Measuring the turnover rates of Arabidopsis proteins using

TECHNICAL ADVANCE

Measuring the turnover rates of Arabidopsis proteins usingdeuterium oxide: an auxin signaling case study

Xiao-Yuan Yang1,2, Wen-Ping Chen2,3, Aaron K. Rendahl4, Adrian D. Hegeman1,2,3, William M. Gray1,2 and Jerry D. Cohen2,3,*

1Department of Plant Biology, University of Minnesota, St. Paul, MN 55108, USA,2Microbial and Plant Genomics Institute, University of Minnesota, St. Paul, MN 55108, USA,3Department of Horticultural Science, University of Minnesota, St. Paul, MN 55108, USA, and4School of Statistics, University of Minnesota, St. Paul, MN 55108, USA

Received 3 March 2010; revised 4 May 2010; accepted 21 May 2010; published online 21 June 2010.*For correspondence (fax +1 612 624 4941; e-mail [email protected]).

The author responsible for distribution of materials integral to the finding presented in this article in accordance with the policy described in the Instruction for

Authors (http://www.theplantjournal.org) is: Jerry D. Cohen ([email protected]).

SUMMARY

Rapid environmental responses in plants rely on endogenous signaling mechanisms, which in many cases are

mediated by changes in protein turnover rates. It is therefore necessary to develop methods for measuring

protein dynamics that monitor large sets of plant proteins to begin to apply a systems biology approach to the

study of plant behavior. The use of stable isotope labeling strategies that are adaptable to proteomic methods

is particularly attractive for this purpose. Here, we explore one example of such methods that is particularly

suitable for plants at the seedling stage, where measurement of amino acid and protein turnover rates is

accomplished using a heavy water labeling strategy. The method is backed by microarray evaluation to define

its feasibility for specific experimental approaches, and the CULLIN-ASSOCIATED AND NEDDYLATION

DISSOCIATED 1 (CAND1) and TRANSPORT INHIBITOR RESPONSE 1 (TIR1) proteins are used to illustrate the

potential utility in understanding hormonal signaling regulation. These studies provide insight not only into

the potential utility of the method, but also address possible areas of concern regarding the use of heavy water

labeling during plant growth. These considerations suggest a prescription for specific experimental designs

that minimize interference resulting from the induction of treatment-specific gene expression in the results

obtained.

Keywords: auxin response, deuterated water, protein stability, proteomics, stable isotopes, turnover rates.

INTRODUCTION

Plant development is characterized by the orderly appear-

ance and disappearance of a succession of regulatory and

structural proteins, and the mRNAs that encode them.

Together, biosynthesis and degradation rates determine the

rate of protein turnover (Pratt et al., 2002). The kinetics of

protein turnover are critical to the cellular regulatory pro-

cesses that allow plants to rapidly respond to changing

environmental conditions, or intracellular signal molecules,

by altering the levels of key proteins. These considerations

indicate that the determination of protein turnover often has

the same or greater importance as gene transcription and

protein translation when characterizing functional changes

in the cell (Li, 2009). To date, much of the protein analysis in

plants has focused on the comparison of protein abundance

under different conditions, and the changes in the steady-

state levels of proteins is frequently and, often erroneously,

cited as a measure of the dynamics of protein metabolism.

However, large changes in the flux can be accompanied

with little or no change in levels, and, conversely, minor

alterations in the balance between rates of synthesis and

degradation can have dramatic effects on the levels of

proteins or metabolites (Cargile et al., 2004; Bateman et al.,

2007). Determination of protein turnover rates provides

further insight into metabolic dynamics, and requires

680 ª 2010 The AuthorsJournal compilation ª 2010 Blackwell Publishing Ltd

The Plant Journal (2010) 63, 680–695 doi: 10.1111/j.1365-313X.2010.04266.x

methods for the measurement of changes in absolute

abundance as well as metabolic flux.

