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Depositordc.contributorMillar, Andrew
Funderdc.contributor.otherBBSRC - Biotechnology and Biological Sciences Research Councilen_UK
Funderdc.contributor.otherEuropean Commissionen_UK
Funderdc.contributor.otherWellcome Trusten_UK
Spatial Coveragedc.coverage.spatialPotsdam-Golmen_UK
Spatial Coveragedc.coverage.spatialDEen
Spatial Coveragedc.coverage.spatialGERMANYen
Time Perioddc.coverage.temporalstart=2012; end=2017; scheme=W3C-DTFen
Data Creatordc.creatorSeaton, Daniel
Data Creatordc.creatorGraf, Alex
Data Creatordc.creatorBaerenfaller, Katja
Data Creatordc.creatorStitt, Mark
Data Creatordc.creatorMillar, Andrew
Data Creatordc.creatorGruissem, Wilhelm
Date Accessioneddc.date.accessioned2018-02-21T15:52:23Z
Date Availabledc.date.available2018-03-01T05:15:30Z
Citationdc.identifier.citationSeaton, Daniel; Graf, Alex; Baerenfaller, Katja; Stitt, Mark; Millar, Andrew; Gruissem, Wilhelm. (2018). Data files and analysis scripts for Seaton et al. "Photoperiodic control of the Arabidopsis proteome reveals a translational coincidence mechanism", bioRxiv 2017, Mol. Syst. Biol. 2018, 2012-2017 [dataset]. University of Edinburgh. School of Biological Sciences and SynthSys. https://doi.org/10.7488/ds/2309.en
Persistent Identifierdc.identifier.urihttp://hdl.handle.net/10283/3031
Persistent Identifierdc.identifier.urihttps://doi.org/10.7488/ds/2309
Dataset Description (abstract)dc.description.abstractDataset description: The dataset comprises transcriptome (RNA levels) and proteome (protein levels) data for samples of the plant Arabidopsis thaliana, the alga Ostreococcus tauri and the cyanobacterium Cyanothece, along with analysis scripts in the python language, and analysis outputs, for the publication described below: Article abstract: Plants respond to seasonal cues, such as the photoperiod, to adapt to current conditions and to prepare for environmental changes in the season to come. To assess photoperiodic responses at the protein level, we quantified the proteome of the model plant Arabidopsis thaliana by mass spectrometry across four photoperiods. This revealed coordinated changes of abundance in proteins of photosynthesis, primary and secondary metabolism, including pigment biosynthesis, consistent with higher metabolic activity in long photoperiods. Higher translation rates in the daytime than the night likely contribute to these changes via rhythmic changes in RNA abundance. Photoperiodic control of protein levels might be greatest only if high translation rates coincide with high transcript levels in some photoperiods. We term this proposed mechanism 'translational coincidence', mathematically model its components, and demonstrate its effect on the Arabidopsis proteome. Datasets from a green alga and a cyanobacterium suggest that translational coincidence contributes to seasonal control of the proteome in many phototrophic organisms. This may explain why many transcripts but not their cognate proteins exhibit diurnal rhythms.en_UK
Dataset Description (TOC)dc.description.tableofcontentsDataset description: The data are provided as a Research Object bundle created by the FAIRDOM data management platform (SEEK v.1.5.2), on the FAIRDOMHub instance of that platform (www.fairdomhub.org). The dataset here corresponds to an Investigation with direct link https://fairdomhub.org/investigations/163. Note that this version of the dataset is live and may include updates compared to the published version. The data files are organised in the ISA hierarchy (see www.isa-tools.org). ___ A "snapshot" of the Investigation, i.e. a Research Object containing all linked objects and files (see www.researchobject.org), was saved and made public on FAIRDOMHub on 21 Feb 2018, with DOI: http://doi.org/10.15490/fairdomhub.1.investigation.163.2. The same Research Object is uploaded here. ___ The contents of the bundle are described in the Manifest file, .ro/manifest.json, and are summarised below (note this is an automatically-generated list that has lost the original hierarchy and formatting). The contents can be seen as a .ZIP archive file, including the various attached files of input data, calculated values and python analysis scripts; the Research Object structure can be inspected online at http://www.rohub.org. --- Rhythmic and photoperiod-specific transcriptome datasets for Arabidopsis Literature data used in the Seaton et al. 2017 study; data processing by Daniel Seaton. Stitt lab, TiMet photoperiod microarrays Transcript profiling by microarray in 4, 6, 8, 12 and 18 h photoperiods, originally published in Flis et al, 2016, Photoperiod-dependent changes in the