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2,375 results for “Antibiotics”
Geographic and specialty antibiotic prescribing in Medicare Part D, and its relationship with hospital acquired infections
<p>Datasets analyzed in the paper: Geographic and specialty antibiotic prescribing in Medicare Part D, and its relationship with hospital acquired infections.</p>
Long-term effects of antibiotic treatments on honeybee colony fitness – a modelling approach
<p><b>1.</b> Gut microbiome disequilibrium is increasingly <span>implicated </span>in host fitness reductions, including for the economically important and disease-challenged western honey bee, <i>Apis mellifera</i>. In lab experiments the antibiotic tetracycline, which is used to prevent American Foulbrood Disease in countries including the US, elevates honey bee mortality by disturbing the microbiome. It is unclear however, how elevated individual mortality affects colony level fitness.</p> <p><b>2.</b> We used an agent-based model (BEEHAVE) and empirical data to assess colony level effects of antibiotic-induced worker bee mortality, by measuring colony size. We investigated the relationship between the duration that the antibiotic-induced mortality probability is imposed for and colony size.</p> <p><b>3.</b> We found that when simulating antibiotic-induced mortality of worker bees from just 60 days per year, up to a permanent effect, the colony is reduced such that tetracycline treatment would not meet the European Food Safety Authority's (EFSA) honey bee protection goals. When antibiotic mortality was imposed for the hypothetical minimal exposure time, which assumes that antibiotics only impact the bee's fitness during the recommended treatment period of fifteen days in both spring and autumn, the colony fitness reduction was only marginally under the EFSA's threshold.</p> <p><b>4.</b> Synthesis and Applications: Modelling colony level impacts of antibiotic treatment shows that individual antibiotic-induced honey bee worker mortality can lead to colony mortality. To assess the full impact, the persistence of antibiotic-induced mortality in honey bees must be determined experimentally, <i>in vivo</i>. We caution that as the domestication of new insect species increases, maintaining healthy gut microbiomes is of paramount importance to insect health and commercial productivity. The recommendation from this work is to limit prophylactic use of antibiotics and to not exceed recommended treatment strategies for domesticated insects. This is especially important for highly social insects as excess antibiotic use will likely decrease colony growth and an increase in colony mortality.</p>
Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations
<p>Standard infectious disease practice calls for aggressive drug treatment that rapidly eliminates the pathogen population before resistance can emerge. When resistance is absent, this elimination strategy can lead to complete cure. However, when resistance is already present, removing drug-sensitive cells as quickly as possible removes competitive barriers that may slow the growth of resistant cells. In contrast to the elimination strategy, the containment strategy aims to maintain the maximum tolerable number of pathogens, exploiting competitive suppression to achieve chronic control. Here we combine <em>in vitro</em> experiments in computer-controlled bioreactors with mathematical modeling to investigate whether containment strategies can delay failure of antibiotic treatment regimens. To do so, we measured the "escape time" required for drug-resistant <em>E. coli</em> populations to eclipse a threshold density maintained by adaptive antibiotic dosing. Populations containing only resistant cells rapidly escape the threshold density, but we found that matched resistant populations that also contain the maximum possible number of sensitive cells could be contained for significantly longer. The increase in escape time occurs only when the threshold density--the acceptable bacterial burden--is sufficiently high, an effect that mathematical models attribute to increased competition. The findings provide decisive experimental confirmation that maintaining the maximum number of sensitive cells can be used to contain resistance when the size of the population is sufficiently large.</p>
Antibiotic resistance in Gram-negative Bacteria Causing Blood stream infections
<p><b>Background:</b> The pathogenic spectrum of blood stream infections (BSIs) varies across regions. Monitoring the pathogenic profile and antimicrobial resistance is a prerequisite for effective therapy and infection control. The study analyzed the pathogenic spectrum of blood cultures with special focus on the resistance pattern of <i>A.baumannii,</i> <i>E.coli</i> and <i>K.</i> <i>pneumoniae</i> from a referral hospital of Saudi Arabia.</p>
Antibiotics Change the Growth Rate Heterogeneity and Morphology of Bacteria
<p>Each experiment was conducted on the Multipad Agarose Plate (MAP) and is labeled with BE followed by a number. This repository contains four main elements:</p> <ol> <li>The file <code>Experiment condition map.json</code> describes how the MAP platforms were set up for each experiment. Some datasets also include data that was discarded. </li> <li>The folder <code>Benchmarking</code> contains data for how we validated the performance of our <code>PadAnalyser</code> package. <code>Originals</code> have a set of images of bacteria affected by antibiotics that produce different morphologies. <code>Annotated_raw</code> is the human-labelled version of these, and <code>Annotated</code> are these converted to a format that can be used by code for comparison. <code>Segmentation Benchmarking.ipynb</code><em> </em>is a Jupyter Notebook that segments the cells and compares the output to the annotated data. </li> <li>The folder <code>Dataframes</code><em> </em>contains Pandas data frames from all experiments, classified by bacteria species. </li> <li>The folder <code>Videos</code> contains debug videos visualising how the segmentation algorithm performed on the different datasets. Contact us for access to the raw data. Videos in the format <code>*_ssi.mp4</code> contain single-cell masks, and <code>*_css.mp4</code> contains colony masks. </li> </ol>
