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\begin{document}

\title{Diurnal dynamics of the Arabidopsis rosette proteome and phosphoproteome}



\author[1]{R. Glen Uhrig}%
\author[1]{Sira Echevarría-Zomeño}%
\author[1]{Pascal Schlapfer}%
\author[2]{Jonas Grossmann}%
\author[2]{Bernd Roschitzki}%
\author[3]{Niklas Koerber}%
\author[3]{Fabio Fiorani}%
\author[4]{Wilhelm Gruissem}%
\affil[1]{ETH Zürich}%
\affil[2]{Functional Genomics Center Zurich}%
\affil[3]{Forschungszentrum Jülich GmbH}%
\affil[4]{ETH Zurich}%


\vspace{-1em}



  
  \date{May 18, 2024}


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\selectlanguage{english}
\begin{abstract}
Plant growth depends on the diurnal regulation of cellular processes,
but it is not well understood if and how transcriptional regulation
controls diurnal fluctuations at the protein-level. Here we report a
high-resolution Arabidopsis thaliana (Arabidopsis) leaf rosette proteome
acquired over a 12 h light : 12 h dark diurnal cycle and the
phosphoproteome immediately before and after the light-to-dark and
dark-to-light transitions. We quantified nearly 5000 proteins and 800
phosphoproteins, of which 288 fluctuated in their abundance and 226
fluctuated in their phosphorylation status. Of the phosphoproteins, 60\%
were quantified for changes in protein abundance. This revealed six
proteins involved in nitrogen and hormone metabolism that had concurrent
changes in both protein abundance and phosphorylation status. The
diurnal proteome and phosphoproteome changes involve proteins in key
cellular processes, including protein translation, light perception,
photosynthesis, metabolism and transport. The phosphoproteome at the
light-dark transitions revealed the dynamics at phosphorylation sites in
either anticipation of or response to a change in light regime.
Phosphorylation site motif analyses implicate casein kinase II and
calcium/calmodulin dependent kinases among the primary light-dark
transition kinases. The comparative analysis of the diurnal proteome and
diurnal and circadian transcriptome established how mRNA and protein
accumulation intersect in leaves during the diurnal cycle of the plant.%
\end{abstract}%



\sloppy




\textbf{INTRODUCTION}

Plant growth and biomass production are direct functions of the diurnal
cellular carbon balance, which is regulated by a combination of light
responses and the circadian clock. Light responses are triggered by a
change in regime (i.e., presence or absence of light), while the
circadian clock is comprised of transcriptional regulators that operate
in anticipation of a change (e.g. transition from light to dark) and
whose activities span the 24 hour (h) photoperiod (Nohales \& Kay, 2016;
Oakenfull \& Davis, 2017; Seluzicki, Burko, \& Chory, 2017). The clock
transcriptional regulators include CCA1/LHY, PRR5, PRR7 and PRR9, which
form the morning loop, and TOC1, ELF3, LF4 and LUX, which form the
evening loop (Flis et al., 2015; Staiger, Shin, Johansson, \& Davis,
2013). More than 30\% of all Arabidopsis genes are regulated by the
circadian clock at the transcript level (Blasing et al., 2005;
Covington, Maloof, Straume, Kay, \& Harmer, 2008). However, less is
known about how the resulting diurnal transcriptome relates to protein
abundance (Abraham et al., 2016; Choudhary, Nomura, Wang, Nakagami, \&
Somers, 2015; Graf et al., 2017) and post-translational protein
modifications (Choudhary et al., 2015; Uhrig, Schlapfer, Roschitzki,
Hirsch-Hoffmann, \& Gruissem, 2019), both of which may also affect
protein function at light-dark transitions and throughout the diurnal
cycle. Transcript and protein abundance changes are often disconnected
because changes in transcript levels show no corresponding change in
protein abundance (Baerenfaller et al., 2012; Seaton et al., 2018). For
example, this was found, in the circadian clock mutants CCA1/LHY,
PRR7/PRR9, TOC1 and GI (Graf et al., 2017) or for the variability in the
timing of peak protein levels relative to the cognate transcript
expression (translational coincidence) as a function of the
photoperiod-dependent coordination between transcriptome and proteome
changes (Seaton et al., 2018). Variable delays between peak transcript
and protein abundance have implicated post-transcriptional regulation
(e.g. splicing), translational regulation (e.g. translation rate) as
well as post-translational regulation (e.g. protein phosphorylation) as
possible mechanisms to explain the temporal differences in RNA and
protein abundance. Recent studies of plant protein-level regulation have
found extensive variability in protein turnover (Li et al., 2017; Seaton
et al., 2018). This adds further regulatory complexity because
quantitative proteome workflows cannot easily account for protein
turnover. Although RNA and protein synthesis, stability and turnover all
contribute to the coordination of RNA and protein abundance, how these
mechanisms are integrated is currently not well understood. Insights
into this regulatory complexity requires both time-course experiments
and multi-Omics analysis. We used a large-scale quantitative proteomics
approach to determine the extent of diurnal abundance and/or
phosphorylation changes of Arabidopsis leaf rosette proteins involved in
cellular and metabolic processes and how these protein level changes
relate to cognate transcript levels.

Reversible protein phosphorylation is the most abundant
post-translational modification (PTM) in eukaryotes (Adam \& Hunter,
2018; Rao, Thelen, \& Miernyk, 2014). In non-photosynthetic eukaryotes
phosphorylation is found to modulate more than 70\% of all cellular
processes (Olsen et al., 2006), including the circadian clock itself
(Robles, Humphrey, \& Mann, 2017). This is likely similar in land plants
because they have a significantly larger kinome (Lehti-Shiu \& Shiu,
2012) compared to humans, which encode 518 protein kinases (Manning,
Whyte, Martinez, Hunter, \& Sudarsanam, 2002). In contrast, both plants
and humans have an equally comparable small number of protein
phosphatases (Kerk, Templeton, \& Moorhead, 2008). However, most protein
phosphatases require association with regulatory subunits to achieve
their specificity (Moorhead et al., 2008; Uhrig, Labandera, \& Moorhead,
2013), suggesting that similar complexity in protein dephosphorylation
across plants and humans has evolved through the expansion of protein
phosphatase regulatory subunits.

In plants, diurnal protein phosphorylation is regulated either in
response to light, by the circadian clock (Choudhary et al., 2015), or
both (Uhrig et al., 2019), while the clock itself is regulated by
phosphorylation (Kusakina \& Dodd, 2012; Uehara et al., 2019). Recent
studies of the circadian phosphoproteome combining the analysis of a
free-running cycle and the circadian clock mutant \emph{elf4} (Choudhary
et al., 2015) or CCA1-OX over-expression (Krahmer et al., 2019) have
revealed temporally modified phosphorylation sites related to casein
kinase II (CKII) and sucrose non-fermenting kinase 1 (SnRK1). SnRKs are
likely involved in the regulation of the circadian phosphoproteome
because the transcription of genes encoding multiple SnRK and
calcinuerin B-like (CBL) interacting kinases (CIPK) was mis-regulated in
the Arabidopsis circadian clock mutants \emph{cca1/lhy1}
,\emph{prr7prr9} , \emph{toc1} and \emph{gi201} mutants at end-of-day
(ED) and end-of-night (EN) (Graf et al., 2017). Similarly, studies
quantifying changes in the phosphoproteome at ED and EN in Arabidopsis
rosette leaves, roots, flowers, siliques and seedlings have revealed a
large number of diurnally changing phosphorylation events corresponding
to diverse protein kinase motifs (Reiland et al., 2009; Uhrig et al.,
2019).

Considering the physiological and metabolic changes at the light-dark
(L-D) and dark-light (D-L) transitions (Annunziata et al., 2018; Gibon
et al., 2009; Usadel et al., 2008), we performed a quantitative
phosphoproteome analysis of proteins that are phosphorylated immediately
before and after the L-D and D-L transitions during a 12 h light : 12 h
dark photoperiod and asked how these phosphorylation events intersect
with protein abundance changes. Together with the 24 h photoperiod
time-course proteome data, our systems-level analysis provides new
insights into diurnal protein and phosphorylation regulation in
Arabidopsis rosette leaves.