Efforts aimed at measuring protein turnover have a long

history. The development of isotopic methods for the

measurement of protein turnover has parallel origins with

early studies of de novo synthesis of cellular proteins

(Zielke and Filner, 1971; Chrispeels and Varner, 1973). These

methods can be divided into several general approaches

involving the use of labeling techniques. General labeling

methods, such as the use of 2H2O, 18O, 15N or 13C to label the

whole organism, were used to show de novo synthesis, as

indicated by an increase in the density of the resulting

proteins (Gonzalez-Prieto et al., 1995; Gawlitzek et al., 1999;

Yao et al., 2001; Goshe and Smith, 2003; Cargile et al., 2004;

Roessner-Tunali et al., 2004; Busch et al., 2006; Belloto

et al., 2007; Schaff et al., 2008; Zhao et al., 2009). Specific

isotopically-labeled (radioactive or stable isotope) amino

acids have been used to label cellular proteins, with labeling

rates reflecting de novo synthesis, and the rate of loss

monitoring proteolysis (Cooke et al., 1979; Thompson et al.,

1989; Ong et al., 2002; Doherty et al., 2005; Kruger et al.,

2008). The application of stable isotope labeling using

individual amino acids [as suggested, for example, for the

very special case of cell culture (SILAC; Ong and Mann,

2007) is made more complex because plants are autotroph-

ic, and actively synthesize all amino acids de novo. Thus,

the supplied labeled amino acids are diluted, and are not

incorporated into proteins efficiently (Gruhler et al., 2005).

In addition, when labeling whole plants, specific amino

acids are not transported equally to all cells in all tissues,

with the potential to bias the analytical result based on the

particular experimental design. Among these various label-

ing techniques, deuterium oxide has a long history as a

protein and amino acid label, and the use of deuterium

labeled compounds and 2H2O labeling has proven effective

for many different types of studies (Mitra et al., 1976; Busch

et al., 2006; Belloto et al., 2007; Xiao et al., 2008). The

advantage of deuterium oxide labeling is that it is ‘totally

invasive’, in that it rapidly enters cellular compartments and

equilibrates with the water environment. Limitations on the

use of 2H2O include the fact that multicellular organisms are

limited in the percentage of deuterium they tolerate. The

simplicity of deuterium labeling, the advantages of using

water as a rapidly exchangeable label and the economy of

deuterium over other alternatives, suggests that its use

should be explored further, and both advantages and

precautions in its application should be documented.

Here, we describe a 2H2O labeling system that we devel-

oped using Arabidopsis specifically for use with intact

seedlings, where other labeling techniques are problematic

because of carbon and nitrogen recycling, as well as

because of the other reasons outlined above. We illustrate

its utility by determining the turnover rates of 15 amino acids

and two proteins that are involved in the auxin signal

transduction pathway. We also present physiological and

microarray analysis of Arabidopsis plants grown on 2H2O to

provide additional insight into the effects of deuterium oxide

on plant growth.

RESULTS

Characterization of the effects of 2H2O on plant growth

Previous studies have reported that 2H2O inhibits seed

germination and further development in plants (Thomson,

1963). Our analyses of Arabidopsis growth on 2H2O media

confirm these findings. Seedlings grown on 2H2O media

exhibited shorter roots and hypocotyls, smaller cotyledons

and fewer lateral roots, compared with seedlings grown

on water media. We measured the root and hypocotyl

lengths of 5- and 7-day-old seedlings grown on media with

increasing 2H2O concentrations. The effect of heavy water

on plant growth was concentration dependent (Figure 1).

For 7-day-old seedlings, the inhibition of hypocotyl length

ranged from 10 to 40%, and the inhibition of root length

was from 10 to 60% over the concentrations of 2H2O

tested.