Dataset: Survey on the actual situation of antibiotic resistant bacteria detection by nucleic acid amplification test in clinical microbiology laboratories at hospitals in Japan: Online survey of participants in workshops organized by the Nara Association of Medical Technologists
<p>The coronavirus disease 2019 pandemic has led to the widespread use of the nucleic acid amplification test (NAAT), along with an increase in demand for SARS-CoV-2 tests. NAAT has been used to detect antimicrobial resistance (AMR) genes since before the pandemic, but the test has been performed in a limited number of facilities. We investigated the current status and background of Japanese clinical laboratories by surveying the implementation of genotypic AST in NAAT, which has become widespread owing to the pandemic. This means that 59% of the respondents possessed NAAT and were using it for genotypic AST. GeneXpert and FilmArray were introduced in the majority of cases (62.5% and 82.6%, respectively), with the pandemic as the trigger. More than half of the respondents cited “rapid detection” (56.0%) and “ICT requests” (52.4%) as the reasons for introducing the system. Regarding usefulness, “contribution to infectious disease treatment” (74.1%) showed the highest percentage. Among the respondents who cited “not implemented”, the most frequent responses were “I have no plans, but I want to do it.” (38.1%) and “would do so if requested by a physician” (33.3%). The most common reason for not implementing the system was concern about increased workload (52.9%). We believe that this is due to changes in the working environment caused by the pandemic and the characteristics of Japanese society. In the future, to promote the adoption of genotypic AST, it will be necessary to approach it through reports on its usefulness from domestic facilities, and simultaneously, improving and enhancing efficiency in work processes will also be essential.</p>
Antibiotics are not for viruses
<p>Video in English (see also the Swahili version) warns that attempting to treat a virus or common cold by self-medication with antibiotics can lead to the development of Antimicrobial Resistance (AMR). This was originally developed as an audio piece, but still images have been added in post-production to create a video. </p><p>Audio and images produced as part of a Participatory Action Research (PAR) Workshop with young professionals in Mwanza, Tanzania to create public health messages on Antimicrobial Resistance in a post-COVID East Africa in June 2022. The project built upon data gathered in two international, interdisciplinary research projects (HATUA – 'Holistic Approaches to Understanding Antimicrobial Resistance in East Africa' and CARE – 'COVID-19 and Antimicrobial Resistance in East Africa – Impact and Response'), seeking to understand the wider medical and societal drivers of AMR in East Africa and identify possible interventions to curb the spread of AMR. The workshop ran for 9 days over a 3 week period and consisted of focus group style discussions with participants to explore issues surrounding AMR, antibiotic use, and public health messaging awareness in local communities (days 1-2), participant-led design of poster, radio, and video messages with feedback from the research team and introduction to filming/recording equipment (days 3-4), filming, shooting and recording materials within local settings in Mwanza with participants serving as actors, directors, and crew with guidance from research team (days 4-8) and a final in-person review and hands-on feedback of preliminary mock-ups of posters and videos (day 9). Participants have continued to collaborate via email and WhatsApp as materials were finalised. Final video production and editing was undertaken by the researchers. </p><p>Correspondence: mgk@st-andrews.ac.uk; kjf4@st-andrews.ac.uk</p><p> </p>
Simulations For: Biophysical basis of filamentous phage tactoid-mediated antibiotic tolerance in P. aeruginosa
<p>Coordinate, simulation input and simulation output files for atomistic molecular dynamics simulations in Biophysical basis of filamentous phage tactoid-mediated antibiotic tolerance in P. aeruginosa. </p><p> </p>
Data supporting "Antibiotic dose and nutrient availability differentially drive the evolution of antibiotic resistance and persistence"
<p>Data supporting </p> <p><strong>Antibiotic dose and nutrient availability differentially drive the evolution of antibiotic resistance and persistence </strong></p> <p>E. M. Windels, L. Cool, E. Persy, J. Swinnen, P. Matthay, B. Van den Bergh, T. Wenseleers, J. Michiels</p> <p>Version April 16th, 2024</p>
Antibiotic Resistance in Hospital Wastewater in West Africa: A Systematic Review and Meta-Analysis
Open the record for dataset details and reuse information.
GENOMIC INSIGHTS INTO THE GLOBAL EVOLUTION AND ANTIBIOTIC RESISTANCE OF THE MYCOBACTERIUM TUBERCULOSIS COMPLEX
Open the record for dataset details and reuse information.