\subsection*{MATERIALS AND METHODS}

{\label{materials-and-methods}}

Arabidopsis Col-0 wild-type plants were grown at the Forschungszentrum
J\selectlanguage{ngerman}ülich (Germany) in an environmentally controlled chamber (GrowScreen
Chamber;
https://eppn2020.plant-phenotyping.eu/EPPN2020\_installations\#/tool/30;
Barboza-Barquero et al., 2015) under a 12~h light:12~h dark photoperiod
and controlled conditions as described in Baerenfaller \emph{et
al.}(2012), including air temperature of 21ºC during the day and 20ºC
during the night, air humidity of 70\%, and an incident light intensity
of \textasciitilde{}220 mmol/m\textsuperscript{2}/s at the plant level.
Whole rosettes were harvested at 31 days after sowing (DAS) prior to
flowering. Four whole rosettes were pooled into one sample and 4
biological replicates were taken at each time point. For total proteome
analyses, samples were taken every 2~h during 24~h, starting at
Zeitgeber time 1 (ZT1, i.e. 1~h after lights turned on). For protein
phosphorylation analyses, samples were 30~min before, 10~min after and
30~min after the L-D and D-L transitions. Samples were snap-frozen in
liquid N\textsubscript{2} and stored at -80ºC until protein extraction.

\subsection*{}

{\label{section-1}}

Proteome Analysis

\emph{Extraction and digestion -} Samples were randomized before
processing to avoid batch effects. Frozen rosettes were ground under
liquid N\textsubscript{2}. Proteins were extracted from 100~mg of frozen
powder per sample by adding 150~\selectlanguage{greek}µ\selectlanguage{english}l of extraction buffer (30~mM Tris-HCl
pH~8.0, 4\%~SDS). Tubes were incubated in a shaker (Eppendorf) at 4\selectlanguage{ngerman}ºC at
1400~rpm for 30~min. Samples were centrifuged at 16000~\emph{g} and 4ºC
for 30~min and the supernatant was transferred to a new tube. Protein
concentration was estimated based on Bradford (Bradford, 1976) using the
Bio-Rad Protein Assay reagent. Subsequently, DTT was added to a final
concentration of 50~mM and proteins were reduced for 30~min on ice. For
digestion, 140~\selectlanguage{greek}µ\selectlanguage{english}g of proteins were processed following the FASP method
(Wisniewski, Zougman, Nagaraj, \& Mann, 2009). Peptides were desalted
using SPE C18 columns (Finisterre) and dried down in a SpeedVac
concentrator.

\emph{Peptide fractionation -} To increase proteome coverage, peptide
samples were fractionated by hydrophilic interaction chromatography
(HILIC) on an Agilent 1200 series HPLC system with a YMC-Pack Polyamine
II 250~x~3.0~mm size column with 5~\selectlanguage{greek}μ\selectlanguage{english}m particle size and 120~\selectlanguage{ngerman}Å pore size.
Samples were dissolved in 100~\selectlanguage{greek}μ\selectlanguage{english}l Buffer A (75\%~ACN, 8~mM
KH\textsubscript{2}PO\textsubscript{4}, pH~4.5) and separated with
Buffer B (5\%~ACN, 100~mM KH\textsubscript{2}PO\textsubscript{4},
pH~4.5) at a flow rate of 500~\selectlanguage{greek}µ\selectlanguage{english}l/min with the following gradient:
0-7.5~min, 0\%~B; 7.5-37.5~min, 0-50\%~B; 37.5-42.5~min, 50-100\%~B;
42.5-47.5~min, 100\%~B. Following the separation the column was washed
with 100\%~buffer A and re-equilibrated for 60~min. For each sample, the
27 automatically collected fractions were pooled into five fractions
that were subsequently dried down in a SpeedVac concentrator. Each
sample was then dissolved in 200~\selectlanguage{greek}µ\selectlanguage{english}l of 3\%~ACN, 0.1\%~TFA, desalted on
SPE C18 columns (Finisterre) and again dried in a SpeedVac concentrator.

\emph{LC-MS analysis -} Mass spectrometry queues were arranged to
process comparable fractions in the same batch, with sample order
randomized within each batch. Peptide samples were dissolved in 20~\selectlanguage{greek}µ\selectlanguage{english}l
3\%~ACN, 0.1\%~FA and spiked with internal retention time (iRT)
standards (Biognosys) for chromatography quality control. LC-MS/MS
shotgun analyses were performed on a Thermo Orbitrap Fusion instrument
coupled to an Eksigent NanoLC Ultra (Sciex). Samples were separated on a
self-packed reverse-phase column (75~\selectlanguage{greek}µ\selectlanguage{english}m x 150~mm) with C18 material
(ReproSil-Pur, C18, 120~\selectlanguage{ngerman}Å, AQ, 1.9~\selectlanguage{greek}µ\selectlanguage{english}m, Dr. Maisch GmbH). The column was
equilibrated with 100\%~solvent~A (0.1\%~FA in water). Peptides were
eluted using the following gradient of solvent~B (0.1\%~FA in ACN) at a
flow rate of 0.3~\selectlanguage{greek}µ\selectlanguage{english}l/min: 0-50~min: 3-25\%~B, 50-60~min: 25-35\%~B,
60-70~min: 35-97\%~B, 70-80 min: 97\%~B, 80-85~min: 2\%~B. Mass spectra
were acquired in a data-dependent manner. All precursor signals were
recorded in the Orbitrap using quadrupole transmission in the mass range
of 300-1500~m/z. Spectra were recorded with a resolution of 120000
(FWHM) at 200 m/z, a target value of 4e5 and the maximum cycle time set
to 3~s. Data dependent MS/MS were recorded in the linear ion trap using
quadrupole isolation with a window of 1.6~Da and higher-energy
collisional dissociation (HCD) fragmentation with 30\%~fragmentation
energy. The ion trap was operated in rapid scan mode with a target value
of 1E4 and a maximum injection time of 250~ms. Precursor signals were
selected for fragmentation with a charge state from +~2 to +~7 and a
signal intensity of at least 5e3. A dynamic exclusion list was used for
30~s and maximum parallelizing ion injections was activated. The mass
spectrometry proteomics data were handled using the local laboratory
information management system (LIMS) (T\selectlanguage{ngerman}ürker et al., 2010)

\subsection*{Phosphoproteome Analysis}

{\label{phosphoproteome-analysis}}

\emph{Extraction --} Whole rosette tissue from each time point was
harvested and ground under liquid N\textsubscript{2}. From each
biological replicate 200~mg of ground leaf material was weighed out
under liquid N\textsubscript{2}. In addition to each biological
replicate, 200~mg of samples containing equal weighted parts of each
biological replicate and time-point were created as a reference sample
(gold-standard) for downstream dimethyl labeling. All proteins were
extracted in a 250~\selectlanguage{greek}µ\selectlanguage{english}l solution of 50~mM HEPES pH~8.0, 6~M urea, 2~M
thiourea, 100~mM NaCl, 10~mM EDTA, 2~mM NaOV, 5~mM NaF, 50~\selectlanguage{greek}µ\selectlanguage{english}g/mL
PhosSTOP (Roche). Samples were shaken at room temperature for 30~min at
1000~\emph{g} with vortexing every 10~min. Extracts were then brought to
pH~8.0 using triethylammonium bicarbonate (TEAB). Protein extracts were
then reduced for 30~min with 10~mM DTT, followed by alkylation with
30~mM iodoacetamide for 1~h. Extracts were clarified to separate soluble
and insoluble fractions. The insoluble fraction was re-suspended in
300~\selectlanguage{greek}µ\selectlanguage{english}L 60:40 buffer containing 60\%~MeOH:~40\%~50~mM~TEAB pH~8.0
followed by shaking at 1000~rpm (Eppendorf tabletop) for 2.5~h. The
protein concentration of the soluble fraction was then measured using
the Bradford protein assay (Bradford, 1976). An amount of 1~mg of
soluble protein from each sample was then diluted with 1~vol. of 50~mM
TEAB and then water was added to a total volume of 1.2~ml and a final
urea/thiourea concentration of 1.2~M. The soluble fraction was then
digested for 20~h at 37\selectlanguage{ngerman}ºC using a 1:50 ratio of trypsin (Promega) to
extracted protein while gently shaking. Each insoluble fraction was
digested by 0.5~\selectlanguage{greek}µ\selectlanguage{english}g chymotrypsin and 1 \selectlanguage{greek}µ\selectlanguage{english}g trypsin at 37\selectlanguage{ngerman}ºC for 20 h
shaking at 600 rpm (Eppendorf tabletop). Digestion reactions were
stopped using TFA to a final concentration of 0.5\%. The insoluble
fractions were centrifuged for 10~min at 20000~\emph{g} at room
temperature and the supernatant removed. The supernatant was then dried
and re-suspended in desalting buffer comprised of 3\%~ACN~/~0.1\%~TFA.
The soluble fraction and the supernatant from the insoluble fraction
were desalted using SPE C18 columns (Finisterre) and dried in a SpeedVac
concentrator.

