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LARGE-SCALE BIOLOGY ARTICLE
Systems-Wide Analysis of Acclimation Responses to
Long-Term Heat Stress and Recovery in the Photosynthetic
Model Organism Chlamydomonas reinhardtii
W OPEN
Dorothea Hemme,a,b,1 Daniel Veyel,a,b,1 Timo Mühlhaus,a,b,1 Frederik Sommer,a,b Jessica Jüppner,b
Ann-Katrin Unger,c Michael Sandmann,d,2 Ines Fehrle,b Stephanie Schönfelder,b Martin Steup,d,3 Stefan Geimer,c
Joachim Kopka,b Patrick Giavalisco,b and Michael Schrodaa,b,4
a Molekulare
Biotechnologie and Systembiologie, TU Kaiserslautern, D-67663 Kaiserslautern, Germany
für Molekulare Pflanzenphysiologie, D-14476 Potsdam-Golm, Germany
c Zellbiologie/Elektronenmikroskopie, Universität Bayreuth, D-95440 Bayreuth, Germany
d Institut für Biochemie und Biologie, Universität Potsdam, D-14476 Potsdam-Golm, Germany
b Max-Planck-Institut
We applied a top-down systems biology approach to understand how Chlamydomonas reinhardtii acclimates to long-term
heat stress (HS) and recovers from it. For this, we shifted cells from 25 to 42°C for 24 h and back to 25°C for ‡8 h and
monitored abundances of 1856 proteins/protein groups, 99 polar and 185 lipophilic metabolites, and cytological and
photosynthesis parameters. Our data indicate that acclimation of Chlamydomonas to long-term HS consists of a temporally
ordered, orchestrated implementation of response elements at various system levels. These comprise (1) cell cycle arrest; (2)
catabolism of larger molecules to generate compounds with roles in stress protection; (3) accumulation of molecular
chaperones to restore protein homeostasis together with compatible solutes; (4) redirection of photosynthetic energy and
reducing power from the Calvin cycle to the de novo synthesis of saturated fatty acids to replace polyunsaturated ones in
membrane lipids, which are deposited in lipid bodies; and (5) when sinks for photosynthetic energy and reducing power are
depleted, resumption of Calvin cycle activity associated with increased photorespiration, accumulation of reactive oxygen
species scavengers, and throttling of linear electron flow by antenna uncoupling. During recovery from HS, cells appear to
focus on processes allowing rapid resumption of growth rather than restoring pre-HS conditions.
INTRODUCTION
Global warming will increase occurrences of extreme heat stress
(HS). In particular when occurring during anthesis, HS may severely reduce crop yield and therefore is predicted to pose a significant problem for agriculture (Lobell et al., 2011; Deryng et al.,
2014). To identify entry points for metabolic engineering or
chemical approaches aiming to improve crop plant resistance to
HS, a comprehensive understanding of how plants respond to HS
and recover from it is required. Although many aspects of how
plant systems respond to HS have been studied in the past, many
questions remain open (Sharkey, 2005; Mittler et al., 2012).
1 These
authors contributed equally to this work.
Institut für Chemie, Universität Potsdam, Am Mühlenberg 3,
D-14476 Potsdam-Golm, Germany.
3 Current address: Department of Molecular and Cellular Biology,
University of Guelph, Guelph, Ontario N1G2W1, Canada
4 Address correspondence to [email protected].
The author responsible for distribution of materials integral to the findings
presented in this article in accordance with the policy described in the
Instructions for Authors (www.plantcell.org) is: Michael Schroda
([email protected])
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2 Current address:
One of them is how plants sense heat and integrate signals
from sensors to elicit a coordinated response. A universally
conserved trigger for HS responses is the accumulation of unfolded proteins. In eukaryotes, these are known to be sensed in
the cytosol, the endoplasmic reticulum (ER), and mitochondria
and signaled to the nucleus to initiate appropriate transcriptional
responses (Voellmy and Boellmann, 2007; Walter and Ron,
2011; Haynes et al., 2013). Recent work suggested that an unfolded protein response also exists in the chloroplast (Yu et al.,
2012; Schmollinger et al., 2013; Ramundo et al., 2014). Plasma
membrane fluidity has been identified as an important temperature sensor in land plants that appears to lie upstream of the
unfolded protein response (Sangwan et al., 2002; Suri and
Dhindsa, 2008; Saidi et al., 2009; Wu et al., 2012). Increased
membrane fluidity appears to open calcium channels in the
plasma membrane and inflowing calcium to trigger signaling
cascades, including an H2O2 burst (Königshofer et al., 2008),
leading to the activation of the HS response. HS also was shown
to result in the activation of phospholipase D and phosphatidylinositol4-phosphate 5-kinase, thus leading to the accumulation of lipid
signaling molecules that might trigger specific HS responses
(Mishkind et al., 2009). Yet unidentified triggers may stem from
metabolism, with metabolites serving in HS signaling that
change in abundance due to temperature-induced changes of
enzyme activities.
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Another open question is related to how and why HS affects
photosynthetic activity. Photosystem II (PSII) is considered the
most thermosensitive complex of the photosynthetic light reactions (Berry and Bjorkman, 1980). However, the temperatures
at which PSII damage was observed often were not physiological and the reduced rates of photosynthesis at moderate HS are
more likely to be caused by reduced capacities of downstream
reactions than by PSII damage (Sharkey, 2005; Zhang and Sharkey,
2009; Sharkey and Zhang, 2010). One of the downstream pathways
affected by moderate HS is the Calvin cycle (Weis, 1981), and a key
activity affected is that of Rubisco activase (Feller et al., 1998).
Rubisco activase catalyzes the release from the Rubisco active site
of ribulose-1,5-bisphosphate and other sugar phosphates that,
when bound to Rubisco prior to its carbamylation and Mg2+ binding,
inactivate the enzyme (Jensen, 2000). The question is whether the
inactivation of thermolabile Rubisco activase by HS is an undesired
effect, or rather a mechanism implemented specifically as part of
a regulated response to moderate HS, as suggested previously
(Sharkey, 2005; Sage et al., 2008; Sharkey and Zhang, 2010).
Recently, we used a shotgun proteomics approach based on 15N
metabolic labeling to monitor changes in abundances of the soluble proteome in heat stressed Chlamydomonas reinhardtii cells
during a 3-h time course (Mühlhaus et al., 2011). When comparing
results on Chlamydomonas with proteomics studies conducted on
various land plants (Skylas et al., 2002; Majoul et al., 2003, 2004;
Süle et al., 2004; Ferreira et al., 2006; Lee et al., 2007; Palmblad
et al., 2008; Valcu et al., 2008; Xu and Huang, 2008; Scafaro et al.,
2010; Xu and Huang, 2010), we concluded that the upregulation of
chaperones, FKBP65 orthologs, and thiamine biosynthesis protein
and the downregulation of ferredoxin-NADP(H) oxidoreductase,
Rubisco, methionine synthase, adenosylhomocysteinase, and
S-adenosylmethionine synthetase are conserved in the green lineage. However, there were also many inconsistencies between these
studies. An intriguing discrepancy was that in Chlamydomonas we
could not detect any increase in the abundance of proteins involved
in redox regulation/scavenging of reactive oxygen species, which
was observed in several studies on land plants (Ferreira et al., 2006;
Lee et al., 2007; Palmblad et al., 2008; Xu and Huang, 2008; Scafaro
et al., 2010). Whether these inconsistencies were due to different
durations of HS monitored, the different methodologies applied (2DPAGE versus shotgun proteomics), or organism-specific responses
remained unclear.
Top-down systems biology approaches, where responses at
multiple system levels are monitored over time and integrated to
a more holistic picture, appear helpful to shed light on the many
open questions regarding responses of plant cells to HS. In fact,
Chlamydomonas is an ideal plant model for such approaches because (1) as a single-celled organism all cells are of the same type
and different cell cycle stages may be averaged out by growing
cells in asynchronous cultures, (2) cells can be cultured under
highly controlled conditions and changes in environmental conditions applied homogenously, (3) gene families are less complex
than those of land plants (Merchant et al., 2007). Accordingly,
several recent studies have taken advantage of these characteristics to deepen our understanding of acclimation responses to N
starvation (Blaby et al., 2013; Schmollinger et al., 2014), copper and
iron limitation (Castruita et al., 2011; Höhner et al., 2013), anoxia
(Hemschemeier et al., 2013), or increasing light (Mettler et al., 2014).
In this study, we employed a top-down systems biology approach in which we monitored changes in the proteome (soluble
and membrane proteins), metabolome, and lipidome as well as
changes of many cytological and physiological parameters in
Chlamydomonas during long-term HS and recovery. By integrating data from the different systems levels, we were able to
obtain many insights that provide tentative answers to some of
the open questions outlined and will be the groundwork for further
experiments.
RESULTS
Experimental Setup
To monitor acclimation responses of Chlamydomonas to HS, we
subjected cells growing under mixotrophic conditions in the exponential phase at 25°C to a 42°C HS treatment for 24 h and for
recovery transferred cells back to 25°C for up to 24 h (Figure 1).
Chlamydomonas cells were previously shown to fully recover from
a 24-h HS treatment at 42°C (Mühlhaus et al., 2011). During this
HS and recovery time course, we monitored changes in the proteome, metabolome, and lipidome, as well as cytological and
physiological parameters. Differences in sampling frequency on
the various system levels are based on the different time scales
at which they change and on cost considerations.
Cytological Parameters
We first monitored several cytological parameters during the HS
and recovery time course, i.e., cell numbers, cell size distribution,
cellular ultrastructure, and contents of DNA, protein, chlorophyll,
triacylglycerol (TAG), and starch. At 25°C, cells grew exponentially
with a generation time of ;8 to 9 h (Figure 2A). Immediately after
transfer to 42°C, cells stopped dividing and this division arrest
lasted until ;8 h of recovery when cells resumed exponential
growth. To test whether the cell division arrest was accompanied
by an arrest of DNA replication, we determined the ploidy level of
the cells during HS and recovery by flow cytometry (Figure 2B). In
our study, we used cells grown asynchronously under mixotrophic
conditions. Accordingly, before HS, 77% of the cells were 1n,
21.7% were 2n, while only ;1.3% were 4n (note that vegetative
Chlamydomonas cells are haploid; Harris, 2001). This distribution
hardly changed during the 24 h HS treatment; consequently, the
same was true for the average DNA content. DNA replication resumed with a short lag phase of 2 to 3 h after transferring cells back
to 25°C. The fraction of 1n cells decreased, while that of 2n and 4n
cells increased. After ;7 to 8 h of recovery, the distribution of 1n
and 2n cells was equal and later 2n and 4n cells became even more
abundant. A trend back to the initial distribution was observed after
24 h of recovery.
At 25°C, cells had a mean diameter of 4.9 mm, which increased to 6 mm during the 24 h HS treatment (Figure 2C). The
increase in cell diameter was more pronounced during the first 7
h of HS than at later time points. Within the first 7 h of recovery,
the average cell diameter continued to increase to a peak value
of 6.4 mm and began to decline when cells started dividing
again. Hence, while cell division was arrested during heat stress,
cells continued to grow at a slow rate.
Responses to Heat Stress and Recovery
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Figure 1. Experimental Setup and Sampling Scheme.
Chlamydomonas cells were grown in TAP medium at 25°C, exposed to HS at 42°C for 24 h at t = 0 h, and transferred back to 25°C at t = 24 h for
a recovery period of up to 24 h. The gray area depicts the duration of HS. Time points taken for analyses at individual system levels are indicated by
vertical lines, and dotted gray lines represent time points evaluated on five or more system levels.
At 25°C, the average protein content per cell was 13.9 pg (Figure
2D). It increased by 57% to 21.8 pg during the 24 h HS treatment,
continued to increase by 10% to a peak value of 24 pg during the
first 8 h of recovery, and dropped to 20.1 pg after 24 h of recovery
when cells were dividing again. The average chlorophyll content per
cell was 0.84 pg. It increased by 24% to 1.04 pg during 24 h HS
and declined only slightly during 24 h of recovery. Strikingly, after
24 h of recovery average cell diameter and average contents of
DNA, protein and chlorophyll were higher than pre-HS values, although cells had divided ;1.6 times.
The cellular TAG content increased dramatically within the first
2 h of HS, but declined mildly between 2 and 24 h of HS (Figure 2E).
Immediately after transfer to 25°C, TAG levels dropped slightly, but
stayed constant during the remaining 8 h monitored in the recovery
phase. In contrast to the other cellular macromolecules investigated, the starch content per cell declined within the first 2 h of
HS by 28%, but during the remaining 22 h of HS increased to levels
that were 46% above those present before HS. After transferring
cells back to 25°C, the starch content rose strongly and after 8 h
recovery was 2.7-fold higher than before the onset of HS.
Finally, electron microscopy images were taken to reveal structural rearrangements and possible cell damage during heat stress
and recovery (Figure 2F). Based on the analysis of 520 images of
cells from 0, 3, 12, and 24 h HS, and from 4 and 8 h recovery, cell
damage occurred only sporadically. Consistent with the increase in
cellular TAG content detected by liquid chromatography-mass
spectrometry (LC-MS), we observed the accumulation of large lipid
bodies during HS. Many of these lipid bodies were darkly stained
and often contained less stained spots. Consistent with the HSinduced 46% increase in starch content determined by colorimetry,
an increase in quantity and size of starch granules was also evident
from the electron microscopy images (Figure 2F). Quantification of
starch granules (excluding pyrenoid starch sheaths) by planimetry
revealed an increase by ;18.5% (Supplemental Figure 1). No obvious changes in number, density, and stacking of thylakoid membranes were observed. However, after 12 h of HS, we observed
the formation of aberrant structures at nodal points of several
thylakoid membrane layers. These strongly resemble prolamellar
body (PLB)-like structures previously described in Chlamydomonas mutants with reduced levels of the vesicle inducing protein in
plastids (VIPP1), which was suggested to play a role in biogenesis/
maintenance of thylakoid membrane protein complexes (Nordhues
et al., 2012). PLB-like structures were not observed after 3 h HS
and occurred in only 3% of the cells after 12 h HS (Supplemental
Figure 2A). However, after 24 h HS, PLB-like structures were
observed in 45% of the cells. This percentage stayed constant
during the first 4 h of recovery and decreased to 27% after 8 h
recovery. At this time only residual PLB-like structures were left
(Figure 2F; Supplemental Figure 2B).
