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338 results for “antibiotic resistance”
Antibiotic Resistance in Hospital Wastewater in West Africa: A Systematic Review and Meta-Analysis
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GENOMIC INSIGHTS INTO THE GLOBAL EVOLUTION AND ANTIBIOTIC RESISTANCE OF THE MYCOBACTERIUM TUBERCULOSIS COMPLEX
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Exploring the global metaplasmidome: unravelling plasmid landscapes and the spread of antibiotic resistance genes across diverse ecosystems
<p>Plasmid content was predicted from assembled data already publicly available or constructed from reads for this study. The assembled data supplied by Pasolli and colleagues (Pasolli <em>et al.</em>, 2019) , metasub consortium (Danko <em>et al.</em>, 2020) and TARA ocean (Tully <em>et al.</em>, 2018) were used for the human microbiome, the built environment and the marine ecosystem respectively. For assembly in the current study, reads from metagenomes were selected from two main databases. For the soil ecosystem, the metagenomes were selected from the dedicated curated database “TerrestrialMetagenomeDB” (Corrêa <em>et al.</em>, 2020). </p> <p>If the metagenomes were not assembled, reads were assembled by using megahit 1.2.9 with the metalarge option (Li <em>et al.</em>, 2015) after cleaning the data with bbduk2 (qtrim=rl trimq=28 minlen=25 maq=20 ktrim=r k=25 mink=11 and a list of adapters to remove) from the bbtools suite (<a href="https://jgi.doe.gov/data-and-tools/software-tools/bbtools/">https://jgi.doe.gov/data-and-tools/software-tools/bbtools/</a>).</p> <p>Plasmids were predicted for each assembly by using both reference-based and reference-free approaches as described in previous works (Hilpert <em>et al.</em>, 2021; Hennequin <em>et al.</em>, 2022) and available on the github website (https://github.com/meb-team/PlasSuite/). The databases used for the first approach included those for chromosomes (archaea and bacteria) and plasmids from RefSeq, as well as the MOB-suite tool (Robertson and Nash, 2018), SILVA (Quast <em>et al.</em>, 2013) and phylogenetic markers hosted by chromosomes (Wu <em>et al.</em>, 2013). The database created for this purpose is available at this address <a href="https://github.com/meb-team/PlasSuite/?tab=readme-ov-file#1-prepare-or-download-your-databases">https://github.com/meb-team/PlasSuite/?tab=readme-ov-file#1-prepare-or-download-your-</a><a href="https://github.com/meb-team/PlasSuite/?tab=readme-ov-file#1-prepare-or-download-your-databases">databases</a>. Two reference-free methods were applied to contigs that were not affiliated with chromosomes (discarded) or plasmids (retained in the first step): PlasFlow (Krawczyk <em>et al.</em>, 2018) and PlasClass (Pellow <em>et al.</em>, 2020). Previously undetected viruses were removed by using ViralVerify (<a href="https://github.com/ablab/viralVerify">https://github.com/ablab/viralVerify</a>)(Antipov <em>et al.</em>, 2020) that provides in parallel plasmid/non-plasmid classification. This step would also remove potential plasmid-phage elements as described by Pfeifer <em>et al.</em> (Pfeifer <em>et al.