\emph{Dimethyl labeling and phosphopeptide enrichment -} Total peptide
fractions from each experimental (light label) and gold-standard (heavy
label) sample were labeled according to Boersema \emph{et
al.,}(Boersema, Raijmakers, Lemeer, Mohammed, \& Heck, 2009). Heavy and
light samples were then mixed 1:1 and desalted prior to phosphopeptide
enrichment using TiO\textsubscript{2}. Phosphopeptide enrichment was
performed using TiO\textsubscript{2} heavy and light dimethyl-labelled
phosphopeptides as previously described (Zhou et al., 2011).

\emph{LC-MS -} Phosphorylated peptide samples were analyzed using a Q
Exactive Orbitrap mass spectrometer (Thermo Scientific). Dissolved
samples were injected using an Easy-nLC 1000 system (Thermo Scientific)
and separated on a self-made reverse-phase column (75~\selectlanguage{greek}µ\selectlanguage{english}m x 150~mm)
packed with C18 material (ReproSil-Pur, C18, 120~\selectlanguage{ngerman}Å, AQ, 1.9~\selectlanguage{greek}µ\selectlanguage{english}m, Dr.
Maisch GmbH). The column was equilibrated with 100\% solvent A (0.1\%
formic acid (FA) in water). Peptides were eluted using the following
gradient of solvent B (0.1\% FA in ACN): 0-120~min, 0-35\% B,
120-122~min, 35-95\% B at a flow rate of 0.3~\selectlanguage{greek}µ\selectlanguage{english}l/min. High accuracy mass
spectra were acquired in data-depended acquisition mode. All precursor
signals were recorded in a mass range of 300-1700~m/z and a resolution
of 70000 at 200~m/z. The maximum accumulation time for a target value of
3e6 was set to 120~ms. Up to 12 data dependent MS/MS were recorded using
quadrupole isolation with a window of 2~Da and HCD fragmentation with
28\% fragmentation energy. A target value of 1e6 was set for MS/MS using
a maximum injection time of 250~ms and a resolution of 70000 at 200~m/z.
Precursor signals were selected for fragmentation with charge states
from +2 to +7 and a signal intensity of at least 1e5. All precursor
signals selected for MS/MS were dynamically excluded for 30~s.

Quantitative analysis and bioinformatics

\emph{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.

\emph{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.

\emph{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 \selectlanguage{greek}ε\selectlanguage{english}~=~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.

\emph{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.

\emph{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.

\textbf{RESULTS AND DISCUSSION}

\textbf{Dynamics of the Arabidopsis diurnal proteome and
phosphoproteome}

Using proteotypic peptides and label-free quantitative proteomics
analysis we identified 7060 unique proteins, of which we were able to
quantify 4762 proteins with two and more proteotypic peptides over the
24 h photoperiod time-course (Supplemental Figure 1; Table 1;
Supplemental Table 1). Statistical analysis showed that 288 of these
proteins were significantly changing in abundance (ANOVA P~value~[?]~0.05,
FC~\textgreater{}~1.5); Table 1; Supplemental Table 2), suggesting that
a portion (\textasciitilde{}6\%) of the identified and tracked proteome
is dynamically regulated during the diurnal cycle. Additionally, using a
dimethyl-labeling approach we identified a total of 2298 phosphopeptides
(Supplemental Figure 1; Supplemental Table 3), of which 1776 had a
phosphorylation site probability score [?]~0.8. We were able to quantify
1056 of these phosphopeptides (present in at least 2 biological
replicates and in 3 out of 3 time points for each transition; Table 1).
This corresponded to a total of 1803 identified phosphorylation sites,
of which 253 (14\%) represented newly identified phosphorylation sites
when compared to the compendium of 79,334 known phosphorylation sites
(PhosPhat 4.0; Heazlewood et al., 2008). A total of 271 phosphopeptides
on 226 proteins (\textasciitilde{}26\% of all quantified
phosphopeptides) significantly changed in abundance (ANOVA
P~value~[?]~0.05) at either the D-L, L-D or both transitions (Table 1;
Supplemental Table 4).

\textbf{Most proteins with diurnal changes in abundance fluctuate
independently of their transcript levels and belong to specific
functional networks}

To clarify which cellular and physiological processes have protein
abundance dynamics, we grouped all significantly changing proteins with
similar accumulation profiles into clusters and then subjected these
clusters to gene set enrichment analysis (GSEA). Each of the resulting
six clusters (CL1 -- CL6) is enriched for proteins involved in specific
processes (P~value~[?]~0.01, gene set size [?]~2) (Figure 1A-B; Supplemental
Data 1 - 6). Cluster CL1 contains proteins involved in RNA splicing that
decrease before dawn, while CL2 is enriched in proteins that peak early
in the light period and have roles in nitrogen metabolism, iron
homeostasis, responses to gravity and chloroplast stroma protein import.
CL5 contains proteins with peak abundance before dawn and lower
abundance before dusk that have specific functions in aerobic
respiration and proteasome complex formation, while proteins in CL3 have
functions in membrane-related processes and ribosome biogenesis. The CL3
abundance profile is complex with a sharp minimum during the second half
of the light period that is also found at the transcript level for
selected proteins in this group (see below). Clusters CL4 and CL6
exhibit distinct and opposing wave-form diurnal changes in protein
abundance, with proteins in CL4 peaking during the light period and
proteins in CL6 peaking early in the dark period. CL4 is enriched for
proteins involved in nitrogen metabolism and photosynthesis, which are
required for light-dependent carbon assimilation to support growth,
while CL6 is enriched for proteins involved in metabolic and RNA-related
processes that indicate a systemic change in the plant cell environment.

We then compared the proteins in CL1 to CL6 with their corresponding
transcript expression profiles using transcriptome data from whole
Arabidopsis rosettes grown and harvested in comparable conditions and at
similar time-points (Figure 1C). This revealed that the dynamics of CL1
to CL6 protein changes are not strictly correlated with the diurnal
abundance changes of their transcripts (Figure 1C; Supplemental Data
1-6), as has been found in other studies (Baerenfaller et al., 2012;
Abraham et al., 2016; Graf et al., 2017; Seaton et al., 2018). We also
determined the subcellular compartmentalization of proteins in each
cluster using the consensus localization predictor SUBAcon (SUBA3;
http://suba3.plantenergy.uwa.edu.au; Figure 1D) (Tanz et al., 2013).

Next, we built functional association networks between the proteins in
each cluster using STRING-DB (http://string-db.org; Figure 2). STRING-DB
scoring and Cytoscape visualization allowed us to estimate association
confidence between protein nodes, while subcellular localization
information resolved co-localized nodes at the protein level. Second
level nodes not found in our data were also included to better depict
the broader relationships between significantly changing proteins. This
analysis strategy resolved multiple protein hubs with variable degrees
of interconnectedness and belonging to relevant biological processes,
with some processes complementing those enriched by GSEA. Proteins with
no known connections above the set threshold for the association
networks were removed for network visualization, but they may also serve
on an individual basis to further connect GSEA and STRING\_DB analyses.
Using our STRING-DB analysis approach we defined network structures for
proteins belonging to: RNA splicing (CL1) and processing (CL6; RNA
helicases and binding proteins), chloroplast-related processes (CL4 and
5, light detection; CL1 and CL5, carbohydrate/starch metabolism; CL2,
redox regulation), cell metabolism (CL4, nitrogen and fatty acid
metabolism), secretion and intracellular transport (CL2), cell wall
biosynthesis (CL5) as well as cytosolic (CL1, 3 and 5), mitochondrial
(CL3) and plastidial (CL4 and 5) protein translation (Figure 2). Taken
together, our GSEA and functional association analyses indicate that the
complementary use of system-wide analysis approaches provides a more
comprehensive view of the types of proteins whose abundances are
diurnally modulated in cellular processes.