Photosynthesis and Respiration
To investigate effects of HS on the photosystem (PS) integrity and
on efficiency of energy coupling of light-harvesting complexes
(LHCs) to photosystem I (PSI) and PSII, we recorded 77K fluorescence spectra during the HS and recovery time course. As shown
in Figure 3A, the ratio of the emission maxima for PSII (;687 nm) to
PSI (;713 nm) increased strongly during the first 12 h of HS and
recovered thereafter, but the pre-HS ratio was not regained after 8
h recovery. A similar pattern was observed for a “shoulder” occurring in the spectra at ;681 nm and a blue shift of the PSI
emission peak from ;713.5 nm under nonstress conditions to
;710.5 nm after 24 h of HS (Figures 3B to 3E). These results are
indicative of detachment of LHCII and LHCI from PSII and PSI,
respectively (Armond et al., 1978; Naumann et al., 2005).
We further determined electron flow through PSII at different light
intensities in cells harvested during the HS and recovery time
course based on chlorophyll a fluorescence and oxygen evolution.
As shown in Figure 3F and Supplemental Table 1, the maximal
photosynthetic rate (Pmax) determined by chlorophyll fluorescence
decreased by about half after 3 h of HS and to ;20% of initial
values after 24 h of HS. After 8 h recovery, Pmax had recovered only
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Figure 2. Cytological and Ultrastructural Data.
(A) Growth curve. The cell count per milliliter was determined with a Coulter counter. Error bars (also in [B] to [E]) represent standard deviations from
three biological and two technical replicates. The gray area depicts the duration of HS.
(B) Relative average DNA content and ploidy. Relative cellular DNA content and ploidy, displayed as relative abundance of 1n, 2n, and 4n cells, were
determined by FACS analysis of PI-stained cells.
(C) Average cell size. Average cell sizes were determined using a Coulter counter.
(D) Protein and chlorophyll content. Total protein and chlorophyll contents were determined photospectrometrically according to Lowry et al. (1951) and
Porra et al. (1989), respectively.
(E) TAG and starch content. The TAG content was determined from summed normalized ion counts of all TAG species measured by mass spectrometry. The relative starch content per cell was determined by colorimetry.
(F) Selected electron microscopy images. Images in the upper row represent an overview of single cells at the indicated time points during HS and
recovery. Close-ups of details from the overview images are shown in the lower row. Bars in “overview” = 1 mm and bars in “details” = 0.2 mm. N,
nucleus; L, lipid body; P, pyrenoid; S, starch. For more images, see Supplemental Figure 2B.
to ;34% of pre-HS values. By contrast, the initial slope (a-slope) of
the photosynthesis-irradiation (P-I) curve, estimating PSII quantum
yield, did not change during the first 12 h of HS but dropped by
;25% after 24 h of HS and increased slowly during recovery
(Figure 3G). In line with the a-slope values, oxygen evolution did not
change in cells exposed to HS for 3 h, but decreased by about twothirds in cells exposed to HS for 24 h (Figure 3H; Supplemental
Table 1). Hence, chlorophyll fluorescence at higher irradiances (as
indicated by Pmax) appears to underestimate electron flow through
PSII in heat-stressed cells, whereas at limiting light (indicated by the
Responses to Heat Stress and Recovery
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a-slope) it appears to be a better measure. This is in line with earlier
reports, where rates of oxygen evolution correlated better with the
a-slope than with Pmax (Geel et al., 1997; Gilbert et al., 2000). Also,
respiration (oxygen consumption in the dark) did not change between unstressed cells and cells exposed to HS for 3 h, but declined by about half in cells exposed to HS for 24 h (Figure 3H;
Supplemental Table 1). Finally, the chlorophyll a/b ratio declined
from 2.5 to 2.15 during 24 h HS and increased again during recovery, albeit only to a value of 2.25 after 24 h recovery (Figure 3I;
Supplemental Table 1).
Analysis of the Dynamics of Polar Metabolites
For the profiling of polar metabolites, cells were harvested by
fast filtration and polar metabolites were extracted with cold
methanol/chloroform, derivatized by methoxamination and silylation, and analyzed by gas chromatography-mass spectrometry (GC-MS) (Lisec et al., 2006; Veyel et al., 2014a). Excluding
standards, 106 polar analytes were identified by matching reconstructed spectra against the Golm Metabolome Database
(Kopka et al., 2005; Schauer et al., 2005; Hummel et al., 2010)
using the TagFinder software (Luedemann et al., 2008). The 106
analytes represented 99 distinct metabolites mainly covering
Figure 3. Photosynthesis and Respiration.
(A) 77K fluorescence spectra. 77K fluorescence was recorded from
Chlamydomonas cells before HS, during HS, and during the recovery
phase (Re). The averages of three biological and two technical replicates
are shown. Spectra are normalized to the PSII emission peak at 687 nm
(dotted line). The color code used is applied throughout this figure.
(B) Close-up of PSII emission peak normalized 77K fluorescence spectra. The dotted line indicates an additional emission peak occurring in
heat-stressed cells at 681 nm.
(C) Relative changes in PSII emission peak normalized spectra at 681
nm. Shown is the relative change in fluorescence emission at 681 nm
during HS and recovery relative to that under pre-HS conditions (0 h HS).
Averages and standard deviations of three biological and two technical
replicates are shown.
(D) Close-up of PSI emission peak normalized 77K fluorescence spectra.
(E) Blue shift of PSI emission peaks. Shown is the blue shift of PSI
emission peaks during the HS and recovery time course based on
averages of three biological and two technical replicates. Error bars
represent standard deviations.
(F) Photosynthesis-irradiance (P-I) curve based on fluorescence. Electron flow through PSII was determined for cells sampled during the HS
and recovery time course by PAM measurements and is represented as
YII*PAR (photosynthetically active radiation) plotted against the PAR
applied. Error bars represent standard deviations of three biological
replicates. For numerical values, see also Supplemental Table 1.
(G) a-Slopes of P-I curves. The initial slopes (a-slopes) of P-I curves from
(F) were determined. Error bars represent standard deviations of
three biological replicates. For numerical values, see also Supplemental
Table 1.
(H) P-I curves based on oxygen evolution. Electron flow through PSII was
determined for cells sampled during the HS and recovery time course by
determining oxygen evolution with an optical oxygen measurement
system. Error bars represent standard deviations from three biological
and three technical replicates. For numerical values, see also Supplemental
Table 1.
(I) Chlorophyll a/b ratios. Chlorophyll a and b concentrations were determined spectrophotometrically from three biological and two technical
replicates. For numerical values, see also Supplemental Table 1. Error
bars represent standard deviations.
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organic acids, amino acids, polyols, amines, sugars, phosphorylated sugars, and several unknown metabolites. After
filtering, we tested 85 metabolites for significance in a one-wayANOVA (Benjamini-Hochberg corrected P value # 0.05) resulting in 43 polar metabolites that changed significantly during the
HS and recovery time course (Figure 4; Supplemental Figure 3
and Supplemental Data Set 1).
Using hierarchical clustering, polar metabolites were grouped
into four clusters according to their behavior during the HS and
recovery time course (Figure 4A). Five metabolites (light-blue
cluster) peaked early within the first 3 h of HS, their abundance
declined to levels still higher than under pre-HS conditions after
24 h HS, and in most cases decreased further during recovery.
Abundances of 11 metabolites (dark blue cluster) increased
steadily during HS and declined during recovery. Most of them
displayed a small peak during the first hour of HS followed by
a transient decline during the next 2 h before the overall increase
to maximum abundances reached after 24 h HS. Abundances of
12 metabolites grouped into the yellow cluster peaked transiently
during the first 3 h of HS, declined again within the next 21 h of
HS (in most cases below initial levels), and often increased mildly
during recovery. Finally, abundances of 15 metabolites (red
cluster) declined during the first 3 h of HS, remained low during
the entire duration of HS, and rapidly rose again during recovery,
often to levels much higher than present under pre-HS conditions.
Principal component analysis on polar metabolite data revealed
a functional and temporal separation of the metabolic state of the
cells (Figure 4B). The PCA projection space factoring in the HS
time points is populated almost exclusively by early-peaking
metabolites (light-blue and yellow clusters) that largely appear to
be generated by the degradation of larger molecules (e.g., glycerophosphoglycerol [GPG], ethanolamine phosphate, and inositol
from phospholipids; guanosine and b-alanine from nucleotides;
putrescine and ornithine from arginine; see Discussion). By contrast, the projection space factoring in the recovery time points is
populated by metabolites whose abundance decreased or increased only mildly during the first 3 h of HS (blue and red
clusters) and represent many metabolites required for anabolic
reactions (e.g., amino acids: glycine, asparagine, threonine, serine, isoleucine, and phenylalanine; central metabolites: glucose-6phosphate, fructose-6-phosphate, glycerol-3-phosphate, citrate,
malate, succinate, and 2-oxoglutarate; see Discussion). Hence,
PC2, accounting for 22.75% of the variance, largely appears to
separate “catabolism”-derived metabolites from those involved in
“anabolism.” While PC1 could not be interpreted biologically and
likely represents technical noise, PC3, accounting for 15.5% of
the variance, largely appears to separate metabolites required for
“stress acclimation” from those involved in “housekeeping.” Accordingly, the upper part of the projection space is populated by
metabolites accumulating during 24 h of HS (light-blue and blue
clusters) that are likely to play roles as compatible solutes (GPG,
trehalose, inositol, and several amino acids; see Discussion),
while the lower part is populated by metabolites exhibiting low
levels after 24 h of HS (red and yellow clusters), of which many are
involved in central metabolism (e.g., fumarate, succinate, malate,
and citrate).
Accordingly, the trajectory of the early time points after onset
of HS appears to be driven by “stress acclimation through
catabolism” (i.e., generation of compatible solutes by degradation of larger molecules), while at later time points during HS it is
driven by “stress acclimation through anabolism” (i.e., generation of compatible solutes by de novo synthesis; see Discussion). The trajectory during recovery appears to be driven by
“restoration of housekeeping metabolism” (i.e., removal of
compatible solutes and overshooting of metabolites from the
central metabolism). Notably, PCA also revealed that the cellular
metabolic state after 8 h recovery was still different from that
under pre-HS conditions.
Analysis of Proteome Dynamics
Proteome dynamics during heat stress and recovery was monitored
by applying a shotgun proteomics approach based on full 15N
metabolic labeling. For this, cells were 15N-labeled to >98% by
growth on 15NH4Cl. Unstressed and heat-stressed labeled cells
were pooled to generate a 15N standard for each protein expressed
during the HS and recovery time course, which was then mixed with
14N cells harvested at different time points during the experiment
(Figure 1). Membrane and soluble proteins were extracted, tryptically
digested, and subjected to nano-liquid chromatography-tandem
mass spectrometry. Proteins were identified and quantified with the
IOMIQS framework (integration of mass spectrometry identification
and quantification software) (Mühlhaus et al., 2011). With this approach, a total of 1985 proteins were quantified in samples from at
least five out of six time points taken during HS and recovery. As
for 181 of them, no unique peptides were identified, and they
were combined into 52 protein groups. Hence, we quantified 1856
proteins/protein groups, which for simplicity will be designated as
“proteins” in the following (Supplemental Data Set 2). Of these, 688
could be determined as significantly changing within the time course
using one-way ANOVA with a P value threshold of 0.05 after correction for multiple testing (Benjamini and Hochberg, 1995).
Using hierarchical clustering, the 688 changing proteins were
grouped into five clusters according to their behavior in the time
course (Figure 5A; Supplemental Figure 4). To elucidate whether
proteins sharing a similar behavior were enriched in certain
functional categories, a functional enrichment based on the
MapMan ontology (Thimm et al., 2004; Lohse et al., 2014) was
calculated (Supplemental Data Set 3). According to this analysis,
proteins whose abundance increased strongly during HS and
decreased during recovery (light blue) are enriched in functional
categories photosystems, exotic lipid metabolism, stress, redox,
protein degradation, protein folding, vesicle transport, and glycolysis. Proteins whose abundances increased slightly during HS
and strongly in the initial recovery phase (dark blue) are enriched
in categories N metabolism, amino acid and nucleotide synthesis,
translation initiation, and protein degradation. Proteins whose
abundance declined strongly during HS and increased slowly
during recovery (red) were enriched in categories mitochondrial
and chloroplast ATP synthases, Calvin cycle, N metabolism,
amino acid degradation, tetrapyrrole synthesis, DNA synthesis,
translation, and gluconeogenesis/glyoxylate cycle. Proteins
whose abundances decreased during HS and recovered afterwards (yellow) are enriched mainly in categories protein synthesis
and chloroplast protein targeting. Members of the gray cluster
hardly changed during HS, but decreased during recovery. This
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Figure 4. Polar Metabolite Data.
(A) Cluster behavior and cluster tree of polar metabolites. The behavior of the four clusters was determined by hierarchical clustering combined with gap
statistics on log2-transformed polar metabolite data. The shift from HS to recovery is hallmarked by a dotted line. The colors of branches in the cluster
tree indicate the affiliation of metabolites to one of the four clusters.
(B) PCA on polar metabolite data. Log2-transformed data from all 43 metabolites found to be changing during the HS and recovery time course
(P # 0.05) were included. Time points are represented by dotted circles and individual metabolites by rhombi. The color of a rhombus indicates its
affiliation to one of the four clusters displayed in (A).
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Figure 5. Proteome Data.
(A) PCA on protein data. Log2-transformed data from all 688 proteins found to
be changing during the HS and recovery time course (P # 0.05) were included. Time points are represented by dotted circles and individual proteins
by rhombi. The color of a rhombus indicates its affiliation to one of the five
clusters obtained by hierarchical clustering combined with gap statistics
(cluster tree; Supplemental Figure 4). Cluster behaviors are displayed in the
inset. The shift from HS to recovery is hallmarked by a dotted line.
(B) Comparison of cellular proteome states. Compared were proteome
states at 24 h HS versus 0 h HS, 8 h recovery (Re) versus 24 h HS, and 8
h recovery versus 0 h HS. MapMan functional categories significantly
enriched between the proteome states compared (P # 0.05) are shown
in different colors, with circles representing individual proteins in a category. Numbers in parentheses indicate the corresponding MapMan bins
(Supplemental Data Set 2, Note 1).
cluster is enriched in categories photosynthesis (specific functions
associated with light reactions), lipid metabolism, cell organization,
transport, and tricarboxylic acid (TCA) cycle.
