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45 results for “Antibiotic resistance genes”
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.
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>
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>
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>
Data from: Costs of antibiotic resistance genes depend on host strain and environment and can influence community composition
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>
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>
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>
Data from: Chiral pesticides selectively influence the dissemination of antibiotic resistance genes: An overlooked environmental risk
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Data from: Fitness benefits to bacteria of carrying prophages and prophage-encoded antibiotic-resistance genes peak in different environments
<p>Understanding the role of horizontal gene transfer (HGT) in adaptation is a key challenge in evolutionary biology. In microbes, an important mechanism of HGT is prophage acquisition (phage genomes integrated into bacterial chromosomes). Prophages can influence bacterial fitness via transfer of beneficial genes (including antibiotic-resistance genes, ARGs), protection from superinfecting phages, or switching to a lytic lifecycle which releases free phages infectious to competitors. We expect these effects to depend on environmental conditions because of, for example, environment-dependent induction of the lytic lifecycle. However, it remains unclear how costs/benefits of prophages vary across environments. Here, studying prophages with/without ARGs in <i>Escherichia coli</i>, we disentangled effects of prophages alone and adaptive genes they carry. In competition with prophage-free strains, benefits from prophages and ARGs peaked in different environments. Prophages were most beneficial when induction of the lytic lifecycle was common, whereas ARGs were more beneficial upon antibiotic exposure and with reduced prophage induction. Acquisition of prophage-encoded ARGs by competing strains was most common when prophage induction, and therefore free phages, were common. Thus, selection on prophages and adaptive genes they carry varies independently across environments, which is important for predicting the spread of mobile/integrating genetic elements and their role in evolution</p>
Data from: Aquatic animals promote antibiotic resistance gene dissemination in water via conjugation: role of different regions within the zebra fish intestinal tract, and impact on fish intestinal microbiota
The aqueous environment is one of many reservoirs of antibiotic resistance genes (ARGs). Fish, as important aquatic animals which possess ideal intestinal niches for bacteria to grow and multiply, may ingest antibiotic resistance bacteria from aqueous environment. The fish gut would be a suitable environment for conjugal gene transfer including those encoding antibiotic resistance. However, little is known in relation to the impact of ingested ARGs or antibiotic resistance bacteria (ARB) on gut microbiota. Here, we applied the cultivation method, qPCR, nuclear molecular genetic marker and 16S rDNA amplicon sequencing technologies to develop a plasmid-mediated ARG transfer model of zebrafish. Furthermore, we aimed to investigate the dissemination of ARGs in microbial communities of zebrafish guts after donors carrying self-transferring plasmids that encode ARGs were introduced in aquaria. On average, 15% of faecal bacteria obtained ARGs through RP4-mediated conjugal transfer. The hindgut was the most important intestinal region supporting ARG dissemination, with concentrations of donor and transconjugant cells almost 25 times higher than those of other intestinal segments. Furthermore, in the hindgut where conjugal transfer occurred most actively, there was remarkable upregulation of the mRNA expression of the RP4 plasmid regulatory genes, trbBp and trfAp. Exogenous bacteria seem to alter bacterial communities by increasing Escherichia and Bacteroides species, while decreasing Aeromonas compared with control groups. We identified the composition of transconjugants and abundance of both cultivable and uncultivable bacteria (the latter accounted for 90.4%–97.2% of total transconjugants). Our study suggests that aquatic animal guts contribute to the spread of ARGs in water environments.
DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data
<p>Database and models for deepARG: </p> <p>see this link for details: <a href="https://bitbucket.org/gusphdproj/deeparg-ss/src/fbe063e24cf79d83a88499353aa15a85b58a300e/?at=master">gusphdproj / deeparg-ss — Bitbucket</a></p>
Antibiotics Resistance Gene in Healthcare Workers
ClinicalTrials.gov study NCT06228248. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: Wastewater pollution differently affects the antibiotic resistance gene pool and biofilm bacterial communities across streambed compartments
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Data from: Fitness benefits to bacteria of carrying prophages and prophage-encoded antibiotic-resistance genes peak in different environments
Open the record for dataset details and reuse information.
Data from: Aquatic animals promote antibiotic resistance gene dissemination in water via conjugation: role of different regions within the zebra fish intestinal tract, and impact on fish intestinal microbiota
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Data for: Longitudinal metatranscriptomic sequencing of Southern California wastewater representing 16 million people from August 2020-21 reveals widespread transcription of antibiotic resistance genes
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Data from: Phylogenetic relatedness determined between antibiotic resistance and 16S rRNA genes in actinobacteria
Background: Distribution and evolutionary history of resistance genes in environmental actinobacteria provide information on intensity of antibiosis and evolution of specific secondary metabolic pathways at a given site. To this day, actinobacteria producing biologically active compounds were isolated mostly from soil but only a limited range of soil environments were commonly sampled. Consequently, soil remains an unexplored environment in search for novel producers and related evolutionary questions. Results: Ninety actinobacteria strains isolated at contrasting soil sites were characterized phylogenetically by 16S rRNA gene, for presence of erm and ABC transporter resistance genes and antibiotic production. An analogous analysis was performed in silico with 246 and 31 strains from Integrated Microbial Genomes (JGI_IMG) database selected by the presence of ABC transporter genes and erm genes, respectively. In the isolates, distances of erm gene sequences were significantly correlated to phylogenetic distances based on 16S rRNA genes, while ABC transporter gene distances were not. The phylogenetic distance of isolates was significantly correlated to soil pH and organic matter content of isolation sites. In the analysis of JGI_IMG datasets the correlation between phylogeny of resistance genes and the strain phylogeny based on 16S rRNA genes or five housekeeping genes was observed for both the erm genes and ABC transporter genes in both actinobacteria and streptomycetes. However, in the analysis of sequences from genomes where both resistance genes occurred together the correlation was observed for both ABC transporter and erm genes in actinobacteria but in streptomycetes only in the erm gene. Conclusions: The type of erm resistance gene sequences was influenced by linkage to 16S rRNA gene sequences and site characteristics. The phylogeny of ABC transporter gene was correlated to 16S rRNA genes mainly above the genus level. The results support the concept of new specific secondary metabolite scaffolds occurring more likely in taxonomically distant producers but suggest that the antibiotic selection of gene pools is also influenced by site conditions.
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