Proteomic analysis of plant material
Extraction of leaf proteins and analysis of protein content using
gel-free LC-MS/MS was carried out as described by Miller et al.(2017). Briefly, frozen ground leaf samples (4 replicates per treatment)
were extracted in a buffer containing Rapigest. After reduction,
alkylation and trypsin digestion, samples were analysed by LC-MS/MS
using an UltiMate® 3000 Rapid Separation LC (RSLC, Dionex Corporation,
Sunnyvale, CA) coupled to an Orbitrap Elite mass spectrometer (Thermo
Fisher Scientific, MA, USA). Raw data were imported into Progenesis QI
(build 2.0.5556.29015; Nonlinear Dynamics, Newcastle, United Kingdom)
and runs were aligned according to the default settings. Only ions with
a charge state of up to +4 were considered. MS/MS data were searched
against the A. thaliana TAIR 10 database and assigned to peptides
using Mascot version 2.4.0 (Matrix Science, London, United Kingdom). A
maximum of one missed cleavage (Trypsin) was permitted, with a peptide
mass tolerance of 10 ppm and an MS/MS tolerance of 0.5 Da. Data were
then re-imported into Progenesis to allow for assignment of proteins
from peptide data. Raw protein intensities were then exported from
Progenesis and normalised to the sample with the median total protein
content for that treatment, as described previously (Miller et
al. , 2017). Total protein for each sample was calculated by summing the
intensities of all the quantified proteins.
A principal component analysis (PCA) was performed in the R software
package using log2 scaled protein intensities using the pcaMethods R
package. The ”svd” function was used, and 10 principal components were
included in the calculation. Proteins were considered to have
significantly changed in abundance when a p value of <0.05 was
reached, with a fold change of 1.2 or greater. For hierarchical
clustering analysis, log2 scaled protein values were
used. Hierarchical clustering was performed using Euclidean distance and
the complete linkages method. For heatmap/cluster analysis, fold change
data were calculated relative to the wild-type Col-0 at LL and log2
scaled. A heatmap was then generated using the heatmap.2 package in R
software, using the Euclidean distance algorithm for dendrogram
creation. The dendrogram was cut into 6 clusters.