PCA applied to the proteome data revealed that the main
variance (PC 1, 67.38%) clearly separates the cellular proteome
compositions (referred to as proteome states) before and after
HS treatment, especially time points 0 h HS from 24 h HS (Figure
5A). Separation along PC 1 mainly can be attributed to proteins
upregulated (light and dark blue) and downregulated (red and
yellow) during HS that respectively contribute to opposing
vectors. Specifically, functional categories like stress, protein
folding, photosystems, redox, glycolysis, or protein degradation
oppose categories like gluconeogenesis/glyoxylate cycle, Calvin
cycle, DNA synthesis, tetrapyrrole synthesis, N metabolism,
protein synthesis, and ATP synthases (Supplemental Data Set 3).
Hence, this separation appears to indicate a functional partitioning between “stress acclimation” and “housekeeping” proteome states. Although PC 3 accounts for only 8% of the total
variance, we observe a separation between proteins in the lightblue, gray, and red clusters on the left and dark-blue and yellow
clusters on the right side. As we find proteins enriched in categories N metabolism, amino acid biosynthesis, nucleotide synthesis, and protein synthesis in the dark-blue and yellow clusters,
observations on the protein level appear to correlate with the
separation between “catabolic” and “anabolic” processes, as also
apparent from the metabolite analysis.
PCA also revealed the relative cellular proteome compositions
prior to HS, 24 h after HS, and 8 h after recovery to be the most
uncorrelated time points in the data set (Figure 5A). Proteome
states in cells after 1, 2, and 4 h of recovery were found to be on the
trajectory between 24 h HS and 8 h recovery; therefore, the proteome state at 8 h recovery contains most information present in
the other recovery time points. Accordingly, we generated three
different contrasts (24 h versus 0 h HS, 8 h recovery versus 24 h
HS, and 8 h recovery versus 0 h HS) to compare proteome states
representative for nonstress, prolonged heat stress, and recovery in
more detail (Figure 5B). When comparing proteome states of 24 h
HS with unstressed cells, we found seven functional categories to
be significantly enriched with differentially expressed proteins. Four
of them contain proteins that were mainly upregulated (branched
chain amino acid metabolism with four proteins upregulated; abiotic
heat stress with five proteins upregulated; redox with 21 proteins
upregulated and two downregulated; and protein folding with 34
proteins upregulated and two downregulated) (Supplemental Data
Set 2, Note 1). Two functional categories contain proteins that were
almost entirely downregulated (histones with 15 proteins downregulated; ribosomal proteins with 51 proteins downregulated and
one upregulated). The functional category photosynthesis is a split
category containing 33 upregulated and 23 downregulated proteins
with no clear separation between functions.
A comparison of the proteome states between 8 h recovery and
24 h HS revealed six functional categories to be enriched with
differentially expressed proteins (Figure 5B; Supplemental Data Set
2, Note 1). The very large part of these in categories photosynthesis, redox, histones, ribosomal proteins, and protein folding
changed in the opposite direction of that with which they had
changed during 24 h of HS or underwent no significant change. By
Responses to Heat Stress and Recovery
contrast, all proteins involved in branched chain amino acid synthesis continued to increase or remained upregulated.
When comparing proteome states of cells that had recovered for
8 h from 24 h of HS with unstressed cells (0 h HS), eight functional
categories were still found to be enriched with differentially expressed proteins, although cells were again competent for dividing
in this state (Figure 5B; Supplemental Data Set 2, Note 1). A very
large portion of proteins in categories photosynthesis, redox, abiotic
stress/protein folding, histones, and branched chain amino acid/
aspartate synthesis remained altered in the direction into which they
had changed during the 24 h of HS and only few had again reached
pre-HS levels. While only very few proteins in these categories had
changed into a direction opposite to that into which they had
changed during HS, this was the case for most ribosomal proteins.
Hence, when compared with unstressed cells, almost all proteins in
bins redox, abiotic stress/protein folding, branched chain amino
acid/aspartate synthesis, and ribosomal proteins were upregulated
in cells that had recovered for 8 h from 24 h of HS. Histones were
downregulated and photosynthesis-related proteins showed mixed
behavior.
Analysis of Dynamics of Lipophilic Metabolites
Lipophilic metabolites were analyzed using ultraperformance
liquid chromatography and high-resolution mass spectrometry
(Hummel et al., 2011). We profiled 185 distinct lipophilic
compounds by extracting exact masses from the ultraperformance liquid chromatography and high-resolution mass
spectrometry chromatograms for respective metabolites. Of
the 185 compounds, 108 represented metabolites from six of
the seven major membrane lipid classes present in Chlamydomonas: diacylglycerol trimethyl homoserine (DGTS; a primary betaine membrane lipid in Chlamydomonas considered
to replace phosphatidyl cholin; (Giroud et al., 1988) and
phosphatidyl ethanolamine (PE) classified as cytoplasmic lipids; monogalactosyl diacylglycerol (MGDG), digalactosyl diacylglycerol (DGDG), and sulfoquinovosyl diacylglycerol
(SQDG) classified as plastidic lipids; and phosphatidyl glycerol
(PG) being present in both compartments (Janero and Barrnett,
1981). In addition, nine diacylglycerol (DAG) species and 68
TAG species were monitored. Neither phosphatidyl inositol nor
phosphatidyl cholin was detected with the applied method. Out of
the 185 lipophilic metabolites, abundances of 164 changed significantly during HS and recovery, among these, ;86.1% (93 of
108) of the detected membrane lipid species and ;98.5% (67 of
68) of the TAGs (P value # 0.05) (Supplemental Figure 5 and
Supplemental Data Set 4).
Based on their behavior in the HS and recovery time course,
TAGs were grouped into two clusters by hierarchical clustering
(Figure 6A; Supplemental Figure 6). Abundances of TAGs belonging to the red cluster increased strongly during the first 2 h of HS,
declined slightly during the next 22 h of HS, and declined strongly
during recovery. Fatty acids (FAs) of these TAGs contained a total
of 52 to 56 carbons and 4 to 10 double bonds (Figures 6B and 6C).
Abundances of TAGs belonging to the blue cluster increased
gradually during HS and recovery. They were enriched in species
containing 0 to 6 double bonds and 48 to 50/54 to 60 carbon
atoms. In PCA, these two clusters are separated along PC 1
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(49.57%) (Figure 6D), most likely reflecting a different source of FAs
incorporated into the TAGs of the two clusters (see Discussion).
The data set on membrane lipids is far less complete and
much more complex than that on TAGs. Based on the behavior
of lipids during the HS and recovery time course, hierarchical
clustering allowed their grouping into five clusters (Figures 7A
and 7B). Abundances of lipids in the red cluster increased rapidly after the onset of HS, remained high during HS, and decreased during recovery. Lipids in the light blue cluster behaved
opposite to lipids in the red cluster as their abundance decreased rapidly after onset of HS, remained low during HS, and
increased during recovery. Abundances of lipids in the yellow
cluster decreased during the first 2 h of HS, but increased during
the remaining 22 h HS and during recovery. The opposite behavior was observed for lipids in the dark-blue cluster, which
increased during the first 2 h of HS, but declined during the
remaining 22 h of HS and during recovery. Abundances of lipids
grouped into the cyan cluster continuously decreased during HS
and increased in the recovery phase.
FAs in lipids of the upregulated red and yellow clusters were
generally shorter and more saturated than FAs in lipids of the
downregulated (light blue, dark blue, and cyan) clusters (Figures 7C
and 7D). Regarding the distribution of individual lipid species
among the clusters, we observe a strong enrichment of cytosolic
membrane lipid DGTS in the red cluster (10 of 11 lipid species).
While only a single plastidic lipid species (DGDG_34_0) is present in
the red cluster, six other plastidic lipids (one MGDG, four DGDGs,
and one SQDG) with 0 to 2 double bonds are present in the yellow
cluster, suggesting that the increase in these plastidic lipids with
more saturated FAs occurs strongly delayed if at all when compared with cytosolic DGTS. Differences in cluster behavior and
cluster composition also are well separated by PCA, where upregulated (red and yellow) and downregulated (light blue, dark blue,
and cyan) lipids appear separated along PC 1 (61.2%), whereas
cytosolic (enriched in red and dark blue clusters) and plastidic lipids
(enriched in yellow and light blue clusters) appear separated along
PC 2 (22%) (Figure 7E).
Overall, membrane lipids show a trend opposite to that of
TAGs. In particular, abundances of cytosolic DGTS species with
shorter chain lengths and more saturated FAs increase during
the first 2 h of HS, while abundances of lipids of all classes with
longer and more unsaturated FAs decrease. By contrast, TAGs
with more unsaturated FAs drastically increase during the same
time frame and decrease during recovery. Strikingly, although
cells commenced dividing again after 8 h recovery, their lipid
composition was still much different from that present before HS
(Figure 7E; Supplemental Figure 5).
DISCUSSION
Using a top-down systems biology approach, we addressed the
question of how Chlamydomonas acclimates to long-term HS
and recovers from it. The HS and recovery time course shows
changes in molecular compositions over time allowing Chlamydomonas to acclimate to the elevated temperature and back
to ambient temperature. This acclimation process is characterized by specific response elements grouped by a common
functional and temporal behavior. In the following, we will
10 of 28
The Plant Cell
Figure 6. TAG Data.
(A) Behavior of TAG clusters. The behavior of the two clusters into which the 67 TAG species changing during the HS and recovery time course could be
grouped by hierarchical clustering and gap statistics is depicted. Shown are z-transformed data, which also were the basis for clustering. The shift from
HS to recovery is denoted by a dotted line.
(B) FA chain length distribution within TAGs changing during the HS and recovery time course. Gray indicates the distribution of numbers of carbon
atoms in FAs of all TAGs; blue and red that in FAs of TAGs belonging to the clusters defined in (A). The average number of FA carbons (Cs) per TAG is
given in the legend for each cluster.
(C) Distribution of the number of double bonds within TAGs changing during the HS and recovery time course. Gray indicates the distribution of
numbers of double bonds in FAs of all TAGs; blue and red that in FAs of TAGs belonging to the clusters defined in (A). The average number of double
bonds (DB) per TAG is given in the legend for each cluster.
(D) PCA on TAG data. Z-transformed data from all 67 TAG species found to be changing during the HS and recovery time course were included. Time
points are represented by dotted circles and individual TAG species by rhombi. Because levels of many TAG species under nonstress conditions were
low and thus below detection limit, time point “0 h HS” was omitted. Rhombi colors indicate the affiliation of a TAG to one of the two clusters defined in
(A). The cluster tree is shown in Supplemental Figure 6.
discuss these response elements and their dynamic succession
during the HS and recovery time course.
Cells Undergo an Immediate Cell Cycle Arrest upon
Onset of HS
A characteristic early response to HS is an immediate arrest of
cell division (Figures 2A and 8), which we have observed
previously in Chlamydomonas and which is conserved in eukaryotic cells (Richter et al., 2010; Mühlhaus et al., 2011). As the
distribution of 1n, 2n, and 4n cells remained almost constant
during HS (Figure 2B), cells apparently were cell cycle arrested
in both G1 (unreplicated DNA = 1n) and G2 phases (after DNA
replication $ 2n, 4n), perhaps as a consequence of reduced
activities of cyclin-dependent kinases (Kühl and Rensing, 2000).
As presumably in all eukaryotes, including Chlamydomonas, the
Responses to Heat Stress and Recovery
Figure 7. Membrane Lipid and DAG Data.
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12 of 28
The Plant Cell
biosynthesis of histones is restricted to the S phase (Walther
and Hall, 1995; Marzluff and Duronio, 2002), no de novo biosynthesis of histones is expected to occur in heat-stressed
Chlamydomonas cells. Accordingly, we find levels of histone
proteins to decline on average by ;30% after 24 h of HS, which
is close to the expected dilution by ;36% given the 57% increase in cellular protein content in this time period (Figure 2D).
Metabolic Remodeling Early after Onset of HS Is
Characterized by the Depletion of Central Metabolites
An early response to HS is the remodeling of the cell’s metabolic
state. Part of this is the rapid depletion of metabolites from primary
metabolism, including succinate, malate, 2-oxoglutarate, and citrate of the TCA and glyoxylate cycles, or fructose-6-phosphate,
glucose-6-phosphate, and glycerate-3-phosphate as metabolites
of glycolysis and Calvin cycle (Figures 4 and 8; Supplemental
Figure 3 and Supplemental Data Set 1). Although several TCA/
glyoxylate cycle and glycolysis enzymes were significantly downregulated during the first 3 h of HS [Mühlhaus et al., 2011; “short
term HS (3 h HS versus 0 h HS)” in Supplemental Data Set 2, Note
1], the degree of downregulation was so modest that depletion of
corresponding metabolites most likely is due to reduced enzyme
activity. In Escherichia coli, HS also led to a rapid depletion of
metabolites from glycolysis, the oxidative pentose phosphate
pathway, and the TCA cycle and was interpreted as part of an
energy conservation strategy (Jozefczuk et al., 2010). By contrast,
metabolites of glycolysis and Calvin cycle were found to increase in
heat-stressed Arabidopsis (Kaplan et al., 2004; Rizhsky et al., 2004).
Further work is required to elucidate whether this discrepancy is
related to trophic state (mixo/heterotrophy versus autotrophy), organization (single versus multicellularity), or generally different HS
acclimation strategies between these organisms.
Metabolic Remodeling Early after Onset of HS Is Also
Characterized by the Accumulation of Metabolites
Potentially Involved in Stress Protection
Although from their overall behavior during the HS and recovery
time course belonging to three different clusters (light blue, blue,
and yellow in Figure 4), 12 metabolites had in common to
strongly accumulate within the first 2 h of HS and to contribute
significantly to the factoring of the early time points in the projection space of the PCA (representing “catabolism”) (Figure 4B;
Supplemental Figure 3 and Supplemental Data Set 1). These are
GPG, guanosine, ethanolamine phosphate, b-alanine, myoinositol, glutamate, pyroglutamate, ornithine, a hexo-aldose,
putrescine, fumarate, and trehalose.
