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569 results for “DNA replication”
Murine polyomavirus DNA transitions through spatially distinct nuclear replication subdomains during infection
<p>The replication of small DNA viruses requires both host DNA replication and repair factors that are often recruited to subnuclear domains termed viral replication centers (VRCs). Aside from serving as a spatial focus for viral replication, little is known about these dynamic areas in the nucleus. We investigated the organization and function of VRCs during murine polyomavirus (MuPyV) infection using 3D structured illumination microscopy (3D-SIM). We localized MuPyV replication center components, such as the viral large T-antigen (LT) and the cellular replication protein A (RPA), to spatially distinct subdomains within VRCs. We found that viral DNA (vDNA) trafficked sequentially through these subdomains post-synthesis, suggesting their distinct functional roles in vDNA processing. Additionally, we observed disruption of VRC organization and vDNA trafficking during mutant MuPyV infections or inhibition of DNA synthesis. These results reveal a dynamic organization of VRC components that coordinates virus replication.</p>
Mapping fast DNA polymerase exchange during replication
<h2>Abstract</h2> <p>Despite extensive studies on DNA replication, the exchange mechanisms of DNA polymerase during replication remain unclear. Existing models propose that this exchange is facilitated by protein partners like helicase. Here we present data, employing a combination of mechanical DNA manipulation and single fluorescent protein observation, that reveal DNA polymerase undergoing rapid and autonomous exchange during replication not coordinated by other proteins. The DNA polymerase shows fast unbinding and rebinding dynamics, displaying a preference for either exonuclease or polymerase activity, or pausing events, during each brief binding event. We also observed a 'memory effect' in DNA polymerase rebinding, i.e., the enzyme tends to preserve its prior activity upon reassociation. This effect, potentially linked to the ssDNA/dsDNA junction's conformation, might play a role in regulating binding preference enabling high processivity amidst rapid protein exchange. Taken together, our findings support an autonomous replication model that includes rapid protein exchange, burst of activity, and a 'memory effect' while moving processively forward. </p> <h2>Data Set Description</h2> <p>The study presents findings on the autonomous exchange mechanisms of DNA polymerase at the replication fork. Employing a combination of mechanical DNA manipulation and single fluorescent protein observation, the research reveals rapid and autonomous exchange of DNA polymerase, independent of protein partners like helicase. It highlights the enzyme's preference for exonuclease or polymerase activity and a 'memory effect' during binding events. In this data repo, we provided all the raw data used to reproduce the findings.</p> <h2>Methodology</h2> <p>The single-molecule experiments were performed at room temperature in a 5-channel microfluidic flow cell using the LUMICKS C-Trap instrument that combines dual optical trapping, confocal microscopy, and microfluidics for single-molecule assays. Data analysis was conducted using Python, Origin, and MATLAB. Detailed methodology and instrument specifications are provided in the associated publication.</p> <h2>File Formats</h2> <ul> <li> <p>All data is obtained from C-trap in .tdms format.</p> </li> </ul> <h2>Usage Notes</h2> <p>The custom-written python scripts used in this study is available at <a href="https://github.com/longfuxu/DNAPolymeraseProject">https://github.com/longfuxu/DNAPolymeraseProject</a>, under the MPL-2.0 license. The repository includes example dataset, example Jupiter notebook, along with a detailed README file for instructions on installation and usage.