Photoperiodic control of the Arabidopsis proteome reveals a translational coincidence mechanism
DownloadData, model and analysis for Daniel Seaton et al. biorXiv 2017 https://doi.org/10.1101/182071 and Molecular Systems Biology, accepted Jan 2018, https://doi.org/10.15252/msb.20177962. Note that the published paper cannot be fully linked into this record as the DOI above was not live when we made the Research Object from this Investigation on FAIRDOMHub.
DOI: 10.15490/fairdomhub.1.investigation.163.2
Zenodo URL: None
Created at: 21st Feb 2018 at 11:36
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.
- Otauri_calculate_dark_vs_light_protein_synthesis.py
- Cyanothece_calculate_degradation_rates.py
- Cyanothece_dark_vs_light_protein_synthesis.py
- Otauri_calculate_degradation_rates.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.py
These checksums allow you to check a Snapshot you have downloaded hasn't been modified. For details on how to use these please visit this guide
MD5: 1f723b04f75569457cbd5a1c59619ece
SHA1: fd75b7a4f0ec163eb8e09b6f102a946d9c6c1a16
Views: 2478 Downloads: 33
Created: 21st Feb 2018 at 11:36
Last updated: 21st Feb 2018 at 11:47
(Since September 2018)