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>
Data from: Collateral sensitivity interactions between antibiotics depend on local abiotic conditions
<p>Mutations conferring resistance to one antibiotic can increase (cross resistance) or decrease (collateral sensitivity) resistance to others. Antibiotic combinations displaying collateral sensitivity could be used in treatments that slow resistance evolution. However, lab-to-clinic translation requires understanding whether collateral effects are robust across different environmental conditions. Here, we isolated and characterized resistant mutants of <i>Escherichia coli</i> using five antibiotics, before measuring collateral effects on resistance to other paired antibiotics. During both isolation and phenotyping, we varied conditions in ways relevant in nature (pH, temperature, bile). This revealed local abiotic conditions modified expression of resistance against both the antibiotic used during isolation and other antibiotics. Consequently, local conditions influenced collateral sensitivity in two ways: by favouring different sets of mutants (with different collateral sensitivities), and by modifying expression of collateral effects for individual mutants. These results place collateral sensitivity in the context of environmental variation, with important implications for translation to real-world applications.</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>
Role of internal loop dynamics in antibiotic permeability of outer membrane porins
<p>Representative structures for Markov State models for wild type and mutants of OmpF "Coupling of internal loop dynamics and antibiotic permeation in outer membrane porins"</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>
Single-cell phenotypic characteristics of tolerance under recurring antibiotic exposure in Escherichia coli
<p>Non-heritable drug resistance, such as tolerance and persistence towards antibiotics, is little characterized compared to genetic resistance. Tolerance and persistence allow cells to survive application of antibiotics that are bactericidal to non-tolerant cells. Non-heritable drug resistance challenges antibiotic treatment, particularly of recurrent infections, and have implications towards heritable resistance evolution. Tolerant cells have commonly been characterized as growth arrested cells prior and during antibiotic application that quickly resume growth post-application. Here, we explore characteristic of tolerant and susceptible <em>E. coli</em> single bacteria cells to different levels of recurrent antibiotic exposure and quantify their occurrence. In using a high throughput single-cell microfluidic device, we find that tolerant cells reduce their growth rate by about 50%, but contrary to previous findings do not go into growth arrest or near growth arrest. The growth reduction is induced by antibiotic exposure and not caused by a stochastic switch or predetermined state as previously described. Cells exhibiting constant intermediate growth survived best under antibiotic exposure and selection did not primarily act on fast growing cells, as expected for a β-lactam antibiotic. Control experiments on population cultures confirmed and challenged scaling of single cell findings to population level processes. Our findings suggest a prevalent type of tolerance that differs from previously described tolerance and persister characteristics. Our described characteristics and its high frequency of occurrence supports acclaims of an underappreciated role of tolerant cells towards resistance evolution.</p>
Off-target integron activity leads to rapid plasmid compensatory evolution in response to antibiotic selection pressure
Integrons are mobile genetic elements that have played an important role in the dissemination of antibiotic resistance. As shown previously (Souque et al, 2021), the integron can generate under stress combinatorial variation in resistance cassette expression by cassette re-shuffling, accelerating the evolution of resistance. However, the flexibility of the integron integrase site recognition motif hints at potential off-target effects of the integrase on the rest of the genome that may have important evolutionary consequences. Here we test this hypothesis by selecting for increased piperacillin resistance populations of <em>P.aeruginosa</em> with a mobile integron containing a hard-to-mobilise beta-lactamase cassette to minimize the potential for adaptive cassette re-shuffling. We found that integron activity can both decrease overall survival rate but also improve the fitness of the surviving populations. Off-target inversions mediated by the integron accelerated plasmid adaptation by disrupting costly conjugative genes otherwise mutated in control populations lacking a functional integrase. Plasmids containing integron-mediated inversions were associated with lower plasmid costs and higher stability than plasmids carrying mutations, albeit at a cost of reduced conjugative ability. These findings highlight the potential for integrons to create structural variation that can drive bacterial evolution, and they provide an interesting example showing how antibiotic pressure can drive the loss of conjugative genes.
Dataset for Conformational dynamics of loop L3 in OmpF: Implications towards antibiotic translocation and voltage gating
<p>Dataset supporting the work in the publication titled, "Conformational dynamics of loop L3 in OmpF: Implications towards antibiotic translocation and voltage gating".</p>
Dataset - Antibiotic persistence of intracellular Brucella abortus
<p>Dataset for the manuscript "Antibiotic persistence of intracellular <em>Brucella abortus</em>" published at PLoS Neglected Tropical Diseases, July 2022. ( <a href="https://doi.org/10.1371/journal.pntd.0010635">https://doi.org/10.1371/journal.pntd.0010635</a>).</p> <p>Data corresponding to each main figure are in separate .zip folders. Metadata compiled in a separated file.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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