</em>, 2021), but would minimise false positives. Eukaryotic contamination was removed by aligning the sequences against the NT database and human chromosomes (GRCh38) using minimap2 (Li, 2018) with -x asm5 option. Contigs mapping with 95% identity for at least 80% coverage were removed. The predicted plasmids, hereafter referred as plasmid-like sequences (PLSs), were grouped by "scientific names" (<em>i.e.</em> 27) such as defined in the SRA metadata (air, lake, wetland…) and subsequently named ecosystems. These ecosystems were grouped in 9 biomes (Tab Supplementary 4). The data were then dereplicated by ecosystems using cd-hit-est with a threshold of 99%. The dereplicated PLSs were then clustered using MMseqs2 (Steinegger and Söding, 2017) with 80% of coverage an 90% of identity (--min-seq-id 0.90 -c 0.8 --cov-mode 1 --cluster-mode 2 --alignment-mode 3 --kmer-per-seq-scale 0.2) to define plasmid-like clusters (PLCs).</p> <div> <p>The PLC sequences are included in the file "predicted_PLC.fasta" and the main features are dercribed in the file "metadata_PLC.tsv"</p> <ul> <li>fasta_id: fasta identification of the PLC</li> <li>ecosystem: ecosystem from which the PLC originates</li> <li>biome: biome of the ecosystem</li> <li>latitude, longitude: GPS coordinate of the ecosystem</li> <li>length: PLC length</li> <li>map_markers: plasmid marker genes detected by PlasSuite (Hilpert et al., 2021)</li> <li>map_ncbi: PLCs present in the RefSeq plasmid database(Hilpert et al., 2021)</li> <li>nb_genes: Number of genes detected by Prokka implemented in PlasSuite</li> <li>nb_args: ARGs detected by PlasSuite</li> <li>plascad: results from plascad (Che et al., 2021)</li> </ul> <p> </p> <p> </p> <p>Antipov, D., Raiko, M., Lapidus, A., and Pevzner, P.A. (2020) MetaviralSPAdes: assembly of viruses from metagenomic data. <em>Bioinformatics</em> <strong>36</strong>: 4126–4129.</p> <p>Che, Y., Yang, Y., Xu, X., Břinda, K., Polz, M.F., Hanage, W.P., and Zhang, T. (2021) Conjugative plasmids interact with insertion sequences to shape the horizontal transfer of antimicrobial resistance genes. Proceedings of the National Academy of Sciences 118: e2008731118.</p> <p>Corrêa, F.B., Saraiva, J.P., Stadler, P.F., and da Rocha, U.N. (2020) TerrestrialMetagenomeDB: a public repository of curated and standardized metadata for terrestrial metagenomes. <em>Nucleic Acids Res</em> <strong>48</strong>: D626–D632.</p> <p>Danko, D., Bezdan, D., Afshinnekoo, E., Ahsanuddin, S., Bhattacharya, C., Butler, D.J., et al. (2020) Global Genetic Cartography of Urban Metagenomes and Anti-Microbial Resistance. <em>bioRxiv</em> 724526.</p> <p>Hennequin, C., Forestier, C., Traore, O., Debroas, D., and Bricheux, G. (2022) Plasmidome analysis of a hospital effluent biofilm: Status of antibiotic resistance. <em>Plasmid</em> <strong>122</strong>: 102638.</p> <p>Hilpert, C., Bricheux, G., and Debroas, D. (2021) Reconstruction of plasmids by shotgun sequencing from environmental DNA: which bioinformatic workflow? <em>Briefings in Bioinformatics</em> <strong>22</strong>: bbaa059.</p> <p>Krawczyk, P.S., Lipinski, L., and Dziembowski, A. (2018) PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. <em>Nucleic Acids Res</em> <strong>46</strong>: e35.</p> <p>Li, D., Liu, C.-M., Luo, R., Sadakane, K., and Lam, T.-W. (2015) MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. <em>Bioinformatics</em> <strong>31</strong>: 1674–1676.</p> <p>Li, H. (2018) Minimap2: pairwise alignment for nucleotide sequences. <em>Bioinformatics</em> <strong>34</strong>: 3094–3100.