\textbf{The influence of the circadian clock on diurnal fluctuations of
both proteins and transcripts is limited.}

To determine if the significant changes we measured in the diurnal
proteome could be controlled by the circadian clock, we next compared
our data to a quantitative proteomics dataset acquired under
free-running (continuous light) conditions (Krahmer et al., 2019). Our
dataset of quantified proteins contains 1800 of the 2038 proteins (88\%)
reported in this study, suggesting a comparable depth of proteome
analysis (Supplemental Data 7). To directly compare our data set with
the one reported in Krahmer et al. (2019), we performed the same JTK
cycle analysis to identify proteins cycling with 22 or 24~h period
(Hughes et al., 2010). When corrected for multiple testing, we found a
total of 21 proteins that exhibit a significant fluctuation in
abundance, of which 3 demonstrated a similar pattern under continuous
light conditions (Figure 3A). A less stringent test using ``shuffling of
protein levels'' (Hughes et al., 2010) identified a total of 147
fluctuating proteins (211 were found to fluctuate under continuous light
conditions using this method; Krahmer et al., 2019). Between our study
and Krahmer et al. (2019), 3 proteins fluctuate in both studies, 1 only
in L-D conditions and 7 only in continuous light. The fact that of these
11 proteins 10 have significant JTK-cycle fluctuations in continuous
light (i.e., free-running condition) suggests that they are under
circadian control, although additional proteome analysis of normal
photoperiods prior to free-running conditions is needed to substantiate
this possibility. We did find alpha-crystallin domain 32.1 (ACD32.1;
AT1G06460) to fluctuate at the protein-level independent of the
circadian clock. ACD32.1 was previously shown to be regulated diurnally
at the transcript level in continuous light (Covington et al., 2008),
but it did not fluctuate in the proteome data of Krahmer et al., 2019.
ACD32.1 is a peroxisome-targeted chaperone protein (Pan et al., 2018)
implicated in the suppression of protein aggregation (Ma, Haslbeck,
Babujee, Jahn, \& Reumann, 2006). The protein peaks in abundance
immediately after dark, suggesting a need for peroxisomal protein
stability in the dark to maintain peroxisome functions required for
plant growth, including fatty acid oxidation (Pan et al., 2018). To
determine if a relationship exists between protein and transcript levels
among the proteins identified in the JTK-cycle analysis, we queried the
Diurnal Database v2.0 (http://diurnal.mocklerlab.org/) and found many
gene sets with thousands of genes fluctuating in their transcript
levels. We searched the Diurnal Database for the genes encoding the 21
proteins showing a significant change in protein abundance in our
dataset (Figure 3A, magenta and blue) and found 18 of the genes, of
which 3 did not have fluctuating transcript levels. Thus, only few
protein--transcript pairs have both fluctuating transcripts and
proteins. For the few pairs that do, neither protein nor the
corresponding transcript levels were peaking at a specific Zeitgeber
time (Figure 3B). But when comparing the patterns of individual pairs,
there was typically a median delay of 5.5 h between the transcript peak
and protein peak, and the expression patterns are correlated (Figures 3C
and D). Since such a shifted dependency of transcript and protein
expression pattern is rare in our proteome dataset, its biological
significance needs to be investigated further.

The identification of only a single highly significant JTK-cycling
protein in our dataset, together with limited fluctuations of proteins
reported for measured proteomes of Arabidopsis wild-type and circadian
clock mutants growing in free-running cycles of continuous light
(Choudhary et al., 2015; Krahmer et al., 2019) is unexpected at first
sight. However, previous studies of growing Arabidopsis leaves during
the diurnal cycle (Baerenfaller et al., 2012) have revealed that the
abundance of most measured proteins is not affected by corresponding
changes in their transcript levels. This disconnect between protein and
transcript abundances might be explained by one or more of the following
factors. First, to some extent, absence of protein abundance
fluctuations can be accounted for by JTK\_Cycle stringency, which only
tests for periodical protein changes following a sinus function. Second,
the temporal separation of the fluctuating circadian transcriptome and
the mostly stable proteome may provide a currently unknown adaptive
advantage. This possibility is consistent with previous results reported
from proteome analyses of circadian clock mutants (Graf et al., 2017).
Third, turnover rates of proteins may increase or decrease with constant
translation of transcripts whose levels fluctuate, resulting in stable
diurnal abundances of most proteins on a proteome-scale basis
(Martin-Perez \& Villen, 2017). Finally, regular changing diurnal
conditions (light, temperature, humidity, etc.) may not significantly
impact the levels of most proteins. Instead of regulating proteins by
adjusting their abundance, plants might react to expected diurnal
changes in conditions by regulating the activity of proteins. This would
be supported by the fact that most circadian regulation in Arabidopsis
involves post-translational modifications of proteins such as protein
phosphorylation (Choudhary et al., 2015; Krahmer et al., 2019; Uhrig et
al., 2019) independent of changes in mRNA and/or protein levels. Further
studies will be necessary to understand which factors explain these
findings.

\textbf{Analysis of light-dark transitions in a diurnal cycle reveal
dynamic fluctuation in the Arabidopsis phosphoproteome}

Protein phosphorylation is often associated with changing environmental
conditions (Li et al., 2017; S. Zhang et al., 2019; Zhao et al., 2017).
Therefore, we examined time-points before (30~min) and after (10~min,
30~min) the D-L and L-D transitions for changes in the phosphoproteome
(Supplemental Figure 1). We identified 1776 phosphopeptides from 1091
proteins (phosphorylation site probability score [?]~0.8) and quantified
1056 of these phosphopeptides from 725 proteins at the two light
transitions (Table 1, Supplemental Table 3). We found that 176
phosphopeptides from 153 proteins at the D-L transition and 164
phosphopeptides from 144 proteins at the L-D transition had significant
changes in abundance (Supplemental Figure 2 and 3; Supplemental Table
4). We then benchmarked the quality of our dataset by querying it for
proteins known to be diurnally regulated by protein phosphorylation
(Supplemental Table 5). This revealed phototropin~1, nitrate reductase
(NIA1 and NIA2) and CF1 ATP synthase. Phototropin~1 is phosphorylated in
the light (Sullivan, Thomson, Kaiserli, \& Christie, 2009; Sullivan,
Thomson, Lamont, Jones, \& Christie, 2008), while the NIA1, NIA2 and the
CF1 ATP synthase beta-subunit are phosphorylated in the dark (Kanekatsu,
Saito, Motohashi, \& Hisabori, 1998; Lillo, Meyer, Lea, Provan, \&
Oltedal, 2004; G. Moorhead et al., 1999; Reiland et al., 2009). Our
quantitation of NIA1 and 2 protein phosphorylation changes across
time-points revealed that NIA2 was more rapidly dephosphorylated on
Ser\textsuperscript{534} at the D-L transition than NIA1, potentially
relating to regulatory differences between NIA1 and 2. Additionally, we
found a new NIA2 phosphorylation site at Ser\textsuperscript{63} with
opposing diurnal changes in phosphorylation at the same transition
(Supplemental Figure 4).

We next performed a GSEA of all significantly changing phosphoproteins
(P~value~[?]~0.01, FDR~[?]~0.05, gene set size [?]~2) at each transition.
Enriched biological processes at the D-L transition include
phosphoproteins involved in light detection, nitrogen metabolism, cell
wall-related processes and phosphorylation signaling, while
phosphoproteins identified at the L-D transition are involved in light
detection, vesicle-mediated transport, auxin signaling and nucleus
organization (Table 2). We then generated a hierarchical heat map of the
phosphopeptides to identify clusters of proteins at each light
transition with similar phosphorylation dynamics (Supplemental Figure 2
and 3). When compared to datasets of phosphorylated proteins previously
identified in Arabidopsis growing under free-running cycle conditions
(Choudhary et al., 2015; Krahmer et al., 2019), or at the ED and EN
time-points of a 12-hour photoperiod (Reiland et al., 2009; Uhrig et
al., 2019), our data reveals proteins that have diurnal changes in their
phosphorylation status and also novel rate-of-change information for
these phosphorylation events (Supplemental Figure 2 and 3). For example,
the L-D cluster I has phosphoproteins involved in nitrogen metabolism
and the cell cycle (AD10 and AD30) and the L-D cluster III (BD30) has
phosphoproteins involved in plastid organization (Supplemental Figure
2). In contrast, the D-L cluster II (AL10 and AL30) has phosphoproteins
involved in central and carbohydrate metabolism (Supplemental Figure 3).
Interestingly, parallel phosphorylation changes in L-D cluster I occur
on proteins involved in nitrogen metabolism and the cell cycle. Nitrogen
is acquired by plants primarily in the form of nitrate or ammonium, and
is an essential macronutrient for plant growth. Nitrate signaling is
linked to cell cycle progression through the TEOSINTE BRANCHED 1/
CYCLOIDEA/PROLIFERATING CELL FACTOR 20 (TCP20) -- NIN-LIKE PROTEIN 6/7
(NLP6/7) regulatory network. TCP20 positively regulates genes encoding
proteins involved in nitrate assimilation and signaling and
downregulates the expression of \emph{CYCB1;1} , which encodes a key
cell-cycle protein involved in the G2/M transition (Guan, 2017). Our
data suggests that in addition to TCP20 transcriptional regulation,
reversible protein phosphorylation may also play a role in this
regulatory intersection between nitrate signaling and the cell cycle.