The rapid increase of b-alanine appears to be a conserved
metabolic signature for HS in a wide range of organisms, as it
has been observed also in E. coli (Jozefczuk et al., 2010), Arabidopsis (Kaplan et al., 2004; Rizhsky et al., 2004), and Drosophila
melanogaster (Malmendal et al., 2006). b-Alanine may be
synthesized from polyamines (Terano and Suzuki, 1978) or
propionate (Hatch and Stumpf, 1962) or may derive from the
degradation of uracil (Duhaze et al., 2003). The notion that uracil
from degraded RNA is the source of b-alanine appears supported by the observation that guanosine, a breakdown product
of guanine nucleotides, accumulated with similar kinetics as
b-alanine. Accumulating b-alanine was suggested to serve as
compatible solute (Kaplan et al., 2004). Compatible solutes
stabilize membranes and prevent protein unfolding and aggregation (Singer and Lindquist, 1998b; Martin et al., 1999; Yancey,
2005). However, b-alanine also is a precursor for the biosynthesis of pantothenate (vitamin B5), which is required for the
biosynthesis of the 4’-phosphopantetheine moiety of CoA and
acyl carrier protein (Ottenhof et al., 2004). An increased requirement of the latter for FA metabolism immediately after
onset of HS is an attractive idea given the dramatic lipid
rearrangements taking place early after onset of HS (see below).
The metabolite with the most striking increase within the first 2
h of HS was GPG (Supplemental Figure 3A and Supplemental
Data Set 1). In fact, GPG was found to accumulate in response
to salt stress and elevated temperatures in the thermophilic archaebacterium Archaeoglobus fulgidus (Martins et al., 1997) and
to exhibit a thermostabilizing effect on several proteins in vitro
(Lamosa et al., 2000, 2003; Santos and da Costa, 2002). As our
GC-MS measurements provide only relative data on GPG levels,
we cannot assess whether the absolute concentrations of accumulating GPG are sufficiently high for a compatible solute
Figure 7. (continued).
(A) Behavior of membrane lipid and DAG clusters. Depicted is the behavior of the five clusters into which the 93 membrane lipid and 4 DAG species
changing during the HS and recovery time course were grouped. Clusters were defined by hierarchical clustering combined with gap statistics. Shown
are z-transformed data, which also were the basis for the clustering. The shift from HS to recovery is indicated by a dotted line.
(B) Membrane lipid and DAG cluster tree. Colors of tree branches indicate the affiliation of membrane lipid and DAG species to one of the five clusters
defined in (A). Cluster trends and average numbers of carbon atoms (Cs) and double bonds (DBs) in FAs of lipid/DAG species are given in the insets
next to each tree branch. Membrane lipid and DAG species are displayed as follows: lipid species name_No. of C-atoms_No. of double bonds.
(C) FA chain length distribution within lipid species changing during the HS and recovery time course. Gray indicates the distribution of numbers of
carbon atoms in FAs of all lipids and DAGs, blue, red, cyan, and yellow that in FAs of lipids/DAGs belonging to the clusters defined in (A). The average
number of FA carbons (Cs) per lipid/DAG is given in the legend for each cluster.
(D) Distribution of double bonds within lipid species changing during the HS and recovery time course. Gray indicates the number of double bonds in
FAs of all lipids/DAGs, and blue, red, cyan and yellow that of FAs in lipids/DAGs belonging to the clusters defined in (A). The average number of double
bonds per lipid/DAG is given in the legend for each cluster.
(E) PCA on membrane lipid and DAG data. Z-transformed data from all 93 lipid and DAG species found to change during the HS and recovery time
course were included. Time points are represented by dotted circles and individual membrane lipid species by symbols: crosses, plastidic lipids;
rhombi, DGTS; triangle, PG; squares, diacylglycerols. Symbol colors indicate the affiliation of a lipid to one of the five clusters defined in (A).
Responses to Heat Stress and Recovery
function in Chlamydomonas. GPG may be produced by the
actions of phospholipase A or lipid acyl hydrolase, which degrade PG into GPG and free FAs (Matos and Pham-Thi, 2009;
Cheng et al., 2011). Accordingly, we found levels of all PG
species to decrease already after the first hour of HS (Figure 7B;
Supplemental Figure 5).
With ethanolamine phosphate and myo-inositol, two additional possible degradation products from phosphorous glycerolipids increased early after onset of HS (Supplemental Figure
3B and Supplemental Data Set 1). Ethanolamine phosphate is
the head group of PE and myo-inositol that of phosphatidyl
inositol (deprived of the phosphate moiety). These metabolites
might be produced by phospholipases C and D, respectively
(Matos and Pham-Thi, 2009), which is in agreement with a decrease already after the first hour of HS in PE species whose FAs
contain three or more double bonds (Figure 7B; Supplemental
Figure 5). In contrast to GPG, but similar to b-alanine, levels of
ethanolamine phosphate and myo-inositol reached their maximum already within the first 15 min of HS and declined afterwards, but remained elevated during HS. Whether they serve in
signaling (Mishkind et al., 2009), as compatible solutes (Yancey,
2005), or are an inert by-product of lipid remodeling remains to
be elucidated.
Sugars like sucrose, raffinose, and maltose increased early after
onset of HS in Arabidopsis and were suggested to serve as
compatible solutes (Kaplan et al., 2004; Guy et al., 2008). Likewise,
early HS-induced hexo-aldose and trehalose might act as compatible solute in Chlamydomonas. Possibly, a similar role can be
attributed to pyroglutamate, as it has been shown to serve as
osmoprotectant in haloalkaliphilic methanotrophs (Trotsenko and
Khelenina, 2002). However, as pyroglutamate may be generated
by glutamate cyclization and shares a very similar kinetic during
HS and recovery with glutamate (Supplemental Figure 3B), it might
also be an experimental artifact (Steinhauser and Kopka, 2007).
Ornithine and putrescine are likely generated from the degradation of arginine in a pathway leading to the biosynthesis of
spermidine from putrescine (Kusano et al., 2008). This assumption is supported by the 2.1-fold increase in abundance of
spermidine synthase SPD1 after 24 h HS (Supplemental Data
Set 2). When exogenously applied, spermidine was shown to
protect Arabidopsis seedlings from HS (Sagor et al., 2013).
Over all, the early response to HS at the metabolite level is
characterized by the rapid accumulation of compounds that
most likely are generated rapidly because they are derived from
the catabolism of larger molecules (Figure 8). These products
from catabolism may represent precursors for the biosynthesis
of compounds playing roles in stress protection (b-alanine for
pantothenate biosynthesis; ornithine and putrescine for
spermidine biosynthesis) or function directly in stress protection as compatible solutes [GPG, inositol, trehalose, and
(pyro)glutamate].
Membrane Lipid Remodeling Is an Early Response to
HS and Appears to Occur with Lipid Bodies as Buffer
for Unsaturated Fatty Acids
Within the first 2 h of HS, strong rearrangements in the composition
of lipophilic metabolites took place. These rearrangements were
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dominated by the accumulation of DAGs and TAGs with polyunsaturated FAs, the accumulation in particular of cytosolic
DGTS with saturated FAs, and the decrease in abundance of
lipids with polyunsaturated FAs (Figures 6 to 8; Supplemental
Figure 5). While abundances of cytoplasmic and plastidic lipids
with polyunsaturated fatty acids decreased to a similar extent,
we observed a much slower increase of more saturated plastidic
lipids as compared with cytosolic DGTS (compare red with
yellow cluster in Figures 7A, 7B, and 7E). A possible explanation
for this discrepancy might be that plastidic membrane lipids with
polyunsaturated FAs are not replaced by saturated species of
the same lipid class, but by other molecules not captured in our
analysis. These may comprise specific saturated stress lipids
like MGlcDG found in cyanobacteria to accumulate under heat
stress conditions and, together with small heat shock proteins,
to preserve the functional integrity of thylakoid membranes
(Tsvetkova et al., 2002; Balogi et al., 2005). To accomplish lipid
remodeling, lipid acyl hydrolases and phospholipases apparently target mainly membrane lipids containing polyunsaturated
FAs, leading to the generation of DAGs and free FAs, which are
converted into TAGs. The dark staining of lipid bodies in electron
micrographs taken 3 h after onset of HS, apparently caused by
the strong interaction of osmium tetroxide with double bonds
generating higher electron density (Hayat, 1970), corroborates
that TAGs in these lipid bodies contain polyunsaturated FAs
(Figure 2F).
Lipids with saturated FAs are generated by the de novo
biosynthesis of saturated palmitic (16:0), stearic (18:0), and
monounsaturated oleic (18:1) FAs in the chloroplast (Ohlrogge
and Browse, 1995). Additional double bonds are introduced later
via different desaturases (Riekhof et al., 2005; Hu et al., 2008).
The introduction of further carbon atoms (two per cycle) to
elongate FAs is achieved even later by membrane-bound elongation enzymes situated in the ER membrane (Nugteren, 1965;
Ghanevati and Jaworski, 2001). Accordingly, we found the saturated FAs in lipids increasing during the first 2 h of HS on average to be shorter than those of the declining lipids with
polyunsaturated FAs (Figures 7A to 7C; Supplemental Figure 5),
in line with the proposed de novo biosynthesis of lipids with
saturated FAs. De novo biosynthesis of lipids with saturated FAs
requires glycerol, acetyl-CoA, NADPH, and ATP (Ohlrogge and
Browse, 1995). As during the first 2 h of HS the increase in TAGs
and lipids with saturated FAs correlates with a decrease in
starch (Figure 2E), glycerol and acetyl-CoA might to some part
derive from starch breakdown, but acetate from the medium is
also a likely source. As discussed below, the main portion of
ATP and NADPH might be contributed by the light reactions of
photosynthesis, whose capacity was found not to be impaired
3 h after onset of HS (Figure 3H).
What may be the reason for the HS-induced rapid increase in
the fraction of membrane lipids with saturated FAs? The ratio of
saturated to unsaturated FAs in membrane lipids was shown to
correlate with growth temperature in poikilothermic organisms
(Marr and Ingraham, 1962, and references therein). This ensures
a constant membrane viscosity to ensure functionality of
membrane proteins and was termed “homeoviscous adaptation” (Sinensky, 1974). Homeoviscous adaptation to HS is
fast and in bacteria can be accomplished within 30 min after
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The Plant Cell
Figure 8. Cellular Responses during the HS and Recovery Time Course.
Relative activities of cellular processes or abundances of cellular compounds are given as bars, with bar thickness being roughly proportional to
activity/abundance. Dotted lines indicate the absence of an activity/compound.
onset of HS (Mejía et al., 1995; Shigapova et al., 2005; Li et al.,
2013). Our results suggest that homeoviscous adaptation also
occurs within a few hours in heat-stressed Chlamydomonas
cells.
While the fraction of lipids with unsaturated FAs may increase by
de novo biosynthesis and the activity of desaturases (Russell,
1984), de novo biosynthesis is the only option for increasing the
fraction of lipids with saturated FAs. This implies the exchange of
unsaturated FAs by de novo produced saturated FAs and, given
the cell cycle arrest of heat-stressed Chlamydomonas cells, the
necessity to dispose of unsaturated FAs by means other than
dilution by growth. Apparently, by employing lipid bodies as
provisional sinks for unsaturated FAs in form of TAGs,
Chlamydomonas has found an elegant solution to this problem and
it is likely that this applies also to other stresses like N or S deprivation (Sheehan, 1998; Miller et al., 2010; Cakmak et al., 2012).
However, as discussed below, TAGs in light of the cell cyclearrested cells should also be considered as sinks for NADPH, ATP,
and fixed carbon from photosynthesis; in cases of nutrient stress,
this may be their dominating role. Interestingly, exposure of Arabidopsis to a 30-min HS at 40°C led to a significant increase in the
number of plastoglobules (Zhang et al., 2010). Plastoglobules are
attached to thylakoids through a half-lipid bilayer that surrounds the
globule and is continuous with the thylakoid membrane (Austin
et al., 2006). They contain lipids, FAs, TAGs, carotenoids, tocopherols, quinones, and chlorophylls and have been shown to
accumulate also under other conditions likely to require lipid
remodeling, like deetiolation, senescence, or salt stress (Sprey
and Lichtenthaler, 1966; Kaup et al., 2002; Sam et al., 2003).
Hence, it is tempting to speculate that analogous to lipid bodies
in Chlamydomonas, plastoglobules may serve as provisional
deposits for FAs from membrane lipid remodeling and as sinks
for NADPH, ATP, and carbon in stressed land plant chloroplasts.
Levels of Molecular Chaperones Are Modulated between
3 and 24 h HS Depending on Protein Class and
Subcellular Localization
Prolonged HS faces cells with the problem of enhanced rates of
protein unfolding for which they have to compensate to reestablish protein homeostasis at the elevated temperature (Sharma
et al., 2011). This may be achieved by a combination of elevated
Responses to Heat Stress and Recovery
levels of molecular chaperones, reduced expression of (thermolabile) proteins, and the accumulation of compatible solutes
(Singer and Lindquist, 1998a, 1998b). Accordingly, except for the
Hsp110 family member HSP70G, all proteins in bins protein
folding (29.6) and abiotic stress (bin 20.2.1), previously found to
increase early after onset of HS, were still upregulated after 24 h
of HS (Figures 5B and 8; Supplemental Data Set 2, Note 1;
Mühlhaus et al., 2011). Except for two universal stress proteins, all
proteins in these bins are (co)chaperones and immunophilins.
However, there was variation between levels reached after 3 and
24 h of HS that depended on the (co)chaperone class or subcellular location. For example, levels of small HSPs and ClpB-type
HSP100s declined between 2.3- and 7.1-fold after 24 h HS
compared with 3 h HS (Supplemental Data Set 2, Note 1, and
Supplemental Figure 7). sHSPs intercalate into forming protein
aggregates to improve their accessibility for ClpB/HSP70 and
ClpBs mediate the disassembly of protein aggregates (Goloubinoff
et al., 1999; Mogk et al., 2003; Weibezahn et al., 2004). Hence, the
formation of aggregates appears to be reduced in HS-acclimated
cells, presumably mediated by the accumulation of compatible
solutes, potentially including GPG, trehalose, a hexo-aldose,
inositol, and several amino acids. Moreover, as levels of ClpB/
sHSPs declined by significantly more than 29% (expected from
dilution resulting from the 40% increase in total cellular protein
content between 3 and 24 h HS), they appeared to be actively
degraded, thus indicating a tight control of their levels.
