <td>annotated_output.vcf.gz</td> <td>snpEff annotated vcf file</td> </tr> <tr> <td>annotated_output.vcf.gz.csi</td> <td>indexed annotated vcf file</td> </tr> <tr> <td>filtered_output.vcf.gz</td> <td>filtered vcf file (removed low coveraged and low quality variants)</td> </tr> <tr> <td>filtered_output.vcf.gz.csi</td> <td>indexed filtered vcf files</td> </tr> <tr> <td>output.non_silent.vcf.gz</td> <td>vcf file containing only the nonsilent variants in the pangenome cds loci</td> </tr> <tr> <td>merged_output_listN.vcg.gz</td> <td>intermediary vcf files of 1000 merged strains vcf - these intermediary merged files are numbered from 1 to 7</td> </tr> <tr> <td>merged_output_all.vcf.gz</td> <td>final vcf.gz files of all merged vcf files in this study</td> </tr> <tr> <td>List_N_merging.txt</td> <td>dataset_generation/data/vcf_merging/</td> <td>List of the 1000 vcf.gz files to be merged together. There are 7 lists, numbered from 1 to 7</td> </tr> <tr> <td>ecor72_array.txt</td> <td>dataset_generation/results/ecor72_DP/</td> <td>Consolidate DP information per nucleotide for each ECOR strain</td> </tr> <tr> <td>variants_pos.tsv</td> <td>dataset_analysis/data/variant_analysis/</td> <td>List of all the variants found in the population and identified by their locus and position within the locus</td> </tr> <tr> <td>allele_freqs.txt</td> <td>Variant frequency informations</td> </tr> <tr> <td>variants_non_silent_pos.tsv</td> <td>List of all the non-silent variants found in the population and identified by their locus and position within the locus</td> </tr> <tr> <td>allele_non_silent_freqs.txt</td> <td>Non-silent variant frequency informations</td> </tr> <tr> <td>cds_eggNog.tsv</td> <td>eggNog output file of the pangenome annotation</td> </tr> <tr> <td>COG_functional_categories.csv</td> <td>Correspondance between COG functional categories and higher-order annotation</td> </tr> <tr> <td>BVBRC_genome_May31.csv</td> <td>dataset_analysis/data/dataset_analysis</td> <td>List of E. coli with available genomes as reported in BCBRV database</td> </tr> <tr> <td>BVBRC_genome_amr_May31.csv</td> <td>E. coli antimicrobial resistance information available in BCBRV database</td> </tr> <tr> <td>antibiotic_class.csv</td> <td>Antibiotic name and Antibiotic class information</td> </tr> <tr> <td>resistance_output.non_silent.vcf.gz</td> <td>dataset_analysis/data/antimicrobial_resistance_analysis</td> <td>vcf.gz file of the loci expected to be associated with antimicrobial resistance</td> </tr> <tr> <td>antibiotic_resistance_freq.csv</td> <td>Frequency information for the non-silent variant in the selected antimicrobial genes</td> </tr> <tr> <td>SRA_to_genome_name.csv</td> <td>correspondence between strain SRA accession number and genome name (as reported in BVBRC)</td> </tr> <tr> <td>Dataset_metainfo_AMR_analysis.Rmd</td> <td>dataset_analysis/scripts</td> <td>R markdown: conducts the characterization of the population and analysis of the AMR phenotype distribution</td> </tr> <tr> <td>Variant_population_analysis.Rmd</td> <td>R markdown: conducts the analysis and investigation of identified variants in the population</td> </tr> <tr> <td>Antimicrobial_resistance_investigation.Rmd</td> <td>R markdown: conducts the antimicrobial resistance investigation </td> </tr> </tbody> </table> <p> </p> <p> </p>
FIGURE 3. Simplicillium coffeanum COAD 2057. A in Simplicillium coffeanum, a new endophytic species from Brazilian coffee plants, emitting antimicrobial volatiles
FIGURE 3. Simplicillium coffeanum COAD 2057. A, Host plant; B, colony; C, Colony reverse; D–F, Hypha, phialides and conidia. Scale bars = 10 μm
FIGURE 1 in Simplicillium coffeanum, a new endophytic species from Brazilian coffee plants, emitting antimicrobial volatiles
FIGURE 1. Bayesian Inference tree showing the phylogenetic relationship between Simplicillium coffeanum and closely taxa based on partial 28S rDNA sequences. The posterior probability values are indicated at the nodes. The isolates from this study are highlighted in bold. The tree is rooted with Ceratocystis moniliformis. Asterisks indicate the type strains.
FIGURE 2 in Simplicillium coffeanum, a new endophytic species from Brazilian coffee plants, emitting antimicrobial volatiles
FIGURE 2. Bayesian Inference tree of Simplicillium and closely Cordycipitaceae using ITS-5.8S sequences of rDNA. The posterior probability values are indicated at the nodes. The Simplicillium isolates from this study are highlighted in bold. The tree is rooted with Pochonia chlamydosporia. Asterisks indicate the type strains.
INVITED REVIEW: Antimicrobial Resistance Genes in Milk: a 10-year-systematic review and critical comment
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Exploring risk factors and antimicrobial susceptibility patterns associated with bacteriuria among Syrian refugees in makeshift camps
<p><span>Database and code necessary to replicate the analysis.</span></p>
Fig. 2 in Antimicrobial and antioxidant efficacy of Citrus limon L. peel extracts used for skin diseases by Xhosa tribe of Amathole District, Eastern Cape, South Africa
Fig. 2. Nitric oxide scavenging activity of Citrus limon extracts. Results are means of 3 replicates.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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