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338 results for “antibiotic resistance”
Global eutrophication and antibiotic resistance genes dataset for "Coupling mechanisms between cyanobacteria and antibiotic resistance genes in freshwater ecosystems"
This dataset compiles global records of cyanobacteria, antibiotic resistance genes (ARGs), and associated water quality parameters to support research on freshwater ecosystem dynamics. It includes 990 metagenomes, 16,648 chlorophyll-a (Chl-a) records, and over 90 documented cases of ARGs–cyanobacteria co-occurrence under comparable spatiotemporal conditions. The dataset covers the years 2000–2024 and provides both raw measurements and harmonized tables for cross-study comparisons. Data were extracted from previously published literature and public repositories, with references to source publications included. This archive is intended to facilitate reproducible analyses, enable large-scale meta-studies, and support further exploration of microbial interactions in freshwater systems.
Antibiotic resistant pathogen outbreak investigation: an interdisciplinary module to teach fundamentals of evolutionary biology
<p>The evolution of resistance to antibiotics provides a timely and relevant topic for teaching undergraduate students evolutionary biology. Here, we present a module incorporating modified sequencing data from eight antibiotic resistant pathogen outbreaks in hospital settings with bioinformatics and phylogenetic analyses. This module uses whole genome sequencing data from hospital outbreaks investigated by the Centers for Disease Control and Prevention to provide examples of antibiotic resistance spread. Students work in groups to analyze outbreak data to identify the bacterial species and antibiotic resistance genes, to infer a phylogenetic tree examining relatedness among isolates, and to determine a possible source of the outbreak. Students then compile their results in individual reports and provide recommendations for preventing the further spread of antibiotic resistant organisms. In addition to providing genomic outbreak data, we include a teaching concepts guide discussing three integral components of the module: how evolutionary biology concepts of natural selection and competition impact antibiotic resistance; outbreak investigation information to aid in phylogenetic analysis and creation of recommendations; and instructions for the bioinformatics protocol. Completion of this module provides students an opportunity to think critically about the evolution of resistance, practice bioinformatics techniques, and relate evolutionary biology to current events.</p>
Row sequcenes data for assessing the risks of potential pathogens and antibiotic resistance genes among heterogeneous habitats in a temperate estuary wetland
<p>The study included 118 usable samples within three different habitats (water, soil, and sediment) across the Liaohe River basin to the Red Beach wetland collected from seven papers, and all of the sequence files were uploaded for availability.</p>
Putative mobilized colistin resistance (mcr) genes co-occurring with other antibiotic resistance genes are widespread in the human gut microbiome
<p><strong>The dataset from the article </strong><strong>Putative mobilized colistin resistance (mcr) genes co-occurring with other antibiotic resistance genes are widespread in the human gut microbiome</strong></p>
SUPPLEMENTARY (For MD) An integrative pan-genome and subtractive proteomics approach for the identification of potential novel therapeutic drug target against antibiotic resistant honeybee pathogen Paenibacillus larvae
<p><strong>Parameters</strong></p><p>Force field: AMBER ff19SB</p><p>Water type: TIP3P</p><p>Ions: NaCl </p><p>Ligand topology force field: GAFF2</p><p>Temperature: 298k</p><p>Pressure: 1 bar</p><p>minimization step: 20000 on 5 nanoseconds</p><p>initial velocity is changed by changing "ntx" and "ig"</p><p>C2: ntx = 5 , ig = 8</p><p>C3: ntx = 2 , ig = 5</p><p> </p><p><strong>Uploads</strong>- </p><p>1. Zip file of all 3 main files</p><p>2. Unzip file of C1 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>3. Zip file of C1</p><p>4. Unzip file of C2 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>5. Zip file of C2</p><p>6. Unzip file of C3 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>7. Zip file of C3</p><p>8. Zip and unzip file of <strong>Initial</strong> PDB of complex prior to MD simulation with <strong>Post</strong> MD PDB (C1, C2, C3)</p><p>9. Zip file of <strong>topology</strong> files for C1, C2, and C3</p>
Data from: The impact of long-term azithromycin on antibiotic resistance in HIV-associated chronic lung disease
<p><b>Background</b>: Selection for resistance to azithromycin (AZM) and other antibiotics such as tetracyclines and lincosamides remains a concern with long-term AZM use for treatment of chronic lung diseases (CLD). We investigated the impact of 48 weeks of AZM on the carriage and antibiotic resistance of common respiratory bacteria among children with HIV-associated CLD.</p> <p><b>Methods</b>: Nasopharyngeal (NP) swabs and sputa were collected at baseline, 48 and 72 weeks from participants with HIV-associated CLD randomised to receive weekly AZM or placebo for 48 weeks and followed post-intervention until 72 weeks. The primary outcomes were prevalence and antibiotic resistance of <i>Streptococcus pneumoniae</i> (SP), <i>Staphylococcus aureus </i>(SA), <i>Haemophilus influenzae </i>(HI), and <i>Moraxella catarrhalis </i>(MC) at these timepoints. Mixed-effects logistic regression and Fisher's exact test were used to compare carriage and resistance respectively.</p> <p><b>Results</b>: Of 347 (174 AZM, 173 placebo) participants (median age 15 years [IQR =13–18], females 49%),NP carriage was significantly lower in the AZM (n=159) compared to placebo (n=153) arm for SP (18% vs 41%, <i>p</i><0.001)<i>, </i>HI (7% vs 16%, p=0.01)<i>, </i>and MC (4% vs 11%, <i>p</i>=0.02); SP resistance to AZM (62% [18/29] vs 13%[8/63], <i>p</i><0.0001) or tetracycline (60%[18/29] vs 21%[13/63], <i>p</i><0.0001) were higher in the AZM arm. Carriage of SA resistant to AZM (91% [31/34] vs 3% [1/31],<i> p</i><0.0001), tetracycline (35% [12/34] vs 13% [4/31],<i> p</i>= 0.05) and clindamycin (79% [27/34] vs 3% [1/31],<i> p</i><0.0001) was also significantly higher in the AZM arm and persisted at 72 weeks. Similar findings were observed for sputa.</p> <p><b>Conclusions</b>: The persistence of antibiotic resistance and its clinical relevance for future infectious episodes requiring treatment needs further investigation.</p>
Insertion sequences and other mobile elements associated with antibiotic resistance genes in Enterococcus isolates from an inpatient with prolonged bacteremia.