Examples for compartment-specific long-term regulation of
chaperone levels are the ER-resident BIP1 and HSP90B, which
increased only after 24 h HS or continued to increase, respectively,
and mitochondrial HSP70C and CPN60C/CPN10 that continued to
increase between 3 and 24 h HS (Supplemental Data Set 2, Note 1,
and Supplemental Figure 7). Apparently, processes in ER and mitochondria taking place late after onset of HS require more
chaperoning power (increased reactive oxygen species [ROS]
production?). Interesting also is an adjustment of chaperone to
cochaperone ratios after long-term HS. Examples are: (1) cytosolic
HSP90A and HSP70A, whose levels dropped or remained constant, while levels of their cochaperone HOP1 increased; (2) chloroplast CPN60A, B1, and B2, whose levels tended to decline
between 3 and 24 h HS, while those of their cochaperones CPN23,
CPN20, and CPN11 tended to increase. This might allow for a finetuning of chaperoning power to meet the chaperoning requirements of specific substrates in the new steady state.
Reduced Bulk Protein Biosynthesis during HS Comes
Along with Reduced Abundances of Cytosolic, but
Not Plastidic, Ribosomes
The generation time of exponentially growing Chlamydomonas cells
of 5 to 8 h implies that bulk protein per culture volume under optimal
conditions increases by at least 8-fold in 24 h (Harris, 2008).
However, over the 24-h HS period, we observed only a 57% increase in bulk protein per cell (Figure 2D); as cell numbers/mL remain constant during HS (Figure 2A), the 57% increase in bulk
protein is valid also based on culture volume. Hence, HS leads to
a reduction of general protein synthesis in Chlamydomonas that
persists during the entire HS period, although after onset of HS
HSPs are biosynthesized at very high rates (Schulz-Raffelt et al.,
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2007; Schmollinger et al., 2013). This reduced need for translation
capacity manifests itself also by a ;17% decrease in cytosolic ribosome content, which was already observed after 3 h HS and
persisted during the 24-h HS period (Figure 5B; Supplemental Data
Set 2, Note 1; Mühlhaus et al., 2011). A decrease in ribosome
content under HS was also observed in yeast, where it is realized by
the heat shock factor-dependent downregulation of genes encoding ribosomal proteins (Lopez et al., 1999). Interestingly, in contrast
to the 45 significantly declining cytosolic ribosomal proteins, levels
of only two organellar ones declined mildly (by 8 and 15%) after 24
h of HS. Moreover, a single ribosomal protein, termed plastidspecific protein 1 (PSRP-1), increased 1.3-fold during the 24-h HS
period (Figure 5B; Supplemental Data Set 2, Note 1). PSRP-1
contains a ribosome-associated inhibitor A (RaiA) domain. Bacterial
RaiA (or pY) inhibits translation initiation upon cold stress by stabilizing 70S ribosomes and preventing aminoacyl-tRNA and mRNA
binding (Vila-Sanjurjo et al., 2004). This indicates the possibility that
plastidic translation during HS might be throttled by PSRP-1.
Only 29 Proteins Are Found to Be Actively Degraded during
Long-Term HS
Proteins that cannot be folded and associate with chaperones
for too long are handed over to proteases for degradation (Arndt
et al., 2007). The 57% increase in total cellular protein during the
24-h HS period corresponds to a dilution by ;36%. Hence,
degradation rates of proteins declining significantly more than
36% must exceed their biosynthesis rates. Surprisingly, this is
the case only for 29 of the 688 proteins changing significantly
during the HS and recovery time course (Supplemental Data Set
2, Note 2). These are likely to include (1) severely heat labile
proteins, (2) proteins exerting functions that jeopardize survival
during long-term HS, and (3) proteins only transiently required
during HS. A potential example for heat labile proteins is METE,
which was downregulated by 59% after 3 h and by 71% after 24
h of HS (Supplemental Data Set 2, Note 2; Mühlhaus et al.,
2011). Accordingly, E. coli MetE was found to denature and
aggregate upon HS, to be solubilized by the ClpB/DnaK chaperones, and, upon lasting HS, to be degraded by the Lon protease (Mogk et al., 1999). An example for undesired proteins
might be CTH1B and chlB, enzymes involved in chlorophyll
biogenesis, which decreased by 49 and 81%, respectively, after
24 h HS (Supplemental Data Set 2, Note 2). Chlorophyll biosynthesis might be throttled to avoid potentially improperly assembled chlorophyll that would produce reactive oxygen
species (ROS) (Mittler, 2002; Stenbaek and Jensen, 2010). Examples for transiently required proteins are THI4 and THI8,
which are involved in thiamine biosynthesis and decrease by 62
and 85% respectively. As THI4 was found to increase 1.5-fold
after 3 h HS (Mühlhaus et al., 2011), a demand for thiamine
appears to prevail only early after the onset of HS. Similarly, the
homolog of THI4 was upregulated after 6 h HS and downregulated after 54 h HS in poplar (Ferreira et al., 2006).
Proteins Upregulated during HS Are Enriched in Branched
Chain Amino Acids
Four proteins in bin branched-chain amino acid (BCAA) synthesis were significantly upregulated after 24 h HS, although
16 of 28
The Plant Cell
members of this functional category were downregulated or did not
change significantly after 3 h of HS (Figure 5B; Supplemental Data
Set 2, Note 1; Mühlhaus et al., 2011). Hence, upregulation of enzymes involved in BCAA biosynthesis qualifies as a late response to
HS and is consistent with the increase in levels of leucine and isoleucine during HS (Figure 4; Supplemental Figures 3A and 3B and
Supplemental Data Set 1). This is in line with a previous study on
heat-stressed poplar, where a ketol-acid reductoisomerase (AAI1)
involved in BCAA biosynthesis was upregulated after 6 h HS (Ferreira
et al., 2006). As this enzyme was also upregulated in heat-stressed
Agrobacterium tumefaciens, it appears to be a conserved response
(Rosen et al., 2002). Ferreira et al. suggested that proteins biosynthesized de novo during HS might contain more BCAA to better
protect them from ROS attack, as BCAA are less prone to oxidation.
Alternatively, proteins newly biosynthesized during HS may be
equipped with a more elaborate hydrophobic core, rendering them
more thermostable and their folding less chaperone dependent
(Pace et al., 1996). Both ideas are supported by the finding that
BCAAs are significantly enriched in proteins upregulated during HS
(Supplemental Figure 8) and that these comprise upregulated ROS
scavengers, as well as all HSP100s, HSP90s, HSP70s, HSP60s, and
HSP10s (but not the sHSPs) (Supplemental Data Set 2, Note 3).
Antenna Uncoupling Reduces Photosynthetic Electron Flow
Only under Long-Term HS
As judged from unchanged oxygen evolution rates and a-slopes of
P-I curves, the capacity of the photosynthetic light reactions was
not impaired in cells exposed to HS for 3 h (Figures 3G and 3H;
Supplemental Table 1). Accordingly, 3 h of HS also had no effect on
levels of selected subunits of PSI, PSII, LHCII, cytochrome b6/f
complex, or ATP synthase, and PSII maximum quantum efficiency
was only slightly reduced (Nordhues et al., 2012). Also, on the basis
of earlier work, no effect would be predicted on oxygen evolution in
mixotrophically grown Chlamydomonas cells exposed to 42°C for 3
h (Schuster et al., 1988; Tanaka et al., 2000). PSII activity was also
not affected when Arabidopsis plants were exposed for 30 min to
moderate HS of 40°C (Zhang and Sharkey, 2009; Sharkey and
Zhang, 2010).
By contrast, long-term HS had substantial functional and structural effects on the light reactions. These may be interpreted as
a consequence of the functional uncoupling of LHCI and LHCII
from the photosystems, as evidenced by the blue shifts of shortand long-wavelength 77K fluorescence signals and presumably
also by their increased ratio (Figures 3A to 3E) (Naumann et al.,
2005). Reduced excitation energy transfer from antenna to PS
cores is likely to result in reduced linear electron flow (LEF), thus
accounting for the two-thirds reduced oxygen evolution capacity
and the smaller a-slope of P-I curves (Figures 3F to 3H;
Supplemental Table 1). Already in an early study on the effect of
increased growth temperatures on photosynthetic light reactions,
the desert shrub Larrea divaricata grown at 45°C showed functional
uncoupling of LHCII, an increase in the ratio of short to long
wavelength 77K fluorescence signals, and a lower chlorophyll a/
b ratio than 20°C-grown plants (Armond et al., 1978). As revealed
by freeze-fracture of heated oleander chloroplasts, functional uncoupling of LHCII was even followed by physical uncoupling,
leading to grana unstacking (Armond et al., 1980). The latter and the
formation of modified thylakoid attachment sites were suggested to
be caused by the migration of PSII, physically detached from LHCII,
to stroma lamellae (Gounaris et al., 1984). In addition to thylakoid
membrane destacking, moderate HS in Arabidopsis and turfgrass
was also shown to lead to chloroplast swelling (Xu et al., 2006;
Zhang et al., 2010). In transmission electron microscopy images,
we observed neither after long-term HS in Chlamydomonas (Figure
2F; Supplemental Figure 2). In this respect, it is interesting to note
that we found the two CURT-like proteins Cre27.g775100 and
Cre10.g433950 to be upregulated 1.3- and 2.4-fold, respectively,
by long-term HS (Supplemental Data Set 2). In Arabidopsis, CURT
proteins induce thylakoid membrane curvature and are important
for thylakoid architecture (Armbruster et al., 2013); therefore, their
homologs might help preserve the latter in heat-stressed Chlamydomonas chloroplasts.
Prolamellar Body-Like Structures Appear Only under Long-Term
HS and Might Consist of Unassembled Photosystems
Strikingly, between 12 and 24 h of HS, aberrant PLB-like
structures occurred at nodal points between multiple thylakoid
membrane layers (Figures 2F and 8; Supplemental Figure 2).
PLB-like structures were identified in Chlamydomonas cells
depleted of VIPP1 and suggested to contain both aggregated
PS subunits and assembly factors that appear at putative
chloroplast thylakoid centers when PS biogenesis is disturbed
(Nordhues et al., 2012; Rütgers and Schroda, 2013). Hence, we
speculate that most PSI and PSII accumulating during long-term
HS is nonfunctional and buried in the PLB-like structures, as
also indicated by the reduced oxygen evolution rates and the
concomitant ;15% decrease in abundance of ATP synthase
subunits (Supplemental Data Set 2, Note 1). PS biogenesis at
thylakoid centers may be disturbed by the strong depletion after
24 h HS of many PG and SQDG species, which are structural
lipids in PSI and PSII (Figure 7; Supplemental Figure 5) (Jordan
et al., 2001; Sakurai et al., 2006; Guskov et al., 2009). Also
a reduced efficiency of incorporation of chlorophyll into the PSs
may disturb their proper assembly and might explain the decline
in chlorophyll a/b ratios during HS (Figure 3I). Alternatively, as
the VIPP1 oligomers are dynamically assembled and disassembled by HSP70B and HSP90C, a sequestration of both
chaperones to proteins constantly unfolding during long-term
HS might impair VIPP1 function and impair PS biogenesis (Liu
et al., 2007; Heide et al., 2009; Feng et al., 2014). The strong 2.3fold upregulation of VIPP1 by long-term HS may indicate an
attempt of cells to compensate for impaired VIPP1 function
(Supplemental Data Set 2). Whether the accumulation of PS
subunits during long-term HS is caused by an enhanced synthesis or rather by the inability of clearance proteases to remove
inactive/improperly assembled PSs is an open question.
Calvin Cycle Activity Might Be Reduced Early after Onset of
HS to Provide Reducing Power and ATP for the Biosynthesis
of Saturated FAs, but Appears to Recover under Long-Term HS
After 3 h of HS, we previously observed a 6 to 15% decrease in
the abundance of Calvin cycle enzymes and of several proteins
involved in carbon concentration. A remarkably stronger decrease
Responses to Heat Stress and Recovery
by 30% was found for Rubisco activase (RCA1), albeit RCA1
transcripts were increasing, indicating that, similar to its ortholog in land plants, also Chlamydomonas RCA1 is heat labile
(Feller et al., 1998; Mühlhaus et al., 2011). These data, combined with the depletion of intermediates or products of the
Calvin Cycle (glycerate-3-phosphate, glucose-6-phosphate,
fructose-6-phosphate, and starch), suggest that like in land plants
carbon fixation by the Calvin cycle in Chlamydomonas is reduced
during the first 3 h of HS (Weis, 1981; Kobza and Edwards, 1987).
It was suggested previously that the deactivation of Rubisco
under HS might be a regulated response to a limitation elsewhere in
the photosynthetic apparatus (Sharkey, 2005; Sage et al., 2008). As
discussed above, the demand for de novo biosynthesized saturated FAs for the remodeling of membrane lipids during the first
hours of HS potentially represents a significant sink for ATP and
NADPH. Perhaps it is for coping with this new sink arising early
during HS that cells suppress carbon fixation? A fast remodeling of
membrane lipids after the onset of HS appears essential for survival, as plants with more saturated FAs in thylakoid membrane
lipids were much more heat tolerant (Murakami et al., 2000). Also,
the observation that the oxygen evolution capacity was reduced by
two-thirds after 24 h HS while it remained unchanged during the
first 3 h of HS might indicate that strong sinks for NADPH and ATP
prevailed during the first 3 h but became much smaller between 3
and 24 h of HS (Figure 3H; Supplemental Table 1). While this would
indicate long-term acclimation of LEF capacity according to reduced demands for NADPH and ATP, we cannot rule out damage
in thylakoid membrane complexes as cause, potentially indicated
by the accumulation of PLB-like structures.
The following observations suggest that carbon fixation by the
Calvin cycle has to some extent resumed after 24 h of HS: (1) Although levels of Rubisco small and large subunits have decreased
by 30 to 40%, RCA1 levels have increased 1.6-fold (Supplemental
Figure 3C). That some RCA1 protein might be active is suggested
by the finding that Arabidopsis Cpn60b appears to protect Rubisco
activase from thermal denaturation (Salvucci, 2008) and that in
Chlamydomonas levels of CPN60B1/2 were 1.8- to 3.9-fold upregulated after 24 h of HS (Supplemental Data Set 2). (2) A putative
Rubisco methyltransferase (RMT1) was upregulated 2.2-fold after
24 h HS that might mediate lysine trimethylation of the large SU.