Similar to our analysis of protein abundance changes, we built
association networks using STRING-DB to complement the GSEA analysis of
the phosphoproteome (Figure 4). Association networks were generated
based on phosphopeptide quantification data and \emph{in
silico}subcellular localization information to examine relationships
between the significantly changing phosphoproteins at both the D-L and
L-D transitions. Most of the node clusters overlap between both the D-L
(Figure 4A) and L-D (Figure 4B) networks, with larger clusters
consisting of proteins involved in light detection and signaling, carbon
and nitrogen metabolism, protein translation, hormone signaling, ion
transport, cell wall related processes and protein phosphorylation. L-D
transition-specific node clusters include RNA processing, transcription
and secretion, and protein transport (Figure 4B). Similar to our
proteome analyses, network association and GSEA analyses showed a high
degree of overlap, indicating that the two approaches revealed the same
cell processes in which proteins show differences in phosphorylation.

The dynamics of the measured Arabidopsis proteome during the diurnal
period suggests that the proteins affected by abundance changes have
significant functions in cellular processes. But as discussed above,
protein abundance changes are generally not as widespread and extensive
as transcriptome-level changes during the 24 h period. Comparison of our
diurnal proteome time-series to reported transcriptome time-series
substantiates data from earlier reports of ED vs EN studies examining
Arabidopsis rosettes (Baerenfaller et al., 2012; Seaton et al., 2018;
Uhrig et al., 2019) and circadian clock mutants (Graf et al., 2017) that
transcript changes often do not coincide with protein abundance
fluctuations (Baerenfaller et al., 2012; Graf et al., 2017). Changes in
protein phosphorylation could be dependent or independent of protein
abundance fluctuations. Our results show that the majority of
significantly changing diurnal phosphorylation events occur
independently of protein abundance changes, indicating that they most
likely regulate protein function (Duby \& Boutry, 2009; Le, Browning, \&
Gallie, 2000; Lillo et al., 2004; Muench, Zhang, \& Dahodwala, 2012).
Further research is required to elucidate the roles of the
phosphorylation events on the Arabidopsis proteins we have identified.
Based on our results it will be interesting to investigate which of the
seemingly stable proteins / phosphoproteins and significantly changing
phosphoproteins are in fact undergoing changes in their translation and
turnover, but maintain their overall abundance (Li et al., 2017).

\textbf{A small subset of the transition phosphoproteome has protein
level changes}

As the result of enrichment methods, one major question in
phosphoproteomics pertains to the quantified phosphorylation changes and
if they are the result of changes in protein levels. We therefore
performed an integrated analysis of the significantly changing proteome
and phosphoproteome to determine if and how phosphorylation and protein
abundance changes are related. Of the 226 proteins exhibiting a
significant change in phosphorylation (Table 1), 60\% (136 proteins)
were quantified in our proteome data (Supplemental Table 6). These
results are not unexpected because of the phosphopeptide enrichment
strategy and indicate that 40\% of the phosphorylated proteins in our
phosphoproteome dataset are of lower abundance and not amongst the 4762
total quantified proteins. Further assessment of significantly changing
phosphoproteins relative to the quantified proteome at the light
transitions found that 25\% (L-D) and 7.1\% (D-L) of the changing
phosphoproteins were not significantly changing at the protein level
(TOST P~value~[?] 0.05, \selectlanguage{greek}ε\selectlanguage{english}~=~0.4).

We then directly compared the changing phosphoproteome and proteome to
identify proteins exhibiting a significant change in diurnal protein
abundance and phosphorylation. We found that a total of six
phosphorylated proteins (totaling 2.1\% of all 288 proteins
significantly changing in protein abundance; Supplemental Table 6) had
concurrent abundance changes (Figure 5). These include nitrate reductase
1 (NIA1; AT1G77760) and 2 (NIA2; AT1G37130), protein kinase SnRK2.4
(AT1G10940), Rho guanyl-nucleotide exchange factor SPK1 (AT4G16340),
microtubule binding protein WDL5 (AT4G32330), and winged-helix
DNA-binding transcription factor family protein LARP1C (AT4G35890). NIA1
and 2 are directly related to nitrogen assimilation (Lillo, 2008; Lillo
et al., 2004), while WDL5 has been implicated in mitigating ammonium
toxicity through ETHYLENE INSENSITIVE 3 (EIN3) (Li et al., 2019).
SnRK2.4 binds fatty acid derived lipid phosphatidic acid to associate
with the plasma membrane (Julkowska et al., 2015) and responds to
changes in cell osmotic status (Munnik et al., 1999), while SPK1, WDL5
and LARP1C are connected to plant hormone signaling through abscisic
acid (WDL5; Yu et al., 2019), jasmonic acid (LARP1C; B. Zhang, Jia,
Yang, Yan, \& Han, 2012) and auxin (SPK1; Lin et al., 2012; Nakamura et
al., 2018). Of these three proteins with concerted phosphorylation and
abundance changes only SPK1 showed a parallel increase in abundance and
phosphorylation at the same transition (Figure 5), while WDL5 and LARP1C
exhibited opposing patterns of phosphorylation and abundance changes,
suggesting that phosphorylation may regulate their turnover. Proteins
involved in phytohormone signaling are regulated by both protein
phosphorylation and turnover (Dai et al., 2013; Qin et al., 2014),
suggesting that these three proteins may represent new examples of
hormone-mediated phosphodegrons or phosphor-inhibited degrons (Vu,
Gevaert, \& De Smet, 2018). To demonstrate this, further examination is
required of the ubiquitination status of these proteins and the
proximity of those ubiquitin modifications to the annotated
phosphorylation event.

\textbf{Motif analysis reveals diurnal utilization of phosphorylation
sites}

We examined our phosphoproteome data for enrichment of phosphorylation
motifs that are connected to known protein kinases using Motif-X
(motif-x.med.harvard.edu; Chou \& Schwartz, 2011; Schwartz \& Gygi,
2005). The significantly changing phosphorylated peptides at each
transition were analyzed against all quantified phosphopeptides
(P~value~[?]~0.05). Motifs corresponding to serine (pS) phosphorylation
sites were enriched at each transition, while enrichment of
phosphorylated threonine (pT) or tyrosine (pY) motifs was absent
(Supplemental Table 7). The lack of pY motif enrichment has also been
reported in other studies examining phosphoproteome changes under either
ED vs EN (Reiland et al., 2009; Uhrig et al., 2019) or free-running
circadian cycles (Choudhary et al., 2015; Krahmer et al., 2019). Only
one pT motif (pTP) has been previously associated with ED vs EN
phosphoproteome changes (Uhrig et al., 2019). The lack of an enriched
pTP motif here is likely due to our stringent multi-time point threshold
requirement for each phosphorylation site versus the comparison
previously performed between ED and EN only (Uhrig et al., 2019).
Furthermore, pS accounts for 84-86\% of all phosphorylation events in
plants, compared to only 10-12\% pT and 1-4\% pY (Nakagami et al., 2010;
Sugiyama et al., 2008), which makes it less likely to find an enrichment
of pT and/or pY motifs in the phosphoproteome. Of the phosphorylation
sites (site probability score [?]~0.8 we quantified, 82.8\%, 16.5\% and
0.7\% were pS, pT and pY respectively, which is similar to previously
reported distributions of phosphorylation events (Nakagami et al., 2010;
Sugiyama et al., 2008).

At the L-D transition, we found 16 motifs of which 10 correspond to
phosphorylation sites previously identified as targets of protein
kinases CaMKII, PAK1, extracellular signal-regulated kinase (ERK 1/2),
proto-oncogene c-RAF (RAF1), and cell division cycle 2 (CDC2) protein
kinase A and B (Supplemental Table 7). Six phosphorylation sites did not
correspond to known kinase motifs, therefore likely representing
currently uncharacterized and possibly plant-specific motifs considering
the large expansion of protein kinases in plants relative to humans
(Lehti-Shiu \& Shiu, 2012). At the D-L transition, four of five
identified motifs are known phosphorylation sites for checkpoint
kinase~1 (CHK1), PAK2, calmodulin kinase IV (CaMKIV) and casein kinase
(CKII) (Supplemental Table 7). CKII phosphorylates the core circadian
clock transcription factors LHY and CCA1 (Lu et al., 2011), which also
peak at the D-L transition (Kusakina \& Dodd, 2012). Consistent with
this, we find enrichment of phosphorylation events corresponding to the
CKII phosphorylation motif for these proteins at the D-L transition.