</p> <p>Pasolli, E., Asnicar, F., Manara, S., Zolfo, M., Karcher, N., Armanini, F., et al. (2019) Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle. <em>Cell</em> <strong>176</strong>: 649-662.e20.</p> <p>Pellow, D., Mizrahi, I., and Shamir, R. (2020) PlasClass improves plasmid sequence classification. <em>PLOS Computational Biology</em> <strong>16</strong>: e1007781.</p> <p>Pfeifer, E., Moura de Sousa, J.A., Touchon, M., and Rocha, E.P.C. (2021) Bacteria have numerous distinctive groups of phage–plasmids with conserved phage and variable plasmid gene repertoires. <em>Nucleic Acids Res</em> <strong>49</strong>: 2655–2673.</p> <p>Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., et al. (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. <em>Nucleic Acids Res</em> <strong>41</strong>: D590–D596.</p> <p>Robertson, J. and Nash, J.H.E. (2018) MOB-suite: software tools for clustering, reconstruction and typing of plasmids from draft assemblies. <em>Microbial Genomics</em> <strong>4</strong>:.</p> <p>Steinegger, M. and Söding, J. (2017) MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. <em>Nature Biotechnology</em>.</p> <p>Tully, B.J., Graham, E.D., and Heidelberg, J.F. (2018) The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. <em>Scientific Data</em> <strong>5</strong>: 170203.</p> <p>Wu, D., Jospin, G., and Eisen, J.A. (2013) Systematic Identification of Gene Families for Use as “Markers” for Phylogenetic and Phylogeny-Driven Ecological Studies of Bacteria and Archaea and Their Major Subgroups. <em>PLoS One</em> <strong>8</strong>:.</p> </div> <p> </p>
Dataset of Carriage of antibiotic resistant bacteria in endangered and declining Australian pinniped pups
<p>Dataset of samples collected from Australian sea lion, Australian fur seal and long-nosed fur seal pups across eight breeding colonies in Australia from 2016-2019. Includes year of sample collection, animal ID and breeding colony where collection took place. </p> <p>The dataset includes whether integrons were detected in <em>Escherichia coli</em> isolates or in DNA extracted form faecal samples. The gene cassette array for each positive sample has been included. </p> <p>The concentrations of trace elements and heavy metals in blood samples were limited to Australian fur seal pup sampled at Seal Rocks in 2018. The concentrations of Zn, As, Se, Hg, and Pb in whole blood of <em>A. p. doriferus</em> pups sampled at Seal Rocks in 2018 (<em>n</em>=52) were provided by another study (Cobb-Clarke and Gray, personal communication). The data was derived from samples analysed using inductively coupled plasma-mass spectrometry (ICP-MS; Agilent Technologies 7500 ce inductively coupled plasma mass spectroscopy, Santa Clara, CA).</p>
Dataset of Paper "Microplastics in fresh- and wastewater are potential contributors to antibiotic resistance - A minireview"
<p>Dataset of Paper "Microplastics in fresh- and wastewater are potential contributors to antibiotic resistance - A minireview"</p> <ul> <li>Table 2. Reported abundance and characteristics of MPs in freshwater and wastewater in literature.</li> <li>Table 3. Abundance and characteristics of antibiotic resistant elements in freshwater and wastewater.</li> </ul>
Data for: Can heavy metal pollution induce soil bacterial community resistance to antibiotics in boreal forests?