phase of core clock transcripts and global transcriptional outputs at dawn and dusk in Arabidopsis. doi: 10.1111/pce.12754. Flis et al, 2016, Supplemental Table S4, Global expression profiles Microarray data at end of day (ED) and end of night (EN) in 4, 6, 8, 12, and 18h photoperiods. Supplemental Table S4, Global expression profiles.xlsx Photoperiod-dependent changes in the phase of core clock transcripts and global transcriptional outputs at dawn and dusk in Arabidopsis. Plants use the circadian clock to sense photoperiod length. Seasonal responses like flowering are triggered at a critical photoperiod when a light-sensitive clock output coincides with light or darkness. However, many metabolic processes, like starch turnover, and growth respond progressively to photoperiod duration. We first tested the photoperiod response of 10 core clock genes and two output genes. qRT-PCR analyses of transcript abundance under 6, 8, 12 and 18 h photoperiods revealed 1-4 h ... Blasing et al, 2005, diurnal microarray in 12L:12D No description specified Blasing et al, 2005, diurnal microarray dataset in 12L:12D No description specified DIURNAL_LDHH_ST.txt Photoperiod-specific proteome data for Arabidopsis Experimental data reported in the Seaton et al. 2017 study; data processing by Alex Graf. Part of the EU FP7 TiMet project. Photoperiod proteomics Plant material The same plant material used for transcriptome analysis in (Flis et al., 2016) was the basis of our proteome study. Briefly, Arabidopsis thaliana Col-0 plants were grown on GS 90 soil mixed in a ratio 2:1 (v/v) with vermiculite. Plants were grown for 1 week in a 16 h light (250 μmol m−2 s−1, 20 °C)/8 h dark (6 °C) regime followed by an 8 h light (160 μmol m−2 s−1, 20 °C)/16 h dark (16 °C) regime for one week. Plants were then replanted with five seedlings per pot, transferred for ... Table EV1 - Quantitative proteomics dataset Mean and standard deviation of protein abundances in 6h, 8h, 12h, and 18h photoperiods. Table EV1, Quantitiative proteomics dataset.xlsx Table EV3, Statistical analysis of protein changes across photoperiods Results of the statistical analysis, identifying proteins that change in abundance significantly across photoperiods. Table EV3, Statistical analysis of protein changes across photoperiods.xlsx Photoperiodic control of the Arabidopsis proteome reveals a translational coincidence mechanism bioRxiv preprint 2017 Plants respond to seasonal cues such as the photoperiod, to adapt to current conditions and to prepare for environmental changes in the season to come. To assess photoperiodic responses at the protein level, we quantified the proteome of the model plant Arabidopsis thaliana by mass spectrometry across four photoperiods. This revealed coordinated changes of abundance in proteins of photosynthesis, primary and secondary metabolism, including pigment biosynthesis, consistent ... Proteome and translation rate data for the Ostreococcus alga and for cyanobacteria Literature data and associated scripts analysed in the Seaton et al. 2017 study; data processing by Daniel Seaton. Martin et al, 2012, Ostreococcus N15 labelling proteomics data Proteomics data for N15 incorporation into protein in Ostreococcus grown in 12L:12D light:dark cycles. Martin et al, 2012, Ostreococcus N15 labelling proteomics Proteomics data for N15 incorporation into protein in Ostreococcus grown in 12L:12D light:dark cycles. martin2012_ostreo_diurnal_N15_proteomics.csv Proteome turnover in the green alga Ostreococcus tauri by time course 15N metabolic labeling mass spectrometry. Protein synthesis and degradation determine the cellular levels of proteins, and their control hence enables organisms to respond to environmental change. Experimentally, these are little known proteome parameters; however, recently, SILAC-based mass spectrometry studies have begun to quantify turnover in the proteomes of cell lines, yeast, and animals. Here, we present a proteome-scale method to quantify turnover and calculate synthesis and degradation rate constants of individual proteins in ... Aryal et al, 2011, metabolic labelling of Cyanothece protein synthesis Quantitative proteomic analysis of Cyanothece ATCC51142 grown in 12L:12D light:dark cycles, using partial metabolic labeling and LC-MS analysis. Aryal et al, 2011, metabolic labelling of Cyanothece protein synthesis Quantitative proteomic analysis of Cyanothece ATCC51142 grown in 12L:12D light:dark cycles, using partial metabolic labeling and LC-MS analysis. aryal2011_proteomics_data_relative_isotope_abundance_timecourse.csv Dynamic proteomic profiling of a unicellular cyanobacterium Cyanothece ATCC51142 