</p> <h2>Acknowledgments</h2> <p>We thank Seyda Aca and Sandrine D'Haene for assistance with protein purification and DNA construction, Noémie Danné for help with implementing the step-fitting algorithm. We thank Erwin Peterman for critical reading and constructive feedbacks of this manuscript. This work was financially supported by a PhD fellowship from China Scholarship Council (To L.X., funding No. 201704910912), the European Union H2020 Marie-Sklowdowska Curie International Training Network AntiHelix (To G.J.L.W., funding No. 859853), and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program MONOCHROME (to G.J.L.W., funding No.883240).</p> <h2>Competing Interest</h2> <p>The combined optical tweezers and fluorescence technologies used in this article are patented and licensed to LUMICKS B.V., in which M.T.J.H., and G.J.L.W. declare a financial interest. All other authors declare that they have no competing interests.</p> <h2>Author Contributions</h2> <p>L.X. and G.J.L.W. conceptualized the research. L.X. prepared protein samples and collected single-molecule data and analyzed data; M.T.J.H. analyzed data; L.X., M.T.J.H. and G.J.L.W. wrote and edited the manuscript; G.J.L.W. supervised the project; the manuscript is read, revised, and confirmed by all the listed authors.</p>
Dataset for Fig. 1d, Replicate 1 of DNA Nanopore Computing
<p>FAST5 files containing raw nanopore current data for the manuscript "A nanopore interface for higher bandwidth DNA computing".</p> <p>This set includes the data used for Replicate 1 of Fig. 1d.</p> <table> <thead> <tr> <th scope="col">File Name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>DESKTOP_CHF4GRO_20190328_FAK62104_MN21390_sequencing_run_03_28_19_run03_d.fast5</td> <td>Replicate 1, 0.02 uM</td> </tr> <tr> <td>DESKTOP_CHF4GRO_20190328_FAK62104_MN21390_sequencing_run_03_28_19_run03_f.fast5</td> <td>Replicate 1, 0.1 uM</td> </tr> <tr> <td>DESKTOP_CHF4GRO_20190328_FAK62104_MN21390_sequencing_run_03_28_19_run03_h.fast5</td> <td> <p>Replicate 1, 0.2 uM</p> </td> </tr> <tr> <td>DESKTOP_CHF4GRO_20190328_FAK62104_MN21390_sequencing_run_03_28_19_run03_j.fast5</td> <td>Replicate 1, 0.5 uM</td> </tr> <tr> <td>DESKTOP_CHF4GRO_20190328_FAK62104_MN21390_sequencing_run_03_28_19_run03_l.fast5</td> <td>Replicate 1, 1.0 uM</td> </tr> </tbody> </table> <p> </p>
Dataset for Fig. 4d, Replicate 1 of DNA Nanopore Computing
<p>FAST5 files containing raw nanopore current data for the manuscript "A nanopore interface for higher bandwidth DNA computing".</p> <p>This set includes the data used for Replicate 1 of Fig. 4d.</p> <table> <thead> <tr> <th scope="col">File Name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>DESKTOP_CHF4GRO_20210221_FAP42740_MN21390_sequencing_run_02_21_21_run01_b.fast5</td> <td>No circuits activated</td> </tr> <tr> <td>DESKTOP_CHF4GRO_20210221_FAP42740_MN21390_sequencing_run_02_21_21_run01_d.fast5</td> <td>Circuits 5 and 9 activated </td> </tr> <tr> <td>DESKTOP_CHF4GRO_20210221_FAP42740_MN21390_sequencing_run_02_21_21_run01_f.fast5</td> <td>Circuits 1, 7, and 8 activated</td> </tr> </tbody> </table> <p> </p>
Custom codes related to the publication "Mitigating transcription-replication conflicts: In early Drosophila embryos, rapid onset of transcription after mitosis depends on DNA replication"
<p>This dataset includes the custom codes related to the publication: Mitigating transcription-replication conflicts: In early Drosophila embryos, rapid onset of transcription after mitosis depends on DNA replication, Cell Reports 2022.</p> <p>The raw imaging data can be found at https://doi.org/10.5281/zenodo.7102432</p>
Linking the gut microbiome to host DNA methylation by a discovery and replication epigenome-wide association study