<p>Insertion sequences (ISs) and other transposable elements are associated with the mobilization of antibiotic resistance determinants and the modulation of pathogenic characteristics. In this work, we aimed to investigate the association between ISs and antibiotic resistance genes, and their role in dissemination and modification of the antibiotic resistant phenotype. To that end, we leveraged fully resolved <em>Enterococcus faecium</em> and <em>Enterococcus faecalis</em> genomes of isolates collected over five days from an inpatient with prolonged bacteremia. Isolates from both species harbored similar IS family content but showed significant species-dependent differences in copy number and arrangements of ISs throughout their replicons. Here, we describe two inter-specific IS-mediated recombination events and IS-mediated excision events in plasmids of <em>E. faecium</em> isolates. We also characterize a novel arrangement of the ISs in a Tn1546-like transposon in <em>E. faecalis</em> isolates likely implicated in a vancomycin genotype-phenotype discrepancy. Furthermore, an extended analysis revealed a novel association between daptomycin resistance mutations in <em>liaSR</em> genes and a putative composite transposon in<em> E. faecium</em>, offering a new paradigm for the study of daptomycin resistance and novel insights into the dissemination of daptomycin resistance. In conclusion, our study highlights the role ISs and other transposable elements play in the rapid adaptation and response to clinically relevant stresses such as aggressive antibiotic treatment in enterococci.</p>
Data from: Costs of antibiotic resistance genes depend on host strain and environment and can influence community composition
<p>Antibiotic resistance genes (ARGs) benefit host bacteria in environments containing corresponding antibiotics, but it is less clear how they are maintained in environments where antibiotic selection is weak or sporadic. In particular, few studies have measured the effect of ARGs on host fitness in the absence of direct selection or determined if any costs are fixed or depend on the host strain, perhaps marking some ARG-host combinations as reservoirs that can maintain ARGs in the absence of antibiotic selection. We quantified the fitness effects of six ARGs in 11 diverse <em>Escherichia spp</em>. strains. Three ARGs (blaTEM-116, cat, and dfrA5, encoding resistance to β-lactams, chloramphenicol, and trimethoprim, respectively) imposed an overall cost but all ARGs had an effect in at least one host strain, reflecting a significant strain interaction effect. A simulation predicts these interactions cause the success of ARGs to depend on available host strains, and, to a lesser extent, for successful host strains to depend on the ARGs present in a community. These results indicate the importance of considering ARG effects over different host strains, especially the potential of reservoir strains that allow resistance to persist in the absence of direct selection, in efforts to understand resistance dynamics.</p>
Insights into Acinetobacter baumannii AMA205's Unprecedented Antibiotic Resistance
<p><span>The rise of antibiotic-resistant bacteria in clinical settings has become a significant global concern. Among these bacteria, <em>Acinetobacter baumannii</em> stands out due to its remarkable ability to acquire resistance genes and persist in hospital environments, leading to some of the most challenging infections. Horizontal gene transfer (HGT) plays a crucial role in the evolution of this pathogen. The <em>A. baumannii</em> AMA205 strain, belonging to sequence type ST79, was isolated from a COVID-19 patient in Argentina in 2021. This strain’s antimicrobial resistance profile is notable as it harbors multiple resistance genes, some of which had not been previously described in this species. The AmpC family β-lactamase <em>bla</em><sub>CMY-6</sub>, commonly found in Enterobacterales, had never been detected in <em>A. baumannii </em>before. Furthermore, this is the first ST79 strain known to carry the carbapenemase <em>bla</em><sub>NDM-1 </sub>gene. Other acquired resistance genes include the carbapenemase <em>bla</em><sub>OXA-23</sub>, further complicating treatment. Susceptibility testing revealed high resistance to most antibiotic families, including cefiderocol, with significant contributions from <em>bla</em><sub>CMY-6 </sub>and <em>bla</em><sub>NDM-1 </sub>genes to the cephalosporin and carbapenem resistance profiles. The <em>A. baumannii</em> AMA205 genome also contains genetic traits coding for 111 potential virulence factors, such as the iron-uptake system and biofilm-associated proteins. This study underscores <em>A. baumannii's </em>ability to acquire multiple resistance genes and highlights the need for alternative therapies and effective antimicrobial stewardship to control the spread of these highly resistant strains.</span></p>
DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data
