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.bam alignment files of Illumina and ONT sequencing of pREF plasmid
<p>The expression of genes encompasses their transcription into mRNA followed by translation into protein. In recent years, next-generation sequencing and mass spectrometry methods have profiled DNA, RNA and protein abundance in cells. However, there are currently no reference standards that are compatible across these genomic, transcriptomic and proteomic methods, and provide an integrated measure of gene expression. Here, we use synthetic biology principles to engineer a multi-omics control, termed <em>pREF</em>, that can act as a universal molecular standard for next-generation sequencing and mass spectrometry methods. The <em>pREF</em> sequence encodes 21 synthetic genes that can be <em>in vitro</em> transcribed into spike-in mRNA controls, and <em>in vitro</em> translated to generate matched protein controls. The synthetic genes provide qualitative controls that can measure sensitivity and quantitative accuracy of DNA, RNA and peptide detection. We demonstrate the use of <em>pREF</em> in metagenome DNA sequencing and RNA sequencing experiments and evaluate the quantification of proteins using mass spectrometry. Unlike previous spike-in controls, <em>pREF</em> can be independently propagated and the synthetic mRNA and protein controls can be sustainably prepared by recipient laboratories using common molecular biology techniques. Together, this provides the first universal synthetic standard able to integrate genomic, transcriptomic and proteomic methods.</p>
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: Chicken gut microbiome members limit the spread of an antimicrobial resistance plasmid in Escherichia coli
<p>Plasmid-mediated antimicrobial resistance is a major contributor to the spread of resistance genes within bacterial communities. Successful plasmid spread depends upon a balance between plasmid fitness effects on the host and rates of horizontal transmission. While these key parameters are readily quantified in vitro, the influence of interactions with other microbiome members is largely unknown. Here, we investigated the influence of three genera of lactic acid bacteria (LAB) derived from the chicken gastrointestinal microbiome on the spread of an epidemic narrow-range ESBL resistance plasmid, IncI1 carrying <em>bla<sub>CTX-M-1</sub></em>, in mixed cultures of isogenic <em>Escherichia coli </em>strains. Secreted products of LAB decreased <em>E. coli</em> growth rates in a genus-specific manner but did not affect plasmid transfer rates. Importantly, we quantified plasmid transfer rates by controlling for density-dependent mating opportunities. Parametrization of a mathematical model with our in vitro estimates illustrated that small fitness costs of plasmid carriage may tip the balance towards plasmid loss under growth conditions in the gastrointestinal tract. This work shows that microbial interactions can influence plasmid success and provides an experimental-theoretical framework for further study of plasmid transfer in a microbiome context.</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.
Plasmid Sequences for Brophy et al., 2022: Synthetic genetic circuits as a means of reprogramming plant roots
<p>Plasmid Sequences for Brophy et. al. 2022: Synthetic genetic circuits as a means of reprogramming plant roots</p>
Type IV-A3 CRISPR-Cas systems drive inter-plasmid conflicts by acquiring spacers in trans
<p>Plasmid-encoded type IV-A CRISPR-Cas systems lack an acquisition module, feature a DinG helicase instead of a nuclease, and form ribonucleoprotein complexes. Type IV-A3 systems are carried by conjugative plasmids that often harbor antibiotic resistance genes. Their CRISPR array contents suggest a role in inter-plasmid conflicts, but this function remains unexplored. Here, we demonstrate that a plasmid-encoded type IV-A3 system co-opts the type I-E adaptation machinery from its host, Klebsiella pneumoniae, to update its CRISPR array. Furthermore, we reveal that robust interference of conjugative plasmids and phages is elicited through CRISPR RNA-dependent transcriptional repression. By silencing plasmid core functions, type IV-A3 impacts the horizontal transfer and stability of targeted plasmids, supporting its role in plasmid competition. Our findings shed light on the mechanisms and ecological function of type IV-A3 systems and demonstrate their practical efficacy for countering antibiotic resistance in clinically relevant strains.</p>