Although the role of this modification in Rubisco is not yet clear, the
absence of methylation seems to reduce activities of other protein
substrates of the land plant Rubisco methyltransferase (Houtz et al.,
2008). (3) Photorespiration appears to increase under long-term HS.
This is indicated first by the accumulation of photorespiration intermediates serine, glycine, and glutamine between 3 and 24 h of
HS (Supplemental Figure 3 and Supplemental Data Set 1). It is indicated second by the 2.4-fold increase in abundance of phosphoglycolate phosphatase (PGP1; Supplemental Data Set 2), which
is essential to remove 2-phosphoglycolate, the toxic product of O2
incorporation into ribulose-1,5-bisphosphate by Rubisco (Anderson,
1971). Note that the specificity of Rubisco for CO2 over O2 decreases with increasing temperature, while the ratio of dissolved O2
versus CO2 increases, thus accounting for high rates of photorespiration when Rubisco is active under HS conditions (Jordan and
Ogren, 1984; Sharkey, 2005). (4) Abundances of glycerate-3phosphate, glucose-6-phosphate, starch, and four amino acids
(isoleucine, threonine, alanine, and asparagine) increased again
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between 3 and 24 h of HS. The increase in levels of these compounds may to some part also stem from acetate taken up from the
medium and fed into the glyoxylate and gluconeogenesis pathways.
However, while levels of AGP1, considered as key enzyme in starch
biosynthesis (Neuhaus and Stitt, 1990), increased 1.2-fold after 24 h
of HS, several enzymes required for the conversion of acetate to
acetyl-CoA (ACS2 and PAT1), of the glyoxylate cycle (CIS2, ACH1,
and MAS1), and gluconeogenesis (PCK1) were downregulated by
17 to 60% after 24 h of HS (Supplemental Data Set 2, Note 1), thus
indicating a reduced activity of the latter pathways.
Decreased Electron Sinks under Long-Term HS Appears to
Increase Mehler Reactions Leading to the Upregulation of
ROS Scavengers to Detoxify Superoxide and H2O2
Notably, elevated temperatures lead to not only reduced specificity
of Rubisco for CO2 over O2 but also increased production of H2O2
as side product of the oxygenation reaction (Kim and Portis, 2004).
If indeed activities of Calvin cycle and photorespiration in Chlamydomonas partly resume between 3 and 24 h of HS, cells in this
period would face an increased accumulation of H2O2. This idea
appears supported by the upregulation after 24 h HS of several
enzymes involved in the detoxification of H2O2 (Figures 5B and 8;
Supplemental Data Set 2, Note 1). Among these are four hybrid
cluster proteins (HCPs) that all contain putative organellar targeting
signals and are upregulated 1.8- to 8.6-fold. An HCP ortholog in E.
coli was shown to be specifically induced by H2O2 and to confer
protection toward hydrogen peroxide (Almeida et al., 2006).
Moreover, three peroxiredoxins, PRX1, PRX2, and PRX4, targeted
to chloroplast, cytosol, and mitochondria, respectively (Dayer et al.,
2008), were upregulated 1.2-fold. Also, thioredoxins TRXm and
TRXh1, and ferredoxin-thioredoxin reductase (FRR2), most likely
involved in the regeneration of oxidized peroxiredoxins in the respective compartments (Dietz et al., 2006), were upregulated 1.2to 1.9-fold after 24 h of HS. An increased demand under long-term
HS for detoxifying H2O2 is also indicated by the 1.2- to 4.4-fold
upregulation of proteins involved in the biosynthesis of glutathione
(GSH1, a putative glutamyl-cysteine ligase), ascorbate (NED4,
a putative sugar nucleotide epimerase), and the reduction of dehydroascorbate by glutathione (DAR1, a putative glutathionedependent dehydroascorbate reductase; PDI2, PDI4, and RB60,
protein disulfide isomerases also known to catalyze this reaction;
Wells et al., 1990). An increased production of glutathione between
3 and 24 h of HS appears supported by the decline in abundances
of cysteine and glutamate in this time period (Supplemental Figure
3B and Supplemental Data Set 1). That the abundance of glycine,
the third amino acid forming glutathione, is increasing might be
explained by its increased production by photorespiration.
However, the 1.4- to 1.9-fold upregulation of superoxide dismutases FSD1, MSD1, and MSD2, targeted to chloroplast, cytosol,
and mitochondria, respectively (Page et al., 2012), also suggests
the increased generation of superoxide (O22) under long-term HS.
As the dismutases convert superoxide to H2O2, another source for
the obviously increased accumulation of H2O2 might also be superoxide generated, e.g., during photosynthetic LEF by the Mehler
reaction at PSI or in the mitochondrial respiratory chain at complexes I and III (Mehler, 1951; Turrens, 1997; Asada, 2006). Either
source appears surprising, as the capacities for LEF and respiration
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The Plant Cell
are reduced by two-thirds and half, respectively, between 3 and 24
h HS. However, the absence of sufficient sinks for electrons in the
chloroplast may lead to the overreduction of ferredoxin and, thus,
increased Mehler reactions (Schreiber and Neubauer, 1990). This
scenario appears supported by the finding that the Chlamydomonas pgd1 mutant, impaired in TAG biogenesis, loses viability during
N starvation as a consequence of ROS accumulation. In the absence of TAG biogenesis as sink for NADPH and ATP, ROS was
suggested to derive from increased Mehler reactions (Li et al.,
2012). Moreover, proper electron transfer in mitochondrial and
plastidic membrane protein complexes might also be impaired
because essential structural lipids PG and SQDG are depleted.
While abundances of proteins involved in ROS scavenging did not
change or even decreased during the first 3 h of HS (Mühlhaus
et al., 2011), their accumulation after 24 h of HS can be clearly
considered as a late response to HS. This also is in line with previous proteomics studies on different land plant species exposed to
HS for more than 6 h, where levels of proteins like superoxide
dismutase, thioredoxin h, ascorbate peroxidase, and dehydroascorbate reductase have been found to increase (Ferreira et al.,
2006; Lee et al., 2007; Xu and Huang, 2008; Scafaro et al., 2010).
During Recovery from HS, Cells Appear to Readjust
Membrane Fluidity by Recycling Polyunsaturated FAs from
Lipid Bodies into Membrane Lipids
A first phase of recovery from HS, initiated by shifting cells back
from 42 to 25°C, lasts for about 8 h and ends when cells start
dividing again (Figures 2A and 8). Not surprisingly, during this
time most measures taken by the cells during HS are reversed.
As expected from the concept of homeoviscous adaptation
(Sinensky, 1974), cells need to adapt membrane fluidity again to
cooler temperatures and do so by increasing the content of
polyunsaturated FAs at the expense of saturated ones in
membrane lipids (Figure 7; Supplemental Figure 5). Since in
TAGs at the same time the content of polyunsaturated FAs
decreased while that of more saturated FAs increased, it appears that once again an exchange of FAs between TAGs and
membrane lipids occurred (Figure 6; Supplemental Figure 5).
This seems surprising because desaturation of FAs in membrane lipids can be achieved rapidly by desaturases (Russell,
1984). The answer might be associated with FA chain length; as
shown in Figures 7B to 7D, FAs in accumulating lipids not only are
more unsaturated, they are also longer, while those in declining
lipids not only are more saturated but also shorter. Hence, desaturation would need to come along with chain elongation. In
support of the idea that unsaturated, long-chain FAs are recycled
from lipid bodies rather than biosynthesized de novo, enzymes
BCC1 and FAB2, involved in elongation and desaturation of FAs
of plastidic lipids, are downregulated by ;20% during recovery
(Supplemental Data Set 2).
Recovery from HS Is Characterized by the Depletion
of Stress Protectants and the Resumption of
Anabolic Reactions
Apparently, TCA/glyoxylate cycle, glycolysis/gluconeogenesis,
and the Calvin cycle increased their activities during recovery,
as judged from the accumulation of succinate, fumarate, 2oxoglutarate, citrate, malate, glycerate, glycerate-3-phosphate,
and starch (Figures 2E and 4; Supplemental Figure 3 and
Supplemental Data Set 1). By contrast, levels of GPG, trehalose,
and amino acids asparagine, isoleucine, alanine, and threonine
declined rapidly during recovery, qualifying them as potential
compatible solutes. This is because compatible solutes, protecting denatured proteins from aggregation during HS, need to
be degraded during recovery as they would inhibit the reactivation of denatured proteins by molecular chaperones
(Singer and Lindquist, 1998a, 1998b). As bulk protein increased
by only 10% during the 8 h of recovery (Figure 2D), the decline of
amino acids probably only to some extent is due to protein
biosynthesis. Rather, they might be utilized for the biosynthesis
of purines and pyrimidines for DNA replication. Also, the degradation of trehalose and GPG may rapidly provide ribose sugars and phosphate for DNA biosynthesis.
Resumption of Cell Division Lags Behind Resumption of
DNA Replication
DNA biosynthesis resumed 2 to 3 h after shifting cells back to 25°C
and came along with the depletion of guanosine (Figure 2B;
Supplemental Figure 3B). At this time, cell cycle arrest in G1 apparently is relieved and cells enter the S1 state. However, it is not
clear why the ;14% of 2n cells in the population of heat-stressed
cells do not directly enter mitosis nor why the rapidly increasing
number of 2n cells during recovery do not. As such retarded G2/M
progression was reported for the Chlamydomonas fa2 mutant,
lacking a NIMA kinase (Mahjoub et al., 2002), perhaps NIMA kinase
members are thermolabile and regain activity only late during recovery? Surprisingly, despite a rapid onset of DNA biosynthesis,
the biosynthesis of most histone proteins lagged behind, such that
the total cellular histone content was reduced by ;26% after 8 h
recovery compared with pre-HS conditions (Figure 5B; Supplemental
Data Set 2, Note 1). This suggests the existence of a nonassembled
pool of histones in Chlamydomonas, as reported earlier in mammalian cells (Tsvetkov et al., 1989), perhaps to funnel protein biosynthesis capacity to more important targets. One such target might
be ribosomal proteins, whose levels increase during the 8-h recovery
period by more than they have decreased during the 24-h HS period,
leading to ;10% more ribosomes after 8 h recovery compared with
pre-HS conditions (Figure 5B; Supplemental Data Set 2, Note 1).
Readjustment of Protein Abundance during Recovery Often
Is Selective
During the 8-h recovery period, 38 proteins decreased significantly
more than expected from dilution by the 10% increase in bulk
protein in this period and therefore must have higher degradation
than biosynthesis rates (Supplemental Data Set 2, Note 2). Interestingly, 13 of these proteins are molecular chaperones and
immunophilins, thus indicating that an excess of molecular chaperones is not beneficial under nonstress conditions. This might be
because of futile ATP hydrolysis by the HSP100, HSP90, HSP70,
and HSP60 chaperones or because “overchaperoning” is counterproductive for assisting folding of denatured proteins to the
native state (Bersuker et al., 2013). Even more interesting is that
Responses to Heat Stress and Recovery
HSP100 family member ClpD1 and chloroplast HSP70B cochaperone CGE1 increased even further during recovery. For ClpD, this
exceptional behavior is difficult to explain as a clear functional
assignment for this protein is yet lacking (Sjögren et al., 2014).
During long-term HS, levels of HSP70B increased 2.6-fold, while
those of CGE1 only by 1.6-fold (Supplemental Data Set 2). As
CGE1 accelerates nucleotide exchange, the increased HSP70B to
CGE1 ratio during HS may increasingly shift HSP70B toward the
ADP-bound state and, thus, from a folding-supportive to a holdase
activity (Veyel et al., 2014b). Hence, decreasing HSP70B and increasing CGE1 levels during recovery may rapidly shift the system
back to a folding-supportive function.
For proteins involved in photosynthesis, we observed a similar
reversal of changes that took place during HS. Regarding the
light reactions, this is evidenced by a mild ;6% increase in
ATPase subunits and a decrease of PSI/II subunits (Figure 8;
Supplemental Data Set 2, Note 1). Most of the latter do not
decrease by significantly more than 10%, resulting from dilution
by bulk protein increase during recovery and thus may not be
actively degraded. The overall modest adjustments of light reaction proteins during recovery are accompanied by similarly
modest relaxations of LHC uncoupling, increases in PSII quantum yield and maximum quantum efficiency, and chlorophyll a/b
ratio (Figure 3; Supplemental Table 1). Accordingly, PLB-like
structures disappear by about half only between 4 and 8 h of
recovery (Supplemental Figure 2).
The imbalance between the 30 to 40% decrease in Rubisco
and ;60% increase in Rubisco activase generated during longterm HS is corrected during recovery such that Rubisco reaches
80% and Rubisco activase 100% of initial levels. These readjustments most likely increase Calvin cycle activity to meet the
need of ribose for DNA biosynthesis. Although levels of PGP1
decreased by 19% during recovery, they were still 1.9-fold
higher after 8 h recovery compared with pre-HS levels. The new
sink for ATP and NADPH provided by DNA biosynthesis should
decrease ROS that was produced as consequence of an overreduced photosynthetic electron transport chain (Schreiber and
Neubauer, 1990; Li et al., 2012). Accordingly, almost all proteins
involved in ROS scavenging declined during recovery, with the
exception of TRXo, HCP2, and HCP3, which continued to increase. The known or predicted mitochondrial localization of
these three proteins suggests increased ROS production by
mitochondrial respiration during recovery.
The continued increase of enzymes involved in BCAA biosynthesis during recovery is surprising, as levels of isoleucine
and leucine declined (Figure 4A; Supplemental Figure 3). Cells
might attempt to counteract the consumption of BCAA during
recovery; as shown previously for heat-stressed Salmonella
typhimurium, BCAAs for unknown reasons improve recovery
from HS (Hsu-Ming et al., 2012).