Although 2H2O has been known to inhibit plant growth for

more than 40 years, the basis for this toxicity has not been

fully elucidated (Sacchi and Cocucci, 1992; Kushner et al.,

1999). Thus, we conducted a microarray experiment to

determine the effects of 2H2O on gene expression. RNA was

prepared from seedlings grown for 7 days on either control

media or 30% 2H2O to examine the long-term effects of 2H2O

treatment, and from seedlings shifted from H2O media to

either 2H2O or H2O, or from 2H2O media to H2O for 4 h to

examine short-term effects on gene expression. Genes that

were up- or downregulated by at least twofold in response to2H2O treatment, with a P value below 0.05, were analyzed. A

volcano plot provides an overview of differentially

expressed genes (Figure 2a–b). The expression of the vast

majority of genes was unaffected by 2H2O treatment. Only

122 of the genes with detectable expression levels were

increased, and 99 genes were decreased, by 7 days of

continuous 2H2O treatment. Surprisingly, the 4-h 2H2O

treatment had a more dramatic effect on gene expression,

with 509 of the genes being increased and 258 decreased. As

expected, plants grown on 2H2O and transferred for a 4-h

H2O recovery exhibited the least changes in gene expres-

sion, with seven of the genes induced and 166 repressed.

Although several changes in gene expression were com-

mon amongst the different treatments (Figure 2c), the

overlap in affected genes was not extensive. Among the

509 genes in which expression levels were increased by 4 h

of 2H2O treatment, the expression levels of 7.1% of these

genes were also increased by continuous 2H2O treatment.

Likewise, among the 258 genes in which expression levels

were decreased by 4 h of 2H2O treatment, the expression

levels of only four of these genes were also decreased by

Measuring the turnover rates of Arabidopsis proteins 681

ª 2010 The AuthorsJournal compilation ª 2010 Blackwell Publishing Ltd, The Plant Journal, (2010), 63, 680–695

continuous 2H2O treatment. These findings suggest that

there are ‘short-term’ and ‘long-term’ affects of 2H2O on

gene expression: many of the changes in gene expression

that occur initially with 2H2O dissipate over time.

We used hierarchical clustering to further analyze the

expression profiles (Figure 3a). We found that seedlings

grown under continuous and 4-h 2H2O treatments exhib-

ited greater similarity in their expression patterns (Fig-

ure 3a left and middle columns). Most genes affected by

the 4-h 2H2O treatment showed a similar trend with those

found with continuous 2H2O treatment, but failed to reach

the twofold threshold. A total of 165 genes were specifi-

cally affected by continuous 2H2O treatment, and 652 genes

were specifically affected by 4 h of 2H2O treatment (Fig-

ures 2c and 3a). Furthermore, most genes with increased

expression levels after 4 h of 2H2O treatment were also

diminished by the 4-h H2O recovery (Figure 3a middle and

right columns).

Functional categories and metabolic pathway of regulated

genes

We used GO annotation to functionally classify the genes

identified in our microarray analysis (Figure 3b). Stress

response genes were most highly represented, including

several wound, pathogen, heat-shock and oxidative stress

responsive genes (Tables S1 and S2). All microarray data

are available at the GEO website (http://www.ncbi.nlm.nih.

gov/geo) to provide reference information for the use of this2H2O labeling strategy, and to allow the determination of

suitability for particular experimental designs.

Using 2H2O to measure turnover rates of amino acids

To evaluate the efficiency of the 2H2O labeling strategy, we

analyzed the turnover rates of free amino acid pools of

seedlings during both incorporation and dilution experi-

ments. During incorporation experiments, 7-day-old seed-

lings grown in ATS media were transferred into media

containing 30% 2H2O, and samples were collected at differ-

ent time points. During dilution experiments, seedlings were

transferred from ATS media containing 30% 2H2O to normal

ATS media.

We monitored 15 amino acids by GC-MS analysis: the

plant amino acid separation is shown in Figure 4. As shown

in Figure 5, when seedlings were transferred from water

media to 30% 2H2O media, the mass spectral features for

each amino acid were shifted to higher m/z values, reflecting

deuterium incorporation. Similarly, when seedlings were

transferred from 30% 2H2O media to water media, the mass

spectral features of the amino acids showed the anticipated

shift to lower m/z values. Different amino acids exhibited

different mass spectral shifts at experimental time points,

which probably reflects the diversity in the metabolic

processes occurring (Harada et al., 2006). Using the elemen-

tal compositions for each amino acid, we calculated labeling

ratios at different times such that the turnover and exchange

rates for each individual amino acid can be determined.