Spatial Mapping of Mobile Genetic Elements and their Cognate Hosts in Complex Microbiomes - Identifying the host taxon of a previously undescribed plasmid
<p>We investigated the taxonomic association of an unknown plasmid within a plaque biofilm of a patient diagnosed with stage 3 periodontitis. We combined long- and short- read sequencing to identify a complete plasmid with minimal homology to any sequence in the RefSeq database. The plasmid carried several predicted genes for mobilization and toxin-antitoxin systems. We designed MGE-FISH probes for the plasmid and combined this MGE-FISH stain with an 18-genera HiPR-FISH panel.</p> <p>Images are labeled by collection time such that the laser order for a given field of view (fov) is: 488nm Lambda, 514nm Lambda, 561nm Lambda, 633nm Airyscan, 405nm Lambda. We used Flye (https://github.com/fenderglass/Flye) to assemble the plasmid using long read Nanopore sequencing only and we used OPERA-MS (https://github.com/CSB5/OPERA-MS) to do hybrid assembly with Illumina short reads and Nanopore long reads. The assemblies are in the fasta files and the reads that map to the assemblies are in the fastq files. </p>
Human intestinal Bacteria Collection (HiBC): Plasmids sequences
<p>The <a href="https://hibc.rwth-aachen.de/" target="_blank" rel="noopener">Human intestinal Bacteria Collection (HiBC)</a> is a collection of bacterial strains, isolated from the human gut for which 16S rRNA gene sequences, genome sequences and culture conditions are made available to the research community. In addition to previously described bacteria, we include strains that represent novel species which have been taxonomically described and validly named, or will be in the future. This collection will be updated regularly.</p> <p>This dataset includes the plasmids sequences of some of the isolates in the FASTA nucleotide format.</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>
PlasEval: a framework for comparing and evaluating plasmid binning tools
<p>PlasEval is a tool aimed at evaluating the accuracy and at comparing methods for the problem of <strong>plasmid binning</strong>. It has two modes: <strong>evaluate</strong> and <strong>compare</strong>.</p> <p>In the <strong>evaluation</strong> mode of PlasEval, a given set of <em>predicted plasmid bins</em> resulting from a plasmid binning method is evaluated against a <em>ground truth</em> set of plasmid bins, yielding three statistics, the <em>precision</em>, the <em>recall</em> and the <em>F1-score</em>.</p> <p>In the <strong>comparison</strong> mode of PlasEval, given two sets of <em>predicted plasmid bins</em> (either resulting from two plasmid binning tools or from a plasmid binning tool and a ground truth), PlasEval computes a <em>dissimilarity measure</em> that indicates how much both sets of predicted plasmid bins are in agreement.</p> <p>The data shared consists of output files of PlasBin-flow as well as those of 3 other plasmid binning methods, namely, HyAsP, MOB-recon and gplas for 53 test samples. Details about each sample have been provided in the file <strong>Ecoli_samples.csv</strong>. It also includes the short read assembly files (fasta as well as gfa) and the hybrid assembly files. The mappings of short-read contigs to hybrid contigs for all 53 samples have been provided in the <strong>BLAST_output </strong>folder. The plasmid bins, including those representing the ground truth, used as input for PlasEval have been provided in the <strong>PlasEval_input</strong> folder.</p> <p> </p>
Supplementary File S2: Plasmids for independently tunable, low-noise gene expression
<p>Supplementary File S2 README</p> <p>2019-April-26</p> <p>"Plasmids for independently tunable, low-noise gene expression" (Version 2)</p> <p>João P. N. Silva, Soraia Vidigal Lopes, Diogo J. Grilo, Zach Hensel</p> <p>This file describes the contents of the supplementary file for this manuscript. Python scripts were run in a Python 3 environment on OSX with various scientific python packages updated as of April 2019. With minor modifications for any similar environment it should be possible to generate Figures 1, 2, and 4 in the manuscript from these scripts and raw data.