<p>Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the “best hits” of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models’ performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The DeepARG models and database are available as a command line version and as a Web service at <a href="http://bench.cs.vt.edu/deeparg">http://bench.cs.vt.edu/deeparg</a>.</p>
Kinetic in support of "Pre-Steady-State Kinetic Characterization of an Antibiotic-Resistant Mutant of Staphylococcus aureus DNA Polymerase PolC"
<p>These are KinTek Explorer mechanism files containing the data and analysis described in the manuscript "Pre-Steady-State Kinetic Characterization of an Antibiotic-Resistant Mutant of Staphylococcus aureus DNA Polymerase PolC" (bioRxiv 2022.10.04.510889; doi: https://doi.org/10.1101/2022.10.04.510889). </p>
Multispecies coinfections and presence of antibiotics shape resistance and fitness costs in a pathogenic bacterium
<p>Increasing antimicrobial resistance (AMR) poses a challenge for treatment of bacterial diseases. In real life, bacterial infections are typically <span>embedded within complex multispecies communities and influenced by the environment, which can shape </span>costs and benefits of AMR. However, knowledge of such interactions and their implications for AMR <em>in vivo</em> is limited. <span>To address this knowledge gap, we investigated fitness-related traits of a pathogenic bacterium (</span><em>Flavobacterium</em> <em>columnare</em><span>)</span> <span>in its fish host, capturing the effects of bacterial antibiotic resistance, multispecies coinfections</span> <span>(metazoan fluke </span><span><em>Diplostomum</em> <em>pseudospathaceum</em></span><span>), and antibiotic exposure. </span>We quantified real-time replication and virulence of sensitive and resistant bacteria and demonstrate that both bacteria can benefit from coinfection in terms of persistence and replication, depending on the coinfecting partner and antibiotic presence. We also show that antibiotics can benefit resistant bacteria by increasing bacterial replication under coinfection with flukes. These results emphasize the importance of diverse, inter-kingdom coinfection interactions and antibiotic exposure in shaping costs and benefits of AMR, supporting their role as significant contributors to the spread and long-term persistence of resistance.</p>
Multispecies coinfections and presence of antibiotics shape resistance and fitness costs in a pathogenic bacterium
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Data from: The impact of long-term azithromycin on antibiotic resistance in HIV-associated chronic lung disease
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Data from: Costs of antibiotic resistance genes depend on host strain and environment and can influence community composition
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Nano MOFs as targeted drug delivery agents to combat antibiotic resistant bacterial infections
<p>The drug resistance of bacteria is a significant threat to human civilization while the action of antibiotics against drug-resistant bacteria is severely limited due to the hydrophobic nature of drug molecules, which unquestionably inhibit its permanency for clinical applications. The antibacterial action of nanomaterials offers major modalities to combat drug resistance of bacteria. The current work reports, the use of nano MOFs encapsulating drug molecules to enhance its antibacterial activity against model drug-resistant free living bacteria and biofilm of the bacteria. We have attached rifampicin (RF), a well-documented antituberculosis drug with tremendous pharmacological significance, into the pore surface of zeolitic imidazolate framework 8 (ZIF8) by a <span><span>simple synthetic procedure</span></span><span>.</span> The synthesized ZIF8 has been characterized using X-ray diffraction (XRD) method before and after drug encapsulation. The electron microscopic strategies such as scanning electron microscope (SEM) and transmission electron microscope (TEM) methods was performed to characterize the binding between ZIF8 and RF. We have also performed picosecond resolved fluorescence spectroscopy to validate the formation of the ZIF8-RF nanohybrids (NHs). The drug release profile experiment demonstrates that ZIF8-RF depicts pH-responsive drug delivery and ideal for targeting bacterial disease corresponding to its inherent acidic nature. Most remarkably, ZIF8-RF gives enhanced antibacterial activity against methicillin-resistant <i>S. aureus</i> (MRSA) bacteria and also prompts entire damage of structurally robust bacterial biofilms. Overall, the present study depicts a detailed physical insight for manufactured antibiotic-encapsulated NHs presenting tremendous antimicrobial activity that can be beneficial for manifold practical applications.</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>
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>
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>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.