<p>The emergence of microbial antibiotic resistance is a central threat to global health, food security, and development. It has been shown that heavy metal pollution can give rise to microbial resistance to antibiotics, but how wide-spread this phenomenon is remains an open question that urgently needs filling to enable appropriate environmental risk assessments. Here, we determined whether long-term differences in heavy metal pollution in boreal forests had affected soil microbial communities such that they had increased microbial resistance to antibiotics. First, we assessed variation in metal concentrations in samples collected across a distance trajectory from the pollution source, and also the microbial rates and levels of bacterial community resistance to the heavy metal Cu and the antibiotics tetracycline and vancomycin in those samples. Second, we tested if the exposure to Cu or tetracycline could increase bacterial community resistance to Cu and to antibiotics in soils with high versus low background levels of metal contamination. Metal pollution had affected microbial community structures and suppressed decomposer functioning. Importantly, bacterial community Cu resistance increased with higher metal concentrations, which coincided with an induced bacterial community resistance to tetracycline, but not to vancomycin. Laboratory experiments revealed that bacterial community Cu resistance could be further induced in both the low and high end of the pollution gradient, but also that these short-term inductions of community metal tolerance did not coincide with enhanced antibiotic resistance. This yielded a surprising negative correlation between long-term and short-term effects by metals on microbial metal and antibiotic resistances. One mechanism that could provide protection against both metal cations and tetracycline is the small multidrug resistance (SMR) family, which is an energy demanding physiological mechanism that may take time to confer protection. This may explain the different microbial responses to long-term gradients and metal addition experiments. Policy implications. We show that metal pollution in boreal forests will promote antibiotic resistance in soil bacterial communities, revealing an overlooked reservoir of antibiotic resistance. We recommend that environmental risk assessments for any activity giving rise to increased soil metal concentrations need to also consider the induction of microbial antibiotic resistance.</p>
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>
Fig 1 in Tail and fin rot disease and antibiotics resistance pattern in major rainbow trout farms of Nepal
Fig 1: Survey and Sample Collection site
Surface Enhanced Raman Spectroscopy and Machine Learning for Identification of Beta-Lactam Antibiotics Resistance Gene Fragment in Bacterial Plasmid
<p>Background: The appearance of antibiotic-resistant bacteria represents a critical medical problem with high risk to patient health. Therefore, simple, express, and reliable methods of antibiotic resistance detection should be developed.</p> <p>Results: In this work, we propose a combination of highly sensitive surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) for the detection of characteristic gene fragments responsible for antibiotic resistance appearance and spreading. To make the detection procedure close to the real case, we used bacterial plasmids as starting biological objects, containing or not the characteristic gene fragment (up to 1:10 ratio), encoding beta-lactam antibiotics resistance. The plasmids were subjected to enzymatic digestion and the created fragments were captured by functional SERS substrates without preliminary (bio)samples separation or purification. Based on subsequent SERS measurements, a database was created for the training and validation of ML.</p> <p>Significance: The reliability of the proposed method was tested on control samples and we showed the possibility of express SEPS-ML detection of bacterial plasmids containing a characteristic gene up to the 10-7 concentration of the initial plasmid, despite the complex composition of the biological sample (i.e. the presence of the excess of alternative plasmids or various biomolecules). The proposed approach provides a good alternative to modern methods for monitoring antibiotic-resistant bacteria and is favored by its simplicity, low detection limit, and the possibility of express and unpretentious analysis.</p>
Virulence and antibiotic resistance plasticity of Arcobacter butzleri: insights on the genomic diversity of an emerging human pathogen (genome assembly, annotation dataset, core- and pan-genome loci)