</p> <p>Contents:</p> <p>\dna sequences<br> \pDG101.gb Annotated DNA sequence in genbank format of plasmid pDG101<br> \pJS101.gb Annotated DNA sequence in genbank format of plasmid pJS101 (AddGene #118280)<br> \pJS102.gb Annotated DNA sequence in genbank format of plasmid pJS102 (AddGene #118281)<br> \pZH501.gb Annotated DNA sequence in genbank format of plasmid pZH501<br> \pZH509.gb Annotated DNA sequence in genbank format of plasmid pZH509 (AddGene #102664)<br> \pZH713.gb Annotated DNA sequence in genbank format of plasmid pZH713<br> \ZHX99.gb Annotated DNA sequence in genbank format for E. coli MG1655 chromosome insertion mutant ZHX99<br> <br> \data<br> \DG FCS Data: Raw flow cytometry data from BioRad S3 sorted by day and experimental condition; file name format: plasmid_inducer-concentration_inducer-units_inducer.fcs<br> \pJS101 pDG101 independence<br> \"quick scope intensity.ijm" Fiji macro used to extract average fluorescence intensities<br> \"Microscope Data\" Microscope data analyzed using the above Fiji macro; directory names indicate ATc and IPTG concentrations for each experimentation condition/replicate<br> \"intensity analysis\"<br> X_nM_ATc_Y_uM_IPTG.csv files: Exported CSV data from Fiji for each condition<br> backgrounds.csv: Average intensity for every condition, image frame, and color for background subtraction<br> \"images for Figure 4": Image stack with raw images used to generate Fig 4A and 4B</p> <p>\code<br> \fcsAnalysis_final_181228.py step-wise script for generating Figures 1 and 2 from FCS data<br> \fcsCalcDirectory181228.py script containing functions for FCS analysis and figure generation<br> \fcsImages PDF figures output by FCS analysis scripts<br> \FlowCal-master Distribution of the FlowCal library used in analysis for this manuscript; this is distributed under the MIT license<br> \scopeAnalysisWorkflow190430.py step-wise script for generating Figures 4C and 4D from cell fluorescence microscopy data in CSV format exported from Fiji<br> \scopeCalcDirectory190430.py script containing functions for microscopy data analysis and figure generation</p>
plasmid_masking:v24.8.20
<p>kraken2 DB for plasmid built with masking option in Aug 2024. Contains 93587 accession numbers corresponding to 6804 taxons.</p>
Datasets of protein models from plasmids containing conjugative Type 4 Secretion Systems
<p>In the connected article, we have created a database of all modelled protein structures encoded on plasmids that contain conjugative type 4 secretion systems. In this deposition, you will find zip files of all structures modelled by AlphaFold, as well as the ones that were modelled using EMS fold. Further, there the csv file containing the DeepFRI output, as well as a fasta file containing the sequences of the plasmids.</p> <p>The AlphaFold and ESM databases contain the structural models of the curated/triaged proteins, as described in the paper.</p> <p> </p>
Raw data for whole plasmid and whole genome sequencing
<p>Original data for plasmid and genomic DNA sequencing in the paper: Tailoring Microbial Fitness Through Computational Steering and CRISPRi-Driven Robustness Regulation</p>
Multiplexed long-read plasmid validation and analysis using OnRamp
<p>Plasmid read data and references from "Multiplexed long-read plasmid validation and analysis using OnRamp "</p> <p>Experiments:</p> <ol> <li>AAZ605 - 7 plasmids</li> <li>AFQ178 - 9 plasmids</li> <li>ACK577 - 30 plasmids</li> <li>AEZ576 - 15 plasmids</li> <li>plasmids_ref_7.fasta</li> <li>plasmids_ref_9.fasta</li> <li>plasmids_ref_15.fasta</li> <li>plasmids_ref_30.fasta</li> </ol> <p> </p>
Spatial structure and benefits to hosts allow plasmids with and without post-segregational killing (PSK) systems to coexist
<p>To persist a plasmid relies on being passed on to a daughter cell, but this does not always occur. Plasmids with post-segregational killing (PSK) systems kill a daughter cell if it has not been passed on. By killing the host, it also kills competing plasmids in the same host, something competing plasmids without a similar system cannot do. Accordingly, plasmids with PSK systems can displace other plasmids. In nature, plasmids with and without PSK systems coexist and prior theory has suggested this is expected to be very rare or unstable, such that one or the other type of plasmid eventually takes over. Here, we show that if there is spatial structure and plasmids confer benefits to hosts, coexistence of plasmids occurs broadly. Often plasmids confer benefits (even ones with a PSK system) and bacteria are often spatially structured. So, our results may be generally applicable.</p>
MinION plasmid deep long read sequencing for sequence verification
<p>We sequenced four plasmid constructs, each with a whole MinION flowcell, for use in developing and testing a sequence verification procedure. The resulting pipeline can sequence verify plasmid constructs, generating a consensus sequence with associated confidence of each base, adhering to strict acceptance criteria . The data contained herein are a subset of the complete data; 30 fast5 files for each plasmid, to be used as example data.</p> <p> </p> <p>datHL_001519: BCRxV.TF.1<br> datHL_001521: BCRxV.VSVG.1<br> datHL_001617: BCRxV.GagPolRev.1<br> datHL_001620: BCRxV.VSVG.1_mutant</p>