<p>BACKGROUND: The datafiles deposited here are products of the research project "<strong>Linking the gut microbiome to host DNA methylation by a discovery and replication epigenome-wide association study"</strong></p><p>Authors: Ayşe Demirkan1,2, Jenny van Dongen3,4, Casey T. Finnicum5, Harm-Jan Westra1, Soesma Jankipersadsing1, Gonneke Willemsen3,4, Richard G. Ijzerman6, Dorret I. Boomsma3,4, Erik A. Ehli5, Marc Jan Bonder1, Jingyuan Fu,1,7 Lude Franke1, Cisca Wijmenga1, Eco J.C. de Geus3,4, Alexander Kurilshikov1, Alexandra Zhernakova1</p><p>1 Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands</p><p>2 Section of Statistical Multi-omics, Department of Clinical and Experimental Medicine, School of Biosciences and Medicine & People-Centered AI institute University of Surrey, Guildford, United Kingdom</p><p>3 Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands</p><p>4 Amsterdam Public Health Research Institute, Amsterdam, the Netherlands</p><p>5 Avera Institute of Human Genetics, Avera McKennan Hospital & University Health Center, Sioux Falls, SD, USA</p><p>6 Department of Endocrinology, Amsterdam University Medical Center, location VUMC, Amsterdam, the Netherlands</p><p>7 Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands</p><p><strong>Corresponding Authors: </strong>Alexandra Zhernakova; Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands</p><p>Ayse Demirkan; Section of Statistical Multi-omics, Department of Clinical and Experimental Medicine, School of Biosciences and Medicine & People-Centered AI institute University of Surrey, Guildford, United Kingdom.</p><p>FILES: </p><p>1-merged_lld16s.Rata: Epigenome-wide association of 16s microbial abundances in LifeLines-Deep (LLD, n = 616, 450k methylation array) </p><p>2-lld_mgs.Rdata: Epigenome-wide association ofshotgun metagenomic sequencing derived taxa relative abundances (n = 683, 450k methylation array))</p><p>3-lld_<i>mgs_</i>pathways. Rdata: Epigenome-wide association ofshotgun metagenomic sequencing derived bacterial pathway relative abundances (n = 683, 450k methylation array)</p><p>FUNDING: The Lifelines initiative has been made possible by subsidy from the Dutch Ministry of Health, Welfare and Sport, the Dutch Ministry of Economic Affairs, the University Medical Center Groningen (UMCG), Groningen University and the Provinces in the North of the Netherlands (Drenthe, Friesland, Groningen). The Netherlands Twin Register acknowledges funding from the Netherlands Organization for Scientific Research (NWO): (NWO 911–09–032; NWO 480-04-004; 480-15-001/674, NWO 916-130-82), Biobanking and Biomolecular Research Infrastructure (184.033.111), and the BBRMI-NL-financed BIOS Consortium (NWO 184.021.007), NWO Large Scale infrastructures X-Omics (184.034.019), Genotype/phenotype database for behaviour genetic and genetic epidemiological studies (ZonMw Middelgroot 911-09-032); Netherlands Twin Registry Repository: researching the interplay between genome and environment (NWO-Groot 480-15-001/674); the Avera Institute, Sioux Falls (USA), the European Research Council (Genetics of Mental Illness 230374), the European Research Council (Genetics of Mental Illness 230374), and INRA-Pfizer. Pfizer provided support for data collection, but did not have any additional role in the study design, data analysis, decision to publish, or preparation of the manuscript.</p><p> </p>
Data Supporting The Paper 'Fine-tuned spatiotemporal dynamics of DNA replication during phage lambda infection'
<p>The dataset includes raw images, source code, raw vectors saved from MATLAB, and curated data used to generate figures and analyses in the paper <strong>'Fine-tuned spatiotemporal dynamics of DNA replication during phage lambda infection'</strong> by Z. Yu, et al.</p> <p>Additional information about the experiments will be available upon request. </p>