<p>This dataset refers to the analysis of 49 <em>Arcobacter butzleri</em> genomes and includes the assembled contigs (.fasta and .gbk files), the nucleotide sequences of the predicted transcripts (CDS, rRNA, tRNA, tmRNA, misc_RNA) (.ffn files), the respective amino acid sequences of the translated CDS sequences (.faa files), the nucleotide alignments of all the 1165 core-genome loci, the nucleotide alignments of the genes <em>hecA</em>, <em>tetR </em>and <em>porA</em>, the categorized amino acid sequences of the six hypervariable regions of PorA, and the nucleotide sequences of the first allele of each of the 7474 pan-genome loci with the respective complete allelic profile matrix.</p> <p>All raw sequence reads used in this study were deposited in the European Nucleotide Archive (ENA) (BioProject PRJEB34441).</p>
Data set "Correlation of in vitro biofilm formation capacity with persistence of antibiotic-resistant Escherichia coli on fresh leafy produce"
<p>This repository contains the metadata and raw data for the analysis of an <em>in vitro </em>screening of 174 antibiotic-resistance E. coli strains isolated from various sources to evaluate their ability and strength to form biofilms.</p> <p>This repository contains the raw data to characterise a subset of eleven <em>E. coli </em>strains in their population dynamics and persistance on lamb's lettuce (<em>Valerianella locusta</em>) leaves.</p> <p>Raw images (czi format) of live/dead stain of selected strains in <em>V. locusta </em>leaves are provided.</p> <p>Data analysis and image processing scripts can be found in the GitHub repository associated to the manuscript.</p>
Processed data for Evidence of horizontal gene transfer and environmental selection impacting antibiotic resistance evolution in soil-dwelling Listeria
<p>Processed/source data for the manuscript Evidence of horizontal gene transfer and environmental selection impacting antibiotic resistance evolution in soil-dwelling <em>Listeria</em>.</p>
Expanding the host-range of functional metagenomics reveals resistance threats to novel antibiotics
<p>Expanding the host-range of functional metagenomics reveals resistance threats to novel antibiotics. Sequences.</p>
Dataset of paper "Growth and prevalence of antibiotic-resistant bacteria in microplastic biofilm from wastewater treatment plant effluents"
<p>Dataset of paper "Growth and prevalence of antibiotic-resistant bacteria in microplastic biofilm from wastewater treatment plant effluents":</p> <ul> <li>Raw 16srRNA forward and reverse sequence data</li> <li>16srRNA partial sequence data for submission to public database</li> <li>Nucleotide BLAST result from National Centre of Biotechnology Information (NCBI) database</li> <li>Summary of sample metadata and bacterial colony forming units (CFUs)</li> <li>Summary of sample metadata and quantified genes</li> </ul>
Intensified livestock farming increases antibiotic resistance genotypes and phenotypes in animal feces
<p class="MsoNormal"><span>Animal feces from livestock farming can be a major source of antibiotic resistance to the environment, but a clear gap exists on how the resistance reservoir in feces alters as farming activities intensify. Here, we sampled feces from eight Chinese farms, where yak, sheep, pig, and horse were reared under free-range to intensive conditions, and determined fecal resistance using both genotype and phenotype approaches. </span><span>A</span><span>nimals reared </span><span><span>intensively</span></span><span> exhibited increased </span><span><span>diversity</span></span><span> of antibiotic resistance genes (ARGs) and greater resistance phenotypes in feces, which were cross-correlated. Furthermore, a</span><span>t the metagenome contig level, ARGs</span><span> </span><span>were </span><span><span>co-located</span></span><span> with </span><span>mobile genetic elements </span><span>at a higher frequency (27.38%) </span><span>as farming intensified, </span><span>with</span><span> associated resistance phenotyp</span><span><span>e</span></span><span>s </span><span>being less coupled with bacterial phylogeny. </span><span>I</span><span>ntensified farming also expanded the multidrug resistance preferentially carried on pathogens in fecal microbi</span><span>omes</span><span><span>.</span></span><span> Overall, </span><span><span>farming intensification </span></span><span>can </span><span><span>increase </span></span><span>antibiotic resistance</span><span> <span>genotypes and phenotypes in </span></span><span>domestic animal </span><span><span>feces</span></span><span>, with implications for environmental health.</span></p> <p> </p>
Data for: Antibiotic-degrading resistance changes bacterial community structure via species-specific responses