across light-dark diurnal cycles. BACKGROUND: Unicellular cyanobacteria of the genus Cyanothece are recognized for their ability to execute nitrogen (N2)-fixation in the dark and photosynthesis in the light. An understanding of these mechanistic processes in an integrated systems context should provide insights into how Cyanothece might be optimized for specialized environments and/or industrial purposes. Systems-wide dynamic proteomic profiling with mass spectrometry (MS) analysis should reveal fundamental insights into the ... Estimation of rates of translation and turnover from proteomics datasets Data and Python scripts to run the analysis of literature data that estimates rates of protein synthesis in the light and dark, and overall rates of protein turnover, in Cyanothece and Ostrecoccus tauri. Aryal et al, 2011, metabolic labelling of Cyanothece protein synthesis Quantitative proteomic analysis of Cyanothece ATCC51142 grown in 12L:12D light:dark cycles, using partial metabolic labeling and LC-MS analysis. aryal2011_proteomics_data_relative_isotope_abundance_timecourse.csv Martin et al, 2012, Ostreococcus N15 labelling proteomics Proteomics data for N15 incorporation into protein in Ostreococcus grown in 12L:12D light:dark cycles. martin2012_ostreo_diurnal_N15_proteomics.csv Calculated rates of protein degradation in Ostreococcus tauri No description specified Otauri_degradation_rates.csv Calculated rates of protein degradation in Cyanothece ATCC51142 No description specified Cyanothece_degradation_rates.csv Calculated rates of protein synthesis in the light and dark in Ostreococcus tauri No description specified Otauri_dark_and_light_protein_synthesis.csv Calculated rates of protein synthesis in the light and dark in Cyanothece ATCC51142 No description specified Cyanothece_calculate_dark_vs_light_protein_synthesis.csv Estimation of translation and turnover - python scripts Python scripts to run the analysis estimating rates of protein synthesis in the light and dark, and overall rates of protein turnover, in Cyanothece and Ostrecoccus tauri. Cyanothece_calculate_degradation_rates.py Cyanothece_dark_vs_light_protein_synthesis.py Otauri_calculate_degradation_rates.py Otauri_calculate_dark_vs_light_protein_synthesis.py Modelling and analysis of translational coincidence Data analysis and modelling scripts and results for the Seaton et al. 2017 study, from Daniel Seaton. Translational coincidence model These Python scripts define and simulate the translational coincidence model. This model takes measured transcript dynamics (Blasing et al, 2005) in 12L:12D, measured synthesis rates of protein in light compared to dark (Pal et al, 2013), and outputs predicted changes in protein abundance between short (6h) and long (18h) photoperiods. These are compared to the photoperiod proteomics dataset we generated. Blasing et al, 2005, diurnal microarray dataset in 12L:12D No description specified DIURNAL_LDHH_ST.txt Table EV1 - Quantitative proteomics dataset Mean and standard deviation of protein abundances in 6h, 8h, 12h, and 18h photoperiods. Table EV1, Quantitiative proteomics dataset.xlsx Translational coincidence modelling - python scripts No description specified plot_translational_coincidence_model_predictions.py seatongraf_utility_functions.pyen_UK
Languagedc.language.isoengen_UK
Publisherdc.publisherUniversity of Edinburgh. School of Biological Sciences and SynthSysen_UK
Relation (Is Version Of)dc.relation.isversionofhttps://doi.org/10.15490/fairdomhub.1.investigation.163.2en_UK
Relation (Is Referenced By)dc.relation.isreferencedbyhttps://doi.org/10.15252/msb.20177962en_UK
Relation (Is Referenced By)dc.relation.isreferencedbyhttps://doi.org/10.1101/182071en_UK
Rightsdc.rightsCreative Commons Attribution 4.0 International Public Licenseen
Sourcedc.sourceMultiple published datasets are used in the publication, as detailed in the dataset descriptionen_UK
Subjectdc.subjectSystems Biologyen_UK
Subjectdc.subjectPlant Scienceen_UK
Subjectdc.subjectGene Regulationen_UK
Subjectdc.subjectProteomicsen_UK
Subjectdc.subjectSeasonalityen_UK
Subjectdc.subjectPhotoperiodismen_UK
Subjectdc.subjectEnvironmental regulationen_UK
Subjectdc.subjectBiological Rhythmsen_UK
Subjectdc.subjectCircadian clocksen_UK
Subject Classificationdc.subject.classificationBiological Sciences::Molecular Biology Biophysics and Biochemistryen_UK
Titledc.titleData files and analysis scripts for Seaton et al. "Photoperiodic control of the Arabidopsis proteome reveals a translational coincidence mechanism", bioRxiv 2017, Mol. Syst. Biol. 2018en_UK
Typedc.typedataseten_UK

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