Changing protein-DNA interactions promote ORC binding site exchange during replication origin licensing
<p><strong>Changing protein-DNA interactions promote ORC binding site exchange during replication origin licensing.</strong></p> <p>Zhang, Annie, Massachusetts Institute of Technology, ORCID: 0000-0003-3939-2585</p> <p>DOI: 10.5281/zenodo.7814499</p> <p>Primary publication DOI: <a href="https://doi.org/10.1073/pnas.2305556120">https://doi.org/10.1073/pnas.2305556120</a></p> <p> </p> <p><strong>Folder Structure</strong></p> <p> </p> <p>Source data are organized based on the parent figures from the main text (Figs 1-6). Source data from supplementary figures associated are placed within the associated parent figure folder. All figures, their associated parent figures, and the experiment names from which these figures are derived are summarized in the 'Summary.xlsx' file.</p> <p> </p> <p><strong>File Formats</strong></p> <p><strong> </strong></p> <p>1. Integrated trace files are saved as .dat files.</p> <p> </p> <p>- These trace files are obtained by integrating the fluorescence intensity contained within each DNA spot, or Area of Interest (AOI), over the range of the experimental time frame. They can be read and viewed in Matlab or the Matlab program imscroll, which is publicly available: <a href="https://github.com/gelles-brandeis/CoSMoS_Analysis">https://github.com/gelles-brandeis/CoSMoS_Analysis</a>.</p> <p> </p> <p>- Naming of files:</p> <p>The experiment name is specified at the beginning of the file name.</p> <p>The excitation and emission fields are specified using the following abbreviations.</p> <p>Gex: Green excited (Donor excited)</p> <p>Rex: Red excited (Acceptor excited)</p> <p>GexRex: Green and red excited (Donor and acceptor excited)</p> <p>Gem: Green emission (Donor emission)</p> <p>Rem: Red emission (Acceptor emission)</p> <p> </p> <p> </p> <p>To read the .dat integrated trace files in Matlab the user may type: </p> <p>>>[fn fp] = uigetfile</p> <p>% use the dialog box to mouse click on the appropriate *.dat file, then type:</p> <p>>>eval(['load ' [fp fn] ' -mat'])</p> <p>% This loads an aoifits structure array into the Matlab command environment</p> <p> </p> <p>The integrated trace data is stored in the aoifits.data matrix. A description of the columns is in the aoifits.dataDescription.</p> <p> </p> <p>The first five columns within aoifits.data are the most relevant to view fluorescence emission intensities at individual AOIs over time.</p> <p>Column 1: aoinumber</p> <p>Column 2: framenumber</p> <p>Column 3: amplitude (of fluorescence emission)</p> <p>Column 4: xcenter of the aoi</p> <p>Column 5: ycenter of the aoi</p> <p> </p> <p> </p> <p>2. Time Intervals are stored as excel spreadsheet (.xlsx) files.</p> <p> </p> <p>These files contain the AOI numbers used for analysis and frame numbers that correspond to specific events, such as protein arrival and departure events.</p> <p> </p> <p> </p> <p>3. For experiments with <em>E</em><sub>FRET</sub> analysis performed, <em>E</em><sub>FRET</sub> values are stored as .mat matrix files.</p> <p> </p> <p>Column 1: Time in seconds after protein colocalization with DNA</p> <p>Column 2: <em>E</em><sub>FRET </sub>values</p> <p>Column 3: AOI number</p> <p>Column 4: Frame number</p>
Chemoembolization With or Without Antiviral Therapy for Unresectable HBV-related HCC With Low HBV DNA Replication
ClinicalTrials.gov study NCT01894269. IPD Sharing: Not stated. Countries: 1. Publications: 12.
Murine polyomavirus DNA transitions through spatially distinct nuclear replication subdomains during infection
Open the record for dataset details and reuse information.
Data from: The function and evolution of motile DNA replication systems in ciliates
Open the record for dataset details and reuse information.
