This repository contains the data files with differential expressed genes (DEGs) and notebooks that were used to analyse the microarray experiment described in Hammoudi et al., 2018 (PLoS Genetics). The presented scripts were used to create Figure 5 and the Supplemental figures S6-S9. The scripts do not contain the scripts and methods used to analyse the raw microarray data.
This study uncovered the role of SUMO-signalling in thermomorphogenesis (= how growth/shape changes in response to the temperature) and immunity at high temperature. To disentangle these two processes we subjected a pad4-1 mutant (an Arabidopsis mutant in which the PAD4 gene, a key regulator of the immune response, was mutated, which avoided spurious immune signalling due to auto-immunity). We grew all plants for 2 weeks at 22 degrees Celsius under 11L/13D conditions, and moved the plants to 28 degrees 4 hours after dawn. We measured gene expression at three time points: T0 (at the temperature shift), T1 (24 hours after the shift to high ambient temperature) and T2 (72 hours after the shift to high ambient temperature). The experiment contained 3 biological replicates for each mutant at each time point.
Moreover, we used two double-mutants in which not only PAD4 was mutated, but also SIZ1 (siz1-2 pad4-1), or a mutant in which SUMO1 is knocked-out and SUMO2 is silenced using amiRNA (sumo1-1 amiR-SUMO2 (a.k.a. 1xB) pad4).
Bas Beerens sampled the plants and extracted the RNA, Martijs Jonker and Paul Wackers (University of Amsterdam, RNA Biology group) analysed the raw microarray data and determined the gene expression levels at different time points and determined significant changes in expression in time (within the same mutant, compared one time point to the other; these files are in the data/reanalysisTime folder) and between the three mutants (i.e. one time point, comparing siz1 pad4 to pad4 or 1xB pad4 to pad4; these files are in the data/reanalysisStrains folder).
In notebooks, the code has been placed that was used to analyse the global (differences in) gene expression for the differentially expressed genes (DEGs) using Jupyter notebooks. By running these notebooks, you should be able to reproduce the results we presented in the paper in Figure 5 and the Supplemental figures S6-S9. Run the notebooks from the notebook directory.
The notebooks are written in Python2 (version Python 2.7.13) using the Anaconda2 4.3.0 distribution. The goal of this repository is open science, so if you have any comments (bugs, bad practices, omissions, things that are not clearly explained, positive feedback), please make an issue and we'll look into it (and learn from it). This is very much appreciated!
Description of files is in Contents.md.