A Modified Growth Phase Is Indicated by the Resumption of
Cell Division Despite Remodeled Protein, Metabolite, and
Lipid States
After ;8 h of recovery from HS Chlamydomonas cells regained
their ability to divide (Figures 2A and 8). This appears surprising
because at this time the composition of metabolites, membrane
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lipids, and proteins was clearly distinct from pre-HS conditions
(Figures 4B, 5A, and 7E). Specifically, levels of several metabolites of the central metabolism were significantly higher after 8
h of recovery compared with pre-HS conditions (Supplemental
Data Set 1 and Supplemental Figure 3). Moreover, DGTS species with polyunsaturated long-chain FAs were strongly reduced, as were most SQDG species, while MGDG, DGDG, and
DGTS species with short, largely saturated FAs were enriched
(Supplemental Figure 5). While proteins of functional categories
protein folding, abiotic (heat) stress, redox, and BCAA synthesis
were still upregulated and histones still downregulated after 8 h
recovery compared with pre-HS conditions, ribosomal proteins
had overaccumulated (Figure 5B). This might have been triggered by the overaccumulation of starch, TAGs, and central
metabolites potentially signaling ample resources for protein
biosynthesis upon relief of cell cycle arrest. Proteins involved in
photosynthesis also had not relaxed to the pre-HS state. For
example, subunits of PSII remained increased by ;20% above,
and those of the ATP synthase remained decreased by ;12%
below initial levels (Supplemental Data Set 2, Note 1). Accordingly, and potentially exacerbated by the lasting deficiency of
thylakoid membrane lipid SQDG, LHC uncoupling, reduced PSII
quantum yield/maximum quantum efficiency, and reduced
chlorophyll a/b ratio prevailed, as did aberrant PLB-like structures in thylakoid membranes (Figures 2F and 3; Supplemental
Figure 2).
Overall, it appears that Chlamydomonas cells during recovery
focus on processes enabling them to resume cell division and
growth as quickly as possible (DNA biosynthesis, overaccumulation
of central metabolites, TAGs, starch, and ribosomes) rather than
the restoration of a cellular state apparently optimal for growth at
lower temperatures (e.g., original compositions of membrane
lipids and chaperones; full antenna coupling). This strategy
might be based on the fact that dilution by growth will eventually
restore the balance of cellular compounds; the availability of
ample carbon storage may abolish the need to fully restore
photosynthesis. However, as these “imbalances of cellular
compounds” in fact are the result of acclimation to HS, cells may
also maintain them in the frame of a precautionary concept to be
prepared for another HS. Perhaps in support for the latter idea,
average cell diameter and average contents of DNA, protein,
and chlorophyll were higher after 24 h of recovery compared
with pre-HS conditions, although cells had divided already ;1.6
times (Figure 2). Experiments monitoring cellular states for
days after recovery from prolonged HS will be required to
test whether such a “stress memory,” potentially realized by
epigenetic marks (Thellier and Lüttge, 2013), might exist in
Chlamydomonas.
HS Represents Another Trigger for TAG Accumulation with
Potential Impact on Biodiesel Production
Oleaginous algae produce only small quantities of TAGs under
optimal growth conditions but accumulate large amounts of
TAGs when placed under stress conditions like nutrient starvation, salinity, changes in medium pH, temperature, or light intensity (Hu et al., 2008). Although Chlamydomonas was not
considered to be an oleaginous alga (Sheehan, 1998), depriving
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The Plant Cell
it from N or S results in the accumulation of considerable amounts
of TAGs in lipid bodies (Wang et al., 2009; Cakmak et al., 2012).
The results from our study suggest that heat stress can be
considered as another stress causing TAG accumulation potentially exploitable for biodiesel production with Chlamydomonas. As heat stress and N and S starvation lead to a cell cycle
arrest, the latter might be a general trigger for TAG accumulation, as suggested earlier (Sheehan, 1998).
The suitability of algal biodiesel producers is not only determined by the quantity of TAGs but also by the degree of FA
saturation, as more saturated FAs shorten ignition delay time
and ensure better combustion quality (Knothe et al., 1998;
James et al., 2013). In this respect, we observed a rapid increase
of TAGs with highly unsaturated FAs within the first 2 h of HS,
which during prolonged HS and especially during recovery became more saturated (Figures 6 and 8). Hence, high-quality biodiesel may be produced by exposing Chlamydomonas cells to
prolonged HS at 42°C followed by recovery at lower temperatures. Such conditions are found in hot desert climates during
the day/night cycle and therefore may minimize costs for heating
and chilling. In this study, we supplemented growth medium
with acetate. Hence, it is important to test whether TAG
accumulation during HS also takes place under photoautotrophic growth conditions. As this is the case for N-starved
Chlamydomonas cells (Merchant et al., 2012), it is likely to be
true also under HS conditions.
Can Data on Chlamydomonas Be Used to Improve
Thermotolerance in Crop Plants?
Our system-wide and time-resolved analysis of acclimation
responses of Chlamydomonas to prolonged HS suggests a temporally ordered, orchestrated implementation of response elements
at various system levels (Figure 8). Hence, the best way to improve
crop plant thermotolerance probably would be to trigger the entire
cascade of responses before an expected heat wave, ideally by
applying a degradable compound. However, this requires detailed
knowledge on HS sensing and involved HS signaling pathways. A
sufficient increase in thermotolerance may also be achieved by the
triggering of single response elements, like the generation of
compounds involved in stress protection from the catabolism of
larger molecules or the biosynthesis of membrane lipids with more
saturated FAs. This may be achieved rather easily by engineering
involved key enzymes toward an externally controllable activity.
However, we first need to know the contributions of individual HS
response elements to overall thermotolerance. Moreover, it is possible that a response element implemented in isolation would have
negative effects. Clearly, more research on the plant HS response is
required to address this issue.
METHODS
Strains and Culture Conditions
Chlamydomonas reinhardtii strain CF185 (CF185, cwd, mt+, arg7 -,
complemented with plasmid-derived ARG7 wild-type gene) (Schroda
et al., 1999) was used in all experiments. Cells were grown mixotrophically
in Tris-acetate-phosphate (TAP) medium on an orbital shaker at 120 rpm, 25°C,
and constant illumination at ;40 mE m22 s21. Cells were counted and mean
cell diameter was determined using a Z2 Coulter Counter (Beckman Coulter).
For the HS recovery time course, CF185 cells were grown at constant illumination in TAP medium to mid-log phase, harvested by centrifugation at 25°C
and 1950g for 4 min, resuspended in TAP medium prewarmed to 42°C, incubated at 42°C in an agitating water bath for 24 h at constant illumination
(;40 mE m22 s21), and transferred back to pre-HS conditions.
DNA Content and Ploidy
Prior to DNA staining, cells were destained and fixed with ethanol (Garz
et al., 2012). Fixed cells were washed two times with ice-cold PBS buffer
and were kept in PBS at 4°C. DNA in cells was stained with propidium
diiodide (PI) (Sigma-Aldrich), dissolved in PBS ( final concentration 2.5 mg/
mL suspension; Coulson et al., 1977; Coulson and Tyndall, 1978), and
simultaneously treated with RNase A (final concentration 10 mg/mL) for 3 h
at 4°C. Prior to cytometric analysis, cells were washed once with ice-cold
PBS. RNase A and PI solution were freshly prepared before every experiment. To avoid bleaching, PI-treated cells were kept in the dark until
the analysis was finished. PI-dependent fluorescence was quantified by
flow cytometry using a FACS Calibur instrument (Becton Dickinson). For
excitation, a 488-nm argon laser was used. PI-derived fluorescence was
passed through a band-pass filter (central wavelength, 585 nm; half
bandwidth, 42 nm). Fifty thousand cells were analyzed for each sample.
Count rates did not exceed 4000 counts per second. Data were collected
with Cellquest software (Becton Dickinson) and analyzed by a program
written in MATLAB (The MathWorks).
Chlorophyll Content
Cell culture samples (200 mL) were snap frozen in liquid nitrogen and stored at
280°C. Acetone was added to thawed samples to a final concentration of
80%. Cells were ruptured mechanically and centrifuged to remove cells
debris. Chlorophyll absorption of supernatants was measured photospectrometrically (Lambda 25, UV/VIS spectrometer; Perkin-Elmer) at 646.6
and 663.6 nm and corrected against the absorption at 750 nm (chlorophyll a/
b = 0.01776 3 E646.6 + 0.00734 3 E663.6) (Porra et al., 1989).
Total Protein Content
Cell culture samples (8 mL) were harvested at 4°C and 3220g for 2 min, the
supernatant was discarded, and the pellet was frozen in liquid nitrogen
and stored at 280°C. The cell pellet was resuspended and incubated in 1
mL Lowry’s solution (solution A: 4 mg/mL NaOH + 20 mg/mL Na2CO3 in
H2O; solution B: 10 mg/mL potassium sodium tartrate + 5 mg/mL CuSO4
in water; mix solution A + B at 50:1 ratio) for 15 min (Lowry et al., 1951).
After adding 100 mL 13 Folin’s reagent, the suspension was incubated for
another 30 min. Samples were measured photospectrometrically
(Lambda 25, UV/VIS spectrometer) at 750 nm.
Starch Content
The starch-containing fractions of polar and semipolar metabolites gained
during lipophilic metabolite extraction were dried in a speed vac concentrator, reconstituted in 400 mL 0.1 M sodium hydroxide (NaOH), and
boiled for 30 min at 95°C. To neutralize the solution, 80 mL HCl-acetate
buffer was added (0.5 M HCl and 0.1 M acetate/NaOH, pH 4.9). Each
sample (240 mL) was mixed in a 1:1 ratio with starch assay reagent (SA-20
kit; Sigma-Aldrich) and incubated overnight at 37°C on an orbital shaker.
Afterwards, samples were centrifuged at 25°C and 1100g for 5 min, 50 mL
of each sample was dispensed in a microtiter plate, and 150 mL glucose
assay reagent (SA-20 kit; Sigma-Aldrich) added. The mixture was incubated for 15 min at 25°C and the absorbance measured at 340 nm.
Controls and calculations were performed according to the manufacturer’s instructions (SA-20 kit).
Responses to Heat Stress and Recovery
Electron Microscopy
Cell culture samples (8 mL) were harvested at room temperature and
1000g for 2 min. The supernatant was discarded, the pellet was washed
with 100 mM sodium cacodylate (pH 7.2) and centrifuged at 1500g for 1
min, and cells fixed for 2 to 14 h at 25°C in 100 mM sodium cacodylate
containing 2.5% glutaraldehyde (pH 7.2). Samples were processed further as described by Nordhues et al. (2012).
Spectroscopy
The 77K fluorescence emission spectra were recorded using a Jasco F6500 fluorimeter. Two replicates of whole cells from the culture flasks (;5
mg/mL total chlorophyll) were frozen directly in liquid nitrogen. Chlorophyll
a fluorescence was excited at 440 nm and fluorescence emission was
recorded from 655 to 800 nm. Data were corrected for the average
fluorescence emission between 790 and 800 nm and normalized either for
the PSII emission maximum around 687 nm or for the PSI emission
maximum around 712 nm.
Light response curves of chlorophyll a fluorescence at room
temperature were recorded using a Dual PAM 100 pulse amplitudemodulated fluorimeter (Heinz Walz). Before measurements, the cells
were illuminated for 5 min with far-red light followed by 5 min of dark
adaption. Each light response curve of chlorophyll a fluorescence was
fitted using the model of Eilers and Peeters (1988) integrated in the
DualPAM v1.11 software (Heinz Walz). Resulting parameters iK (intensity
where light becomes saturating), alpha (initial slope of relative rate of
electron transfer), and Pmax (maximum relative rate of electron transfer
[YII*PAR]) are shown in Figure 3 and Supplemental Table 1.
Photosynthesis-Irradiation Curves/Oxygen Evolution
and Consumption
Oxygen evolution and consumption were determined from three biological and three technical replicates each. Cells were taken prior to, 3 h,
and 24 h after shift from 25 to 42°C, adapted for ;10 min to 25°C, and
NaHCO3 was added to a final concentration of 10 mM. A 1-mL sample
of this suspension was placed into a multiport measuring chamber
(NOCHM-4; World Precision Instruments). The temperature-controlled
multiport measuring chamber was connected to an optical oxygen
measurement system (oxy-4 mini; PreSens) and illuminated by a surrounding LED ring. Light intensities within cell suspensions were determined by Spherical Micro Quantum Sensor US-SQS/L connected to
a DUAL-PAM-100 (Walz). Oxygen evolution was monitored for 4 min at
different light intensities (50, 150, and 600 mE m22 s21), whereas oxygen
consumption was recorded in the dark. Assuming that oxygen solubility in
water at 25°C under atmospheric pressure is 258 mM, oxygen evolution
and consumption were calculated based on time and cellular chlorophyll
content (mmol O2 h21 mg chlorophyll21).
15N
Quantitative Shotgun Proteomics
Cells were harvested by centrifugation at 4°C and 3220g for 2 min,
washed with 5 mM HEPES-KOH, transferred to Eppendorf tubes,
centrifuged again at 4°C and 21,500g for 2 min, resuspended in lysis
buffer (50 mM NH4HCO3, 1 mM DTT, and 1 mM NaCl), frozen in liquid
nitrogen, and stored at 280°C. 15N-labeled reference cells, grown and
harvested as described (Mühlhaus et al., 2011), were mixed with nonlabeled samples from the HS and recovery time course at a 15N/14N ratio
of 0.8 based on protein content determined by the BCA assay (Thermo
Scientific). Mixed cells were disrupted by two freeze/thawing cycles, and
soluble and membrane-associated proteins were separated by centrifugation in a table lab centrifuge at 4°C and 21,500g for 35 min. The
supernatant, containing soluble proteins, was recovered and the pellet,
21 of 28
containing membrane associated proteins, was washed once in lysis
buffer (50 mM NH4HCO3, 1 mM DTT, and 1 mM NaCl) and centrifuged
again. The resulting pellet, including the membrane-associated proteins,
was again resuspended in lysis buffer. Samples containing 200 mg soluble
and membrane-associated proteins as determined by the BCA assay
were precipitated in 80% acetone over night at 280°C. Precipitated
proteins were resuspended in 6 M urea, 2 M thiourea, reduced with 0.24
mM DTT at 25°C for 30 min, and carbamidomethylated with 1 mM
iodoacetamide at 25°C for 20 min in the dark. Samples were diluted with
40 mM ammonium hydrogen carbonate to 3 M urea and 1 M thiourea and
digested with 0.5 mg endoproteinase Lys C (Roche) on a rotating wheel for
at least 3 h at 37°C. The digest was diluted to 1.5 M urea and 0.5 M
thiourea with 20 mM ammonium hydrogen carbonate and 5% acetonitrile
and digested with 10 mL immobilized trypsin (Poroszyme immobilized
trypsin; Applied Biosystems) for at least 16 h at 37°C on a rotating wheel.