Data from: Two subunits of human ORC are dispensable for DNA replication and proliferation
The six-subunit Origin Recognition Complex (ORC) is believed to be an essential eukaryotic ATPase that binds to origins of replication as a ring-shaped heterohexamer to load MCM2-7 and initiate DNA replication. We have discovered that human cell lines in culture proliferate with intact chromosomal origins of replication after disruption of both alleles of ORC2 or of the ATPase subunit, ORC1. The ORC1 or ORC2-depleted cells replicate with decreased chromatin loading of MCM2-7 and become critically dependent on another ATPase, CDC6, for survival and DNA replication. Thus, either the ORC ring lacking a subunit, even its ATPase subunit, can load enough MCM2-7 in partnership with CDC6 to initiate DNA replication, or cells have an ORC-independent, CDC6-dependent mechanism to load MCM2-7 on origins of replication
Supplementary material 2 from: Van den Bulcke L, De Backer A, Ampe B, Maes S, Wittoeck J, Waegeman W, Hostens K, Derycke S (2021) Towards harmonization of DNA metabarcoding for monitoring marine macrobenthos: the effect of technical replicates and pooled DNA extractions on species detection. Metabarcoding and Metagenomics 5: e71107. https://doi.org/10.3897/mbmg.5.71107
Tables S1–S8
Supplementary material 1 from: Van den Bulcke L, De Backer A, Ampe B, Maes S, Wittoeck J, Waegeman W, Hostens K, Derycke S (2021) Towards harmonization of DNA metabarcoding for monitoring marine macrobenthos: the effect of technical replicates and pooled DNA extractions on species detection. Metabarcoding and Metagenomics 5: e71107. https://doi.org/10.3897/mbmg.5.71107
Figures S1–S14
Molecular determinants of phase separation for Drosophila DNA replication licensing factors
<p>Liquid-liquid phase separation (LLPS) of intrinsically disordered regions (IDRs) in proteins can drive the formation of membraneless compartments in cells. Phase-separated structures enrich for specific partner proteins and exclude others. Previously, we showed that the IDRs of metazoan DNA replication initiators drive DNA-dependent phase separation <i>in vitro</i> and chromosome binding <i>in vivo</i>, and that initiator condensates selectively recruit partner proteins (Parker et al., 2019). How initiator IDRs facilitate LLPS and maintain compositional specificity is unknown. Here, using <i>D. melanogaster (Dm)</i> Cdt1 as a model initiation factor, we show that phase separation results from a synergy between electrostatic DNA-bridging interactions and hydrophobic inter-IDR contacts. Both sets of interactions depend on sequence composition (but not sequence order), are resistant to 1,6-hexanediol, and do not depend on aromaticity. These findings demonstrate that distinct sets of interactions drive condensate formation and specificity across different phase-separating systems and advance efforts to predict IDR LLPS propensity and partner selection <i>a priori</i>.</p>
Supplementary material 9 from: Weigand AM, Macher J-N (2018) A DNA metabarcoding protocol for hyporheic freshwater meiofauna: Evaluating highly degenerate COI primers and replication strategy. Metabarcoding and Metagenomics 2: e26869. https://doi.org/10.3897/mbmg.2.26869
Table S6: Taxa identified based on morphology :
Supplementary material 8 from: Weigand AM, Macher J-N (2018) A DNA metabarcoding protocol for hyporheic freshwater meiofauna: Evaluating highly degenerate COI primers and replication strategy. Metabarcoding and Metagenomics 2: e26869. https://doi.org/10.3897/mbmg.2.26869
Table S5: MetazoaMOTU list with 2-out-of-3 replicate strategy and excluding site 61 :
Supplementary material 7 from: Weigand AM, Macher J-N (2018) A DNA metabarcoding protocol for hyporheic freshwater meiofauna: Evaluating highly degenerate COI primers and replication strategy. Metabarcoding and Metagenomics 2: e26869. https://doi.org/10.3897/mbmg.2.26869
Table S4: MetazoaMOTU list with all replicates and including site 61 :
Supplementary material 6 from: Weigand AM, Macher J-N (2018) A DNA metabarcoding protocol for hyporheic freshwater meiofauna: Evaluating highly degenerate COI primers and replication strategy. Metabarcoding and Metagenomics 2: e26869. https://doi.org/10.3897/mbmg.2.26869
Table S3: Overview of raw reads and quality filtered reads :
Supplementary material 3 from: Weigand AM, Macher J-N (2018) A DNA metabarcoding protocol for hyporheic freshwater meiofauna: Evaluating highly degenerate COI primers and replication strategy. Metabarcoding and Metagenomics 2: e26869. https://doi.org/10.3897/mbmg.2.26869
Figure S2: Taxonomic composition of hyporheic community with all metazoan MOTUs :
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.