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.