The resulting peptide composite was diluted with LC-MS-grade water to
a final acetonitrile concentration of 2.5%, desalted using SPEC-C18 solid
phase extraction plates (Varian/ Agilent Technologies), and dried in
a centrifugal evaporator. The dried peptides were resuspended in 2%
acetonitrile and acidified with 0.5% acetic acid.
Liquid chromatography and mass spectrometry of peptides were
performed according to Mühlhaus et al. (2011) with the exception that
MS1 spectra were acquired at a resolution of 60,000 and the six most
intense precursor ions were selected for MS2 analysis. All single charged
ions were excluded from the analysis in two of three technical replicates of
each sample and dynamic exclusion was set 30 s after two occurrences.
Proteins were identified and quantified using the IOMIQS framework
(Mühlhaus et al., 2011). IOMIQS features four different search engines for
peptide identification: Mascot (version 2.2.04; Perkins et al., 1999), Sequest (version 28; Eng et al., 1994), OMSSA (version 2.1.4; Geer et al.,
2004), and X!Tandem (version 2009.04.01.1; Craig and Beavis, 2004). For
peptide quantification, IOMIQS employs XPRESS (Han et al., 2001).
For database searches, the precursor mass tolerance was set to 10 ppm
(for OMSSA, it was set to 0.015 D), whereas the fragment ion tolerance was
set to 0.8 D. Up to three missed cleavages were allowed for tryptic peptides.
As variable modifications, carbamidomethylation of cysteine and oxidation of
methionine were selected for Mascot, OMSSA, and X!Tandem. To identify
15N-labeled peptides, the searches were repeated with exactly the same
settings but in 15N mode. The database search was performed against
a combined protein sequence database including all translated sequences of
Chlamydomonas Augustus gene models version 10.2 as well as all sequences
from Chlamydomonas mitochondrial and chloroplast proteins (downloaded
from http://chlamycyc.mpimp-golm.mpg.de/files/sequences/protein/). In
addition, all target sequences were shuffled and added to the search database to perform the target decoy approach for FDR calculation (Elias and
Gygi, 2007). The FDR distributions of all search algorithms were aligned to
allow for overall peptide identifications with greater than 95% probability. For
protein inference, peptides were classified according to their information
content according to Qeli and Ahrens (2010). The Occam’s Razor approach
(Nesvizhskii et al., 2003) was applied to each peptide class from high to low
information content to report a minimal set of proteins explaining the identified
peptides by proteins and protein groups. Proteins were classified according to
their peptide(s) with the maximal information content.
For peptide quantification, XPRESS was used with peptide mass
tolerance set to 0.8 D. The “light-to-heavy” ratios calculated by XPRESS
for identified peptides were corrected for unequal mixing of labeled and
unlabeled proteins in each sample by the geometric median of light-toheavy ratios of all peptides in that sample. The median of peptides lightto-heavy ratios was used to determine the relative abundance for the
corresponding proteins. The mean and SD was calculated from three
biological and three technical replicates. For protein quantification, both
fractions (soluble and membrane-associated proteins) were pooled and
mean and SD was calculated from three biological and three technical
22 of 28
The Plant Cell
replicates each. Proteins that showed significant pairwise anticorrelation
between the two fractions were excluded from the data set.
GC-MS: Polar Metabolites
The experiments were done in three independent biological replicates and
from each culture four sample replicates were harvested and processed. For
profiling polar metabolites, 2.5 to 3 mL cell culture was harvested by fast
filtration as described by Krall et al. (2009), which took around 15 to 30 s. The
frozen filters with cells were stored at 280°C until extraction. Cells were
extracted using a mixture of methanol and chloroform (7:3, v:v) including
internal standards (0.02 mg/mL 13C6-sorbitol and 0.2 mg/mL d4-alanine).
Extraction buffer (300 mL) precooled to 220°C was added to the frozen
sample, followed by vortexing for 10 min at 4°C. The sample was quickly spun
down and the liquid was transferred into a new tube. Another 300 mL extraction buffer was used to wash the filter and added to the first 300 mL. The
mixture was then strongly vortexed at 4°C for 60 min. Ultrapure water (300 mL;
LC- grade; Merck) was added to the extraction mixture for phase separation
into methanol-water and chloroform phase. To break any remaining intact
cells, the sample was twice frozen in liquid nitrogen and thawed on ice. The
sample was centrifuged at 21,500g, 0°C for 10 min, and 700 mL methanolwater phase was transferred to a new tube, dried in a speed vac overnight,
flushed with Argon, and stored at 280°C.
Sample derivatization and GC-MS analysis of polar metabolites was
done as described previously (Strehmel et al., 2010; Mettler et al., 2014),
except that during derivatization only 20 and 40 mL of volume for the
methoxamination and sylilation steps were used, respectively. The
ChromaTOF software (version 3.22; LECO) was used to visually inspect,
baseline correct, and export the GC-MS raw data into NetCDF file format
with the following settings: baseline offset, 1.0; smoothing of data points,
5; signal-to-noise ratio, 10. Further preprocessing, including peak finding,
data matrix construction, and analyte annotation, was done using the
TagFinder software (Luedemann et al., 2008) using the spectral reference
library of the Golm Metabolome Database (http://gmd.mpimp-golm.mpg.
de/; Kopka et al., 2005; Schauer et al., 2005; Hummel et al., 2010), dated
3/15/2011. At least four matching masses were required with a minimum
match value of 750 and a maximum retention index deviation of 1.0 for the
positive annotation of an analyte (Strehmel et al., 2008). From the annotated analytes, a data profile was calculated, where the intensities of
each analyte were scaled to 100 over the different samples. The generated data matrix was normalized by the internal standard 13C6-sorbitol,
the sampled biomass, followed by probabilistic quotient normalization
(Dieterle et al., 2006) with the average from all chromatograms set as
reference chromatogram. Next, standards were removed from the data
(normalized GC-MS raw data; Supplemental Data Set 1). Values of analytes representing the same metabolite were summed and technical
replicates were averaged prior to further analysis.
LC-MS: Lipophilic Metabolites
The experiments were done in three independent biological replicates and
from each culture two sample replicates were harvested and processed.
Samples (2 mL) of cell culture were harvested by centrifugation at 4°C and
21,500g for 2 min. The supernatant was removed and the cell pellet was
directly frozen in liquid nitrogen and stored at 280°C. Extraction of lipophilic
metabolites and LC-MS measurements was done as described by Hummel
et al. (2011). Known exact masses were used to extract data of 198 lipids from
the obtained chromatograms (Hummel et al., 2011). The resulting data matrix
comprised 198 metabolites and initially 48 samples (columns) of which two
samples were removed prior to further analysis. The data was normalized to
the internal standard PC 34:2 and background (average of 8 blank samples)
was subtracted. The resulting data were subjected to probabilistic quotient
normalization (Dieterle et al., 2006) with the average of all chromatograms set
as reference chromatogram (normalized LC-MS raw data; Supplemental Data
Set 4). The normalized data were averaged over the technical replicates and
used for further analyses. Heat maps were generated with the gplots-package
in R (http://www.r-project.org/) and reworked in Adobe Illustrator (Adobe
Systems).
General Analysis of Molecular Data
All samples were normalized method specific to minimize technical
influences. For assessing significant changes of metabolites and proteins,
data were log2 transformed and tested with a one-way ANOVA over all
time points using a significance threshold (P value # 0.05) after correction
for multiple hypothesis testing according to Benjamini and Hochberg
(1995). To investigate common behaviors in the data sets, hierarchical
clustering was performed. For this, polar metabolites and protein data
were log2 transformed and lipid and TAG data z-transformed [Z = (X 2 m)/s,
where m = mean of X and s = SD of X]. The hierarchical clustering was
performed using Euclidian distance measure and Ward linkage criterion.
The cluster number was determined using gap statistic taking into account the original data set’s shape (Tibshirani et al., 2001) and further
refined by manual inspecting the cluster dendrograms visualized using D3
graphical library (http://d3js.org/d3.v3.min.js). Central tendencies of the
cluster were visualized by plotting the cluster means.
PCA was performed on the log2-transformed data of significantly
changing polar metabolites and proteins, or z-transformed data of lipids
and TAGs, after removing missing values. To calculate the changes
between time points with maximal variance revealed by PCA, contrast
analysis was employed in the context of the ANOVA according to Hay’s
method for the protein data. For metabolites, multiple t testing was performed with the correction of P values according to Benjamini and
Hochberg (1995). All analyses were performed using Microsoft F# functional
programming language with the bioinformatics library FSharpBio (available
on GitHub: https://github.com/muehlhaus/FSharpBio) and the graphical
library FSharpChart (downloaded from http://code.msdn.microsoft.com/
windowsdesktop/FSharpChart-b59073f5).
Functional Enrichment of Proteins
Identified proteins were functionally annotated using MapMan (Thimm
et al., 2004) for Chlamydomonas including changes made by the community within the IOMIQS functional annotation tool ( T. Mühlhaus, A.
Lüdemann, and M. Schroda, unpublished data). The analysis of overrepresentation of certain functional categories was done using hypergeometric formulation of the null hypothesis to test that the change in
protein abundance and its belonging to an annotation category is statistically independent (Rivals et al., 2007). P values were adjusted according to the Benjamini and Hochberg procedure and the significance
threshold was set to a P value # 0.05. To calculate functional enrichment
of proteins during heat stress and recovery the total protein population for
which we have behavioral information (1985) in our experiment and the
number of differentially changing proteins (772) were used. In addition,
overrepresentation of functional categories on each comparison between
time points with maximal variance was determined. The overlap in these
global and local enrichment calculations was used to gain a more robust
analysis. The hierarchical topology of the MapMan annotation was
simplified using only upper parent category for visualization.
Comparison of BCAA Content per Protein
To compare the BCAA content in different protein sets, quantile distributions of BCAA per protein were calculated for each set (Karlin and
Brendel, 1992). The significance of the differences of the distributions was
assessed by a two-tailed Student’s t test (Baudouin-Cornu et al., 2001).
The protein sets were defined by the contrast between time point 0 and
24 h HS, also assessed by two-tailed Student’s t test, and divided into
Responses to Heat Stress and Recovery
up- and downregulated proteins. The cellular proteome based on Augustus 10.2 gene models was used as reference set.
Determination of Minimal Set of Actively Degraded Proteins
To separate the set of proteins decreasing in abundance between two
time points (T1 and T2) into subsets of proteins (A) actively degraded and
(B) diluted by the increase of bulk protein, the expected dilutions at each
contrast (T2 versus T1) were calculated (Equation 1).
Expected dilution ½% ¼
!
proteinT1
2 1 ∗ 100%
proteinT2
ð1Þ
Individual proteins whose abundance was below that expected from dilution
were assigned to set A and all others to set B. The statistical significance was
determined by a two-tailed Student’s t test followed by Benjamini and
Hochberg correction. The significance threshold was set to a P value # 0.01 for
a stringent selection of actively degraded proteins.
Accession Numbers
Sequences of the genes/proteins mentioned in this work can be found at
Phytozome (http://www.phytozome.net/ ) using the gene identifiers provided in Supplemental Data Set 2 or via the Functional Annotation tool
(http://iomiqsweb1.bio.uni-kl.de/ ).
Supplemental Data
The following materials are available in the online version of this article.
Supplemental Figure 1. Starch Quantification via Planimetry.
Supplemental Figure 2. Development of Prolamellar Body-Like
Structures during Heat Stress and Recovery.
Supplemental Figure 3. Behavior of All 43 Significantly Changing
Metabolites and Corresponding Enzymes in Cellular Metabolic Pathways.
Supplemental Figure 4. Protein Cluster Tree.
Supplemental Figure 5. Heat Map Illustrating the Behavior of All
Measured Triacylglycerol and Lipid Species during the HS and
Recovery Time Course.
Supplemental Figure 6. TAG Cluster Tree.
Supplemental Figure 7. Comparison of Proteome States at 3 h HS
and 24 h HS.
Supplemental Figure 8. Comparison of Branched-Chain Amino Acid
Content between Proteins Up- and Downregulated after 24 h HS.
Supplemental Table 1. Photosynthesis Parameters.
Supplemental Data Set 1. Polar Metabolite Data.
Supplemental Data Set 2. Protein Data.
Supplemental Data Set 3. Protein Cluster Enrichment.
Supplemental Data Set 4. Lipophilic Metabolite Data.
ACKNOWLEDGMENTS
This work was supported by the Max Planck Society and grants from the
Federal Ministry of Education and Research (BMBF), Germany, within the
frame of the GoFORSYS Research Unit for Systems Biology (FKZ 0313924)
and the Deutsche Forschungsgemeinschaft (Schr 617/6-1). We thank Mark
Aurel Schöttler for help with spectroscopy measurements, Joost van Dongen
and Carola Päpke for help with oxygen measurements, and Ru Zhang for
critically reading the article.
23 of 28
AUTHOR CONTRIBUTIONS
M. Schroda, D.H., D.V., T.M., and F.S. designed the experiments and
analyzed the data. D.H., D.V., and F.S. performed all experiments in
cooperation with J.J. and P.G. for lipidomics analyses, I.F. and J.K. for
metabolomics analyses, M. Sandmann and M. Steup for DNA content
determination, and A.-K.U., S.S., and S.G. for electron microscopy. T.M.
and D.V. performed the bioinformatic analyses. M. Schroda, T.M., and D.
H. wrote the article.
Received August 12, 2014; revised October 13, 2014; accepted October
24, 2014; published November 18, 2014.
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Systems-Wide Analysis of Acclimation Responses to Long-Term Heat Stress and Recovery in the
Photosynthetic Model Organism Chlamydomonas reinhardtii
Dorothea Hemme, Daniel Veyel, Timo Mühlhaus, Frederik Sommer, Jessica Jüppner, Ann-Katrin
Unger, Michael Sandmann, Ines Fehrle, Stephanie Schönfelder, Martin Steup, Stefan Geimer, Joachim
Kopka, Patrick Giavalisco and Michael Schroda
Plant Cell; originally published online November 18, 2014;
DOI 10.1105/tpc.114.130997
This information is current as of June 17, 2017
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