A streamlined approach for fluorescence labelling of low copy-number plasmids for determination of conjugation frequency by flow cytometry
<p><span>Bacterial conjugation plays a major role in the dissemination of antibiotic resistance and virulence traits through horizontal transfer of plasmids. </span>Robust <span>measurement</span> of<span> the conjugation frequency of plasmids between bacterial strains and species </span>is therefore important <span>to understand the transfer dynamics </span>and epidemiology <span>of conjugative plasmids. In this study, we present a streamlined experimental approach for fluorescence labelling of low copy-number conjugative plasmids that allows plasmid transfer frequency during filter mating to be measured by flow cytometry. A blue fluorescence gene is inserted into a conjugative plasmid of interest using a simple homologous recombineering procedure. </span><span>A small non-conjugative plasmid, which carries a red fluorescence gene with a toxin-antitoxin system that functions as a plasmid stability module, is used to label the recipient bacterial strain. This offers the dual advantage of circumventing chromosomal modifications of recipient strains and ensuring that the red fluorescence gene-bearing plasmid can be stably maintained in recipient cells in an antibiotic-free environment during conjugation. A strong constitutive promoter allows the two fluorescence genes to be strongly and constitutively expressed from the plasmids, thus allowing flow cytometers to clearly distinguish between donor, recipient and transconjugant populations in a conjugation mix for monitoring conjugation frequencies more precisely over time. </span></p>
Listeria plasmid data
<p>Data for analysis of Listeria plasmids</p> <p> </p> <p>Assemblies were downloaded from NCBI and analyzed with the MOB-suite. The MOB-recon results for all of the assemblies is available “mob.recon.tar.gz” and the original assemblies can be reconstructed by “cat {acs}/*.fasta > {acs}.fasta”. It is roughly 45GB in size, so to save extra downloading issues it is provided as just the MOB-recon results. In order for the data to be uploaded to zenodo, the tarball had to be split into smaller-sized files. So “mob.results.tar.gz” is split into 2GB files with the suffix {part[A-V]}. The tarball can be reconstructed by doing “cat mob.recon.tar* > mob.results.tar.gz”.</p> <p> </p> <p>On the original assemblies, the samples were typed according to the 7-gene MLST scheme using mlst (<a href="https://github.com/tseemann/mlst">https://github.com/tseemann/mlst</a>) v2.23.0 and the lineage, and CC added based on the table provided by the Pasteur institute</p> <p> </p> <p>Using abricate v1.0.0 (<a href="https://github.com/tseemann/abricate">https://github.com/tseemann/abricate</a>) I have performed gene finding for BacMet db (<a href="https://github.com/jrober84/bacmet">https://github.com/jrober84/bacmet</a>) “merged.bacmet.txt “, VFDB “merged.vfdb.txt”. </p> <p> </p> <p>Chewbbaca cgMLST Allele calls for each genome and a profile of all of the allele calls is provided "chew.alleles.profiles.txt.gz". </p> <p> </p> <p>Sample Metadata master table “ListeriaSampleManifest.xlsx”, there are samples which are not in pathogen detection and so do not have additional metadata associated with them. The metadata would need to be extracted from the SRA. This also needs to be standardized and cleaned up for analysis.</p>
Available plasmids used in year 1 of ASAP project 2 - Target Enablement.
<p>Collated plasmids used for the ASAP project 2 drug discovery platform in year 1. Details of insert sequence and vector backbone included. These plasmids will be deposited into Addgene shortly.</p> <p>Plasmids will be accessible for use by all. </p> <p>Plasmids used in successful crystal screens to date are highlighted on the sheet.</p> <p>Up to date information on the progress made by the ASAP project 2 team can be found here: https://asapdiscovery.org/outputs/target-enabling-packages/</p> <p> </p> <p> </p> <p>Research reported here was supported in part by NIAID of the National Institutes of Health under award number U19AI171399.The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health</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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.