<p>Some bacterial resistance mechanisms degrade antibiotics, potentially protecting neighbouring susceptible cells from antibiotic exposure. We do not yet understand how such effects influence bacterial communities of more than two species, which are typical in nature. Here, we used experimental multispecies communities to test the effects of clinically important pOXA-48-plasmid-encoded resistance on community-level responses to antibiotics. We found that resistance in one community member reduced antibiotic inhibition of other species, but some benefitted more than others. Further experiments with supernatants and pure-culture growth assays showed the susceptible species profiting most from detoxification were those that grew best at degraded antibiotic concentrations (greater than zero, but lower than the starting concentration). This pattern was also observed on agar surfaces, and the same species also showed relatively high survival compared to most other species during the initial high-antibiotic phase. By contrast, we found no evidence of a role for higher-order interactions or horizontal plasmid transfer in community-level responses to detoxification in our experimental communities. Our findings suggest carriage of an antibiotic-degrading resistance mechanism by one species can drastically alter community-level responses to antibiotics, and the identities of the species that profit most from antibiotic detoxification are predicted by their intrinsic ability to survive and grow at changing antibiotic concentrations.</p>
Data from: Phenotypic plasticity of antibiotic resistance, metabolism byproduct utilization and the evolution of mutually beneficial cooperation in Escherichia coli
<p><span>Although tag-based donation and recognition have well explained how the cooperative individuals are positively assorted if the cooperative individuals possess some signals and are also able to detect such signals, an additional mechanism is required to explain why some individuals pay the costs of evolving such a tag that may not be rewarded subsequently, and how such tag-based cooperative individuals will meet other similar individuals with a very low mutation rate. Here, we show that many and even all<em> Escherichia coli </em>bacteria cells in the increased antibiotic concentration will plastically evolve to be antibiotic resistant individuals who could protect antibiotic sensitive strain from the attack of antibiotics, and the antibiotic resistant strain could reversibly evolve to be antibiotic sensitive in non-antibiotic supplement medium but in a harsher environment with low glucose. A further experiment showed that antibiotic-sensitive <em>E. coli </em>strain could in turn help reduce the concentration of indole produced by the resistant strain. This metabolic product is harmful to the growth of the antibiotic-resistant strain but benefits the antibiotic-sensitive strain by helping turn on the multi-drug exporter to discharge the antibiotic. The utilization of metabolism byproduct indole produced by antibiotic-resistant cells benefits antibiotic-sensitive cells, while the indole-absorbing service of antibiotic sensitive cells unconsciously help in nullifying the indole side effect on antibiotic resistant strain, and a mutual benefit cooperation could therefore evolve.</span></p>
Data from: Clinical antibiotic-resistance plasmids have small effects on biofilm formation and population growth in Escherichia coli in vitro
<div> <div> <div> <p>Antimicrobial resistance (AR) mechanisms encoded on plasmids can affect other phenotypic traits in bacteria, including biofilm formation. These effects may be important contributors to the spread of AR and the evolutionary success of plasmids, but it is not yet clear how common such effects are for clinical plasmids/bacteria, and how they vary among different plasmids and host strains. Here, we used a combinatorial approach to test the effects of clinical AR plasmids on biofilm formation and population growth in clinical and laboratory Escherichia coli strains. In most of the 25 plasmid-bacterium combinations tested, we observed no significant change in biofilm formation upon plasmid introduction, contrary to the notion that plasmids frequently alter biofilm formation. In a few cases we detected altered biofilm formation, and these effects were specific to particular plasmid-bacterium combinations. By contrast, we found a relatively strong effect of a chromosomal streptomycin-resistance mutation (in rpsL) on biofilm formation. Further supporting weak and host-strain- dependent effects of clinical plasmids on bacterial phenotypes in the combinations we tested, we found growth costs associated with plasmid carriage (measured in the absence of antibiotics) were moderate and varied among bacterial strains. These findings suggest some key clinical resistance plasmids cause only mild phenotypic disruption to their host bacteria, which may contribute to the persistence of plasmids in the absence of antibiotics.</p> </div> </div> </div>
Autologous Fecal Microbiota Transplantation to Prevent Antibiotic Resistant Bacteria Colonization
ClinicalTrials.gov study NCT03061097. IPD Sharing: NO. Countries: 1. Publications: 1.
Evaluate Effectiveness of Epiduo® Gel in Reducing Antibiotic Sensitive & Resistant Strains of Propionibacterium (P)Acnes
ClinicalTrials.gov study NCT00907101. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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