Quantitative analysis and bioinformatics
Total proteome - Label‑free precursor (MS1) intensity based
quantification was performed using Progenesis QI for Proteomics (version
2.1, www.nonlinear.com) to quantify total proteome changes. Briefly, for
each individual fraction, automatic alignment was reviewed and manually
adjusted before normalization. From each Progenesis peptide ion (default
sensitivity in peak picking) a maximum of the top five tandem mass
spectra per peptide ion were exported as a Mascot generic file (*.mgf)
using charge deconvolution and deisotoping option and a maximum number
of 200 peaks per MS/MS. Searches were done in Mascot 2.4.1 (Matrix
Science) against a decoyed (reversed) Arabidopsis protein database from
TAIR (release TAIR10) concatenated with a collection of 261 known mass
spectrometry contaminants. Precursor ion mass tolerance was set to
10 ppm and the fragment ion mass tolerance was set to 0.6 Da. The
following search parameters were used: trypsin digestion (1 missed
cleavage allowed), fixed modifications of carbamidomethyl modified
cysteine and variable modifications of oxidation of methionine,
deamidation of asparagine and glutamine, and acetylation of protein N
terminal peptides. Mascot searches were imported into Scaffold 4.2.1
(Proteome Software). The following thresholds were applied: peptide
FDR ≤ 5, protein FDR ≤ 10, 1 minimum peptide. Spectrum reports were
imported again into Progenesis. After this, individual fraction analyses
were combined into the full quantitative Progenesis experiment. From
this, quantitative peptide values were exported for further processing.
Only peptides that could be unambiguously assigned to a single protein
(gene model annotation) were kept for quantification. A Hi‑4 strategy
(Grossmann et al., 2010) was applied to obtain protein quantitative
values. Proteins with 2 or more peptides assigned were considered as
quantifiable. Following these criteria, the final protein level FDR was
estimated at 0.013.
Phosphoproteome - Quantification of changes in identified
phosphopeptides was performed using MaxQuant (version 1.3.0.5) with
default settings and the following modifications: fixed peptide
modification by carbamidomethylation of cysteines and variable peptide
modifications by phosphorylation of serine, threonine and tyrosine, and
oxidation of methionine, and false discovery rate (FDR) tolerances of
≤ 0.05 (protein) and ≤ 0.01 (peptide). MaxQuant outputs were
subsequently filtered for phosphopeptides with a phosphorylation site
probability score ≥ 0.8 and presence in at least 2 of 4 biological
replicates and 2 of 3 time‑points for each light transition.
Data Analysis - Significant fluctuations in protein abundance and
phosphopeptides were determined using an ANOVA analysis: total proteome
(P value ≤ 0.05 and Fold-change (FC) ≥ 1.5) and phosphoproteome
(P value ≤ 0.05). The significantly changing proteome was subjected to
cluster analysis using GProX (Rigbolt, Vanselow, & Blagoev, 2011). Six
clusters were generated in an unsupervised clustering manner based on
the fuzzy c-means algorithm. Significantly changing proteins and
phosphoproteins were subjected to gene set enrichment analysis (GSEA)
using the SetRank algorithm relative to the identified proteome and
phosphoproteome, respectively (Simillion, Liechti, Lischer, Ioannidis,
& Bruggmann, 2017). Enrichment was calculated for all the available
databases included in the SetRank R package. Only terms with a size ≥ 2
were considered (gene set size ≥ 2). For each protein cluster, a SetRank
corrected P value ≤ 0.01 was applied as threshold. For phosphoproteins
changing at the L-D or D-L transition, a SetRank corrected
P value ≤ 0.01 and an FDR ≤ 0.05 were applied. To test for significantly
non-changing proteins at the transitions to light, (i.e., at dawn, ZT23
to ZT1, and dusk, ZT11 to ZT13), a TOST equivalence test (equivalence R
package) was applied with an ε = 0.4. Significance threshold was
P value ≤ 0.05. The mass spectrometry proteomics data have been
deposited to the ProteomeXchange Consortium via the PRIDE partner
repository. Data are available via ProteomeXchange with identifier
PXD007600.
Additional Analyses - To compare protein and mRNA profiles, mRNA
data generated by the Alison Smith laboratory was obtained from the
Diurnal database (http://diurnal.mocklerlab.org; Mockler et al., 2007).
Data was standardized to plot both protein and mRNA data in the same
graph. Predicted subcellular localization of all changing proteins and
phosphoproteins was performed using the consensus subcellular
localization predictor SUBAcon (suba3.plantenergy.uwa.edu.au) (Tanz et
al., 2013). String DB network analyses were undertaken using both
proteome and phosphoproteome data. String DB analyses were performed in
Cytoscape using the String DB plugin stringApp (Szklarczyk et al.,
2017). A minimum correlation coefficient of 0.5 was used along with a
second layer of 5 additional nodes to infer network connectedness.
JTK Analyses – To compare diurnal protein fluctuations to free
running circadian clock fluctuations published by Krahmer et al. (2019)
we performed an equivalent analysis using the JTK cycle to identify
proteins cycling with 22 or 24 h period (Hughes, Hogenesch, &
Kornacker, 2010). The exact loading script JTK_analysis.zip is
available upon request. The data was then used to produce Figure 3B, C
and D. Proteins identified to fluctuate were normalized such that they
fluctuate around a median of 0 with maximal amplitudes of 2.
Transcriptome data from Diurnal DB (http://diurnal.mocklerlab.org;
Mockler et al., 2007) was used to determine if the associated
transcripts were also fluctuating, and if so, when. To estimate a
confidence interval for the relative expression or protein level errors,
their relative levels were compared to the theoretical cosine function
at the same timepoint. Based on all errors, irrespective of the exact
timepoint, a 99% confidence interval was computed.