Calcium (Ca\textsuperscript{2+}) has been implicated in circadian
regulation (Marti Ruiz et al., 2018), suggesting that calcium-dependent
calmodulin (CaM) protein kinase orthologs are interesting candidates for
mediating circadian clock signaling. Unlike the enrichment of CKII
motifs at only the D-L transition, we find enrichment of
Ca\textsuperscript{2+} related kinases CaMKII (D-L and L-D) and CaMKIV
(D-L) phosphorylation motifs at both transitions (Supplemental Table 7).
Previous analyses identified other Ca\textsuperscript{2+} kinase motifs
enriched at both ED and EN (CDPK-like motifs; Uhrig et al., 2019).
SnRK1-related motifs were identified in the phosphoproteome data from
Arabidopsis CCA1-Ox plants growing in a free-running cycle (Krahmer et
al., 2019). SnRK1 is a central mediator of energy signaling between
different organelles and also phosphorylates CDPKs (Wurzinger,
Nukarinen, Nagele, Weckwerth, \& Teige, 2018). Together, these studies
and the results presented here suggest a broader role for
Ca\textsuperscript{2+} in diurnal plant cell regulation during the L-D
and D-L transitions.

Compared to humans, plants have more protein kinases (Lehti-Shiu \&
Shiu, 2012), but most of their targets remain unknown. Our
phosphoproteome results, together with previously reported
phosphoproteome datasets (Choudhary et al., 2015; Krahmer et al., 2019;
Reiland et al., 2009; Uhrig et al., 2019) provide a compilation of
specific phosphorylation motifs that are rapidly modified at light-dark
diurnal transitions. When combined with transcriptional expression data
for lower abundant proteins kinases (Uhrig et al., 2019), the
phosphoproteome changes uncovered here in other studies (Choudhary et
al., 2015; Krahmer et al., 2019; Reiland et al., 2009; Uhrig et al.,
2019) narrow the protein kinase sub-families to those most likely
catalyzing diurnal phosphorylation events.

\textbf{Key plant processes involve independent changes in both proteome
and phosphoproteome}

We queried the data for all measured proteins that change in their
abundance and/or phosphorylation status during the 24~h diurnal period.
This revealed predominantly proteins involved in translation, metabolism
and cell wall biosynthesis, suggesting that these cellular processes are
subject to diurnal regulation at the protein level. The translation
rates of Arabidopsis enzymes of light-induced metabolic reactions
fluctuate diurnally and this correlates with their activity (Seaton et
al., 2018). For example, several central metabolic enzymes are
synthetized at 50 to 100\% higher rates during the light phase of the
photoperiod (Pal et al., 2013; Piques et al., 2009). We identified 15
proteins involved in protein translation that have diurnal changes in
abundance (Table 3; Supplemental Table 2). Although they belong to
several clusters shown in Figure 1A, nine of the proteins are grouped in
CL3 that represents a general protein increase at the onset of light. In
addition, we found 8 translation-related proteins with changes in their
phosphorylation status at L-D and D-L transitions, of which 5/8 are
eukaryotic initiation factor (eIF) proteins (Table 3; Supplemental Table
8). Phosphorylation affects eukaryotic translation at the initiation
step (Jackson, Hellen, \& Pestova, 2010; Le et al., 2000; Muench et al.,
2012), and numerous eIFs and ribosomal proteins show differences in
phosphorylation levels between light and dark periods (Boex-Fontvieille
et al., 2013; Turkina, Klang Arstrand, \& Vener, 2011; Uhrig et al.,
2019). Our analysis revealed additional diurnally regulated eIFs and
suggests that specific translational regulation mechanisms and ribosome
composition could be controlled by light changes (e.g. day versus night)
and also throughout the 24h photoperiod.

Several enzymes related to fatty acid, biotin, mitochondrial acetyl-CoA
and chloroplast metabolism have diurnal changes in abundance (Figure 2;
Table 3; Supplemental Table 2). Of particular interest are peroxisomal
fatty acid \selectlanguage{greek}β-\selectlanguage{english}oxidation enzymes 3-ketoacyl-CoA thiolase 2 (KAT2/PKT3;
AT2G33150) and 3-hydroxyacyl-CoA dehydrogenase (MFP2/AIM1-like;
AT3G15290). KAT2 is a central enzyme in peroxisomal fatty-acid
degradation for the production of acetyl-CoA that is required for
histone acetylation, which in turn affects DNA methylation (Wang et al.,
2019), and ABA signaling (Jiang, Zhang, Wang, \& Zhang, 2011), which is
essential to daily regulation of stomatal conductance. MFP2/AIM1-like is
an uncharacterized ortholog of MULTIFUNCTIONAL PROTEIN 2 (MFP2) and
ENOYL-COA ISOMERASE (AIM1), which are involved in indole-3-acetic acid
and jasmonic acid metabolism (Arent, Christensen, Pye, Norgaard, \&
Henriksen, 2010; Delker, Zolman, Miersch, \& Wasternack, 2007). KAT2
loss-of-function mutants require sucrose to supplement plant acetyl-CoA
production, suggesting that diurnal changes in fatty acid degradation
through KAT2 and MFP2/AIM1-like are tied to sucrose production and that
products downstream of KAT2 and MFP2/AIM1-like (e.g. hormones) are
essential to plant growth and development (Pinfield-Wells et al., 2005).
Previously, fatty acid and lipid metabolism in leaves and seedlings has
been suggested to be diurnally / circadian clock regulated (Gibon et
al., 2006; Hsiao et al., 2014; Kim, Nusinow, Sorkin, Pruneda-Paz, \&
Wang, 2019; Nakamura, 2018; Nakamura et al., 2014). This includes:
diurnal changes in fatty acids and lipids (Gibon et al., 2006) in
wild-type plants as well as diurnal changes in triacylglycerol~(Hsiao et
al., 2014) and phosphatidic acid (Kim et al., 2019) in the circadian
clock double mutant \emph{lhycca1} . Complementing these studies, our
findings provide a new protein-level understanding of where fatty acid
and lipid regulation may rest which differs from our current transcript
/ metabolite based knowledge, indicating that further protein-level
investigations are required.

We identified enzymes in primary metabolism that changed their
phosphorylation status at the D-L and L-D transitions (Table 3;
Supplemental Table 9). Several of these enzymes were previously
identified as phosphorylated proteins (PhosPhat 4.0) (Heazlewood et al.,
2008). Our sampling of three closely spaced time-points provides new
information about the rate of protein phosphorylation changes at each
transition. Moreover, our results demonstrate that in Arabidopsis
metabolic enzymes are subject to changes in either protein abundance or
phosphorylation, or both, which likely is of regulatory relevance for
metabolic pathway flux. This information is useful when deciding which
protein isoform would be best for engineering increased pathway flux if
two are present simultaneously. For example, in case of the
well-characterized NITRATE REDUCTASE 1 (NIA1; AT1G77760) and 2 (NIA2;
AT1G37130) proteins their abundance parallels transcript levels but
opposed to changes in protein phosphorylation, with NIA2 showing more
rapid changes in phosphorylation at the D-L transition compared to NIA1
(Table 3; Supplemental Figure 4). NIA1 and NIA2 are regulated both
transcriptionally and post-translationally by phosphorylation (Lillo,
2008; Lillo et al., 2004, Wang, Du, \& Song, 2011). Our results define a
rate of change in the phosphorylation of these related isozymes at the
L-D and D-L transitions and define when peak NIA1 and NIA2 protein
levels occur relative to peak transcript levels. These new insights help
to better understand the precise functional differences between NIA1 and
NIA2. \emph{NIA1} and \emph{NIA2} have tissue-specific gene expression
profiles, with \emph{NIA1} ~expression generally complementing that
of~\emph{NIA2} ~in the same organ. NIA1 was predominantly found in
leaves, while NIA2 was predominantly found in meristematic tissue (Olas
\& Wahl, 2019). We analyzed whole Arabidopsis rosettes before bolting,
of which developing leaves and apical meristematic tissue comprises only
a small amount of total tissue. Therefore, it will be interesting to
determine if the observed difference in NIA1 and NIA2 phosphorylation
rates at known regulatory phosphorylation sites reflect a higher
sensitivity of NIA2 to changes in nitrate levels in meristematic and
developing tissues (Olas et al., 2019).

Plant genomes often encode multiple forms of enzymes (isozymes) in
metabolic pathways. The temporal rate at which related co-expressed
protein orthologs are modified by a post-translational modifications
such as protein phosphorylation provides more detailed information on
cellular regulation. Our analysis of the phosphoproteome at three D-L
and L-D time-points shows the dynamics of phosphorylation events at both
transitions. Further information about the temporal rate at which
different phosphorylation sites in a protein are utilized can then be
combined with e.g., enzyme kinetics to reveal how metabolic flux through
multiple enzyme reactions may be fine-tuned by PTMs versus changes in
protein abundance. NIA1 and NIA2 are a good example that resolving
differences in PTM rates helps to better understand the role of PTMs in
protein regulation.

We also find diurnal changes in both protein abundance and protein
phosphorylation for enzymes involved in carbohydrate metabolism (Table
3; Supplemental Table 2, 4). Starch biosynthesis and degradation is
diurnally regulated to manage the primary carbon store in plants
(Kotting, Kossmann, Zeeman, \& Lloyd, 2010). For example, granule bound
starch synthase 1 (GBSS1; AT1G32900) levels increase preceding the D-L
transition, likely in anticipation of starch granule formation
(Szydlowski et al., 2011). Debranching enzyme 1 (DBE1, AT1G03310)
increases in abundance at the end of the light period to facilitate
effective starch degradation in the dark (Delatte, Trevisan, Parker, \&
Zeeman, 2005). Other enzymes such as beta-amylase 1 (BAM1; AT3G23920)
were phosphorylated immediately after the onset of light. Although the
function of BAM1 phosphorylation is currently unknown, our results
provide information to understand its regulation in stomatal starch
degradation and sensitivity to osmotic changes in rosettes (Zanella et
al., 2016).

Cell wall metabolic enzymes involve both diurnal fluctuations in protein
abundance (Figure 2, Table 3; Supplemental Table 2) and changes in
phosphorylation status (Figure 3, Table 3; Supplemental Table 4) at the
D-L and L-D transitions. Cell wall biosynthesis is a major metabolic
activity of growing plants (Barnes \& Anderson, 2017; Cosgrove, 2005).
We find that cellulose synthase enzymes CESA5 (AT5G09870) and CSLC6
(AT3G07330) were rapidly phosphorylated at the L-D transition. CESA5 has
been shown to be phosphorylated and phosphorylation memetic-mutant
enzymes increase movement of the cellulose synthase complex (CSC) in
dark-grown seedlings, indicating a photoperiod-dependent regulation cell
wall biosynthesis (Bischoff et al., 2011). Diurnal cellulose synthesis
may also be controlled by the intracellular trafficking of CSC enzymes
as a result of changes in metabolism (Ivakov et al., 2017). In
dark-grown hypocotyls the ratio of CESA5 to CESA6 phosphorylation in the
CSC complex is important for cellulose synthesis (Bischoff et al.,
2011). Our phosphoproteome results now provide additional information on
the rate of CESA5 phosphorylation at the onset of that dark period. We
also find phosphorylation of the plasma membrane
H\textsuperscript{+}-ATPase HA1 (AT2G18960) at the L-D transition
(Figure 3B). Phosphorylation activates H\textsuperscript{+}-ATPases
(Duby \& Boutry, 2009; Sondergaard, Schulz, \& Palmgren, 2004) and
implicates HA1 as a primary candidate H\textsuperscript{+}-ATPase in
diurnal cell wall acidification to facilitate cell expansion during the
night (Ivakov et al., 2017).

\textbf{CONCLUSION}

To date, detailed analyses of plant functions during a 24 h diurnal
cycle have mostly focused on genome-wide changes in gene expression.
Transcript-level changes can serve as a proxy for protein-level changes,
but in plants transcript levels often do not correlate with protein
abundance. While proteomes have a narrower dynamic range than
transcriptomes, they nevertheless complement transcriptome studies
because they provide direct insights into protein-level changes. Our
quantitative analysis of the proteome over a 12~h light : 12~h dark 24~h
photoperiod and the phosphoproteome at the L-D and D-L transitions
during the diurnal cycle in a single experimental workflow has generated
new information on diurnal abundance fluctuations and/or phosphorylation
changes for Arabidopsis proteins involved in different cellular and
biological processes (Figure 6). The identified proteins and
phosphoproteins provide a useful basis for further experimental studies.
In particular, it will be interesting to understand the specific
functions of diurnally fluctuating ribosomal proteins and other proteins
involved in translation considering that hundreds of ribosomal protein
isoforms are encoded by plant genomes with little information of what
dictates their combinatorial assembly. The regulation of protein
translation in plants at the protein complex level remains poorly
understood, but specific time-of-day abundance peaks for these proteins
suggests that temporal differences in the ribosome complex exists that
likely correlated with the specific time-of-day requirements of the
plant cell. Further elucidation of ribosome and protein translation
regulation will be instrumental in filling the current knowledge gap
between the transcriptome and proteome. Lastly, our phosphoproteome
analysis during the transitions from D-L and L-D provides new
information about candidate protein kinase sub-families catalyzing the
phosphorylation events, providing new opportunities for future
systems-level and targeted studies.

\textbf{ACKNOWLEDGEMENTS}

This work was funded by TiMet - Linking the Clock to Metabolism (Grant
Agreement 245143) supported by the European Commission
(FP7-KBBE-2009-3). Experiments conducted at Forschungszentrum J\selectlanguage{ngerman}ülich
were partly funded by the Helmholtz Association.



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\textbf{FIGURES:}

\textbf{Figure 1: Analysis of the diurnal proteome: clustering,
enrichment analysis and subcellular localization.}

(A) Significantly changing proteins (FC~\selectlanguage{english}[?]~1.5, ANOVA P~value~\selectlanguage{english}[?]~0.05, \selectlanguage{english}[?]~2
peptides) were subjected to an unsupervised clustering analysis (GProX;
http://gprox.sourceforge.net) resolving 6 protein clusters. Y- and
X-axis depict standardized expression level and harvest time (Zeitgeber
time; ZT), respectively. Median expression is depicted in blue. (B) Term
enrichment analysis of significantly changing proteins using SetRank
(P~value~\selectlanguage{english}[?]~0.01, size~\selectlanguage{english}[?]~2). (C) Standardized diurnal transcript
expression level of each corresponding clustered protein (Log10). Median
expression is depicted in blue. Transcript expression level was obtained
from Diurnal DB (http://diurnal.mocklerlab.org/). (D) \emph{In silico}
subcellular localization analysis of significantly changing proteins
using SUBAcon (SUBA3; http://suba3.plantenergy.uwa.edu.au). Bracketed
numbers represent the number of proteins per cluster.

\textbf{Figure 2: Interaction networks of the diurnal proteome.}

An association network analysis using STRING-DB (https://string-db.org/)
of statistically significant diurnally changing proteins was performed
using the generated unsupervised clusters shown in Figure 1. Edge
thickness indicates confidence of the connection between two nodes
(0.5~-~1.0). Changing proteins (grey circles) are labeled by either
their primary gene annotation or Arabidopsis gene identifier (AGI). The
colored outline of each node represents the \emph{in silico} predicted
subcellular localization of this protein (SUBAcon;
suba3.plantenergy.uwa.edu.au). Nucleus (red), cytosol (orange), plastid
(green), mitochondria (blue), plasma membrane (purple), peroxisome
(dark~yellow), endoplasmic reticulum/golgi/secreted (black) are
depicted. A second layer of STRING-DB identified proteins (white nodes)
not found in each respective significantly changing protein cluster was
used to highlight the interconnectedness of proteins in the cluster.
Multiple nodes encompassed by a labelled grey circle represent proteins
involved in the same cellular process.

\textbf{Figure 3: Comparative analysis of diurnal proteome to
free-running circadian proteome (Krahmer \emph{et al.,} 2019).}

(A) Number of proteins measured in this study (blue circle) and
Krahmer\emph{et al.} (2019) (orange circle). Number of stable proteins
(black), fluctuating proteins in our study only (magenta), Krahmer
\emph{et al.}(2019) only (green) and both studies (blue). (B) Table of
21 proteins that show significant (B.Q) fluctuation using JTK with their
respective peak time period for protein and transcript levels (Diurnal
DB, http://diurnal.mocklerlab.org/). (C and D) Normalized (Median~=~0,
Amplitude of 2) protein levels of 15 proteins both fluctuating in
protein and transcript levels (gray lines) shifted to peak at time zero
for protein levels in (C) and transcript levels in (D). Protein data was
plotted twice to visualize a 48 h timeframe. The theoretical cosine
functions with associated 99\% confidence interval for protein levels
(C, red) and transcript levels (D, blue) are shifted by 5.5~h.

\textbf{Figure 4: Interaction networks of the diurnal phosphoproteome at
the D-L and L-D transitions.}

An association network analysis of statistically significant diurnally
changing phosphorylated proteins was performed using the STRING-DB
(ANOVA P~value~\selectlanguage{english}[?]~0.05). Edge thickness indicates strength of the
connection between two nodes (0.5~-~1.0). Phosphorylated proteins (grey
circles) are labeled by either their primary gene annotation or
Arabidopsis gene identifier (AGI). Outer circle around each node depicts
the standardized relative log2 FC in phosphorylation status of this
protein between time-points. The sliding scale of yellow to blue
represents a relative increase and decrease in phosphorylation,
respectively. The inner colored circles represent \emph{in
silico}predicted subcellular localization (SUBAcon;
suba3.plantenergy.uwa.edu.au). Nucleus (red), cytosol (orange), plastid
(green), mitochondria (blue), plasma membrane (purple), peroxisome
(dark~yellow), endoplasmic reticulum/golgi/secreted (black) are
depicted. A second shell of 5 STING-DB proteins (white circles) not
found in our dataset was used to highlight the interconnectedness of the
network. Multiple nodes encompassed by a labelled grey circle represent
proteins involved in the same cellular process.

\textbf{Figure 5: Proteins exhibiting a significant change in both
diurnal protein abundance and protein phosphorylation status.}

Six proteins were found to significantly change in protein abundance and
protein phosphorylation: AT1G10940 (SnRK2.4; blue), AT1G37130 (NIA2;
black), AT1G77760 (NIA1; grey), AT4G16340 (TPX2; red), AT4G32330 (SPK1;
yellow), AT4G35890 (LARP1c; green). (A) Diurnal protein abundance change
profile. Standardized log2 FC values are plotted relative to ZT. (B) D L
and (C) L-D phosphorylation change profiles. Standardized log2 FC values
are plotted relative to transition time-point either 10 or 30 min before
light (BL), after light (AL), before dark (BD) or after dark (AD).

\textbf{Figure 6: Schematic representation of Arabidopsis cellular and
biological processes with diurnal fluctuations in protein abundance or
protein phosphorylation.}

The inner three circles show terms of processes involving proteins with
a maximal change in abundance during the day (yellow) or night (black).
The outer circle show terms of processes involving proteins with changes
in protein phosphorylation at the dark-to-light (D-L) transition (top)
or light-to-dark (L-D) transition (bottom). The segments of each inner
circle relative to ZT0 (day) or ZT12 (night) represent the approximate
time interval in which proteins (ZT) and phosphoproteins (30~min before
light or dark, 10 and 30~min after light or dark) involved in each
process have their maximal change. The cellular and biological terms
shown here were obtained by GO term enrichment of each protein and
phosphoprotein cluster as outlined in Materials and Methods.

\textbf{TABLES:}

\textbf{Table 1: Proteome and phosphoproteome coverage.}

Summary of the identified, quantified and significantly changing diurnal
proteins, phosphopeptides and phosphoproteins. Quantification confidence
thresholds are shown for the proteome (proteins identified by \selectlanguage{english}[?]~2
proteotypic peptides) and the phosphoproteome (site probability score
\selectlanguage{english}[?]~0.8) quantified in \selectlanguage{english}[?]~3 biological replicates for each time point of
the diurnal cycle and for each of the three time-points at the L-D and
D-L transitions. The significance thresholds are shown for the proteome
(FC~\selectlanguage{english}[?]~1.5; ANOVA P~value~\selectlanguage{english}[?]~0.05) and the phosphoproteome (ANOVA
P~value~\selectlanguage{english}[?]~0.05). Application of proteome and phosphoproteome
significance thresholds are denoted by a single (*) and double (**)
asterisks, respectively.

\textbf{Table 2: GSEA of significantly changing phosphoproteins at the
D-L and L-D transition.} GSEA was performed using SetRank
(P~value~\selectlanguage{english}[?]~0.01; FDR~\selectlanguage{english}[?]~0.05, minProt~=~2).

\textbf{Table 3: Proteins involved in plant cell processes with
independent changes in abundance and/or phosphorylation.}

\textbf{SUPPORTING INFORMATION:}

\textbf{Supplemental Figures}

\textbf{Supplemental Figure 1: Schematic depiction of the experimental
workflow.}

The total proteome and phosphoproteome experimental workflow is shown in
black and blue, respectively. Light and dark boxes represent the 12~h
light : 12~h dark photoperiod. The numbers on top of the boxes represent
the tissue harvest times for the total proteome analysis (Zeitgeber
time; ZT). The numbers below the boxes represent the tissue harvest
times for the phosphoproteome analysis (minutes before or after a
transition from L-D and D-L).

\textbf{Supplemental Figure 2: Hierarchical heatmap of significantly
changing diurnal phosphopeptides at the D-L transition.}

The hierarchical heatmap was generated using the R package Pheatmap and
Euclidean distance. Standardized relative log2 FC in phosphopeptide
abundance is shown along with the corresponding AGI and phosphopeptide
with phosphorylation site probabilities. GO terms of proteins in the
heatmap clusters are shown on the right together with their predicted
subcellular localization (SUBAcon). The segments of the circles
represent the nucleus (red), cytosol (orange), plastid (green),
mitochondria (blue), plasma membrane (purple) and other (black)
localizations. The numbers below each pie chart represent the unique
protein identifications. The time points of sampling for phosphoprotein
analysis were 30~min before light (BL30), 10~min after light (AL10) and
30~min after light (AL30).

\textbf{Supplemental Figure 3: Hierarchical heat map of significantly
changing diurnal phosphopeptides at the L-D transition.}

The hierarchical heat map was generated using the R package Pheatmap and
Euclidean distance. Standardized relative log2 FC in phosphopeptide
abundance is shown along with the corresponding AGI and phosphopeptide
with phosphorylation site probabilities. GO terms of proteins in the
heatmap clusters are shown on the right together with their predicted
subcellular localization (SUBAcon). The segments of the circles
represent the nucleus (red), cytosol (orange), plastid (green),
mitochondria (blue), plasma membrane (purple) and other (black)
localizations. The numbers below each pie chart represent the number of
unique protein identifications. The time points of sampling for
phosphoprotein analysis were 30~min before dark (BD30), 10~min after
dark (AD10) and 30~min after dark (AD30).

\textbf{Supplemental Figure 4: Diurnal phosphorylation of nitrate
reductase 1 (NIA1) and 2 (NIA2).} (A-B) Diurnal fluctuations of NIA1 and
2 mRNA and protein levels, and phosphorylation status. Relative changes
in mRNA and protein levels were assessed over 24 h. Transcript data was
extracted from Diurnal DB (http://diurnal.mocklerlab.org/). Relative
changes in protein phosphorylation were measured at the D-L and L-D
transitions only (see Materials and Methods). (C) Model of NIA2 protein
structure including molybdenum cofactor (MoCo), dimerization (Dimer),
cytochrome b5 (Cyt B), FAD and NADH binding domains in addition to hinge
regions 1 and 2. Phosphorylation of the three annotated phosphorylation
sites in NIA2 shown as circles is light-dependent (yellow),
dark-dependent (blue) and nitric oxide-induced (white; Wang et al;
2011).

\textbf{Supplemental Tables}

Supplemental Table 1: All identified and quantified proteins.

Supplemental Table 2: Significantly changing diurnal proteins.

Supplemental Table 3: All identified and quantified phosphoproteins.

Supplemental Table 4: Significantly changing diurnal phosphoproteins.

Supplemental Table 5: Benchmark phosphoproteins.

Supplemental Table 6: Comparative proteome and phosphoproteome analysis.

Supplemental Table 7: MotifX data for D-L and L-D transitions.

Supplemental Table 8: Standardized D-L and L-D changes in the
phosphorylation of protein translation.

Supplemental Table 9: Standardized D-L and L-D transition phosphopeptide
rates-of-change.

\textbf{Supplemental Data}

Supplemental Data 1-6: The matched transcript and protein expression
profiles for genes in clusters 1 -- 6 respectively in Figure 1.

Supplemental Data 7: Comparison of changing diurnal proteome and a
circadian proteome reported by Kramer et al. (2019).\selectlanguage{english}
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