Labeling efficiencies of amino acids ranged from 60 to

approximately 100% of the theoretical values. Under the

assumption that amino acid turnover obeys first-order

reaction kinetics, it was possible to calculate the half-lives

(a)

(b) (c)

Figure 1. 2H2O inhibition of Arabidopsis seed-

ling growth.

(a) Seven-day-old seedlings grown on ATS

medium with increasing 2H2O concentrations.

Scale bar: 1 cm.

(b) and (c) Phenotypic comparison of 5- and

7-day-old seedlings grown on ATS medium with

increasing 2H2O concentrations.

682 Xiao-Yuan Yang et al.


for the 15 amino acid pools following either incorporation or

dilution experiments (Figures 6 and 7; Table 1). A high level

of diversity in the individual half-lives among these 15 amino

acids was observed. Whereas the half-lives of Ala, Asp, Glu

and Ser were <2 h, Leu, Ile, Tyr and Lys exhibited much

longer half-lives (more than 1 day during the incorporation

experiment; Table 1), although the remaining amino acids

had intermediate rates. Curiously, other than the four amino

acids with fast turnover rates, the others exhibited two-

phase kinetic behavior. During the first 8–16 h, the rates of

isotopomer replacement were fast, but the subsequent

relative isotope enrichment of these amino acids arrived at

an amino acid-dependent plateau value, where the rates

decreased dramatically.

Similar amino acid turnover experiments were also

conducted using a 15N-labeling system. Probably because

of the active N recycling during seedling growth, the

measured amino acids exhibited a much longer apparent

half-life relative to that determined using deuterium oxide

(Table S3). This anomalous amino acid turnover from 15N-

labeling focused our studies on 2H2O labeling with seedling

plants.

Examining protein turnover rates using 2H2O labeling

The CULLIN-ASSOCIATED AND NEDDYLATION DISSOCI-

ATED 1 (CAND1) and TRANSPORT INHIBITOR RESPONSE 1

(TIR1) proteins were selected from the evaluation of 2H2O

labeling for the analysis of protein turnover. Both proteins

(a)

(b) (c)

Figure 2. 2H2O altered gene expression in Arabidopsis seedlings.

Key: H2O, 7-day-old seedlings grown on normal media; 2H2O, 7-day-old seedlings grown on 30% 2H2O; H2O–H2O, 7-day-old seedlings grown on H2O media and

transferred to H2O media, as the control; H2O–2H2O, 7-day-old seedlings grown on H2O media and transferred to 30% 2H2O media; 2H2O–H2O, 7-day-old seedlings

grown on 30% 2H2O media and transferred to H2O media.

(a) Volcano plots generated by log2-transformed gene expression values against the negative log10 transformed P values from the Student’s t-test: magenta,

upregulated genes with statistically different expression levels (P < 0.05) that increased by at least twofold; blue, downregulated genes with statistically different

expression levels (P < 0.05) that decreased by at least twofold.

(b) Summary of regulated genes with statistically different expression (P < 0.05) and fold change above 2 (magenta) or below –2 (blue).

(c) Summary of common genes affected under different treatments. Dark blue, light blue and cyan represent genes with decreased expression level in continuous2H2O treatment, 4-h H2O recovery and 4-h 2H2O treatment, respectively; magenta, red and orange represent genes with increased expression level in continuous2H2O treatment, 4-h H2O recovery and 4-h 2H2O treatment, respectively; gray represents common genes.



play important roles in auxin signal transduction (Gray et al.,

2001; Chuang et al., 2004; Dharmasiri et al., 2005; Zhang

et al., 2008). To facilitate purification, the PCAND1:CAND1-HA-

Strep affinity tag construct was introduced into wild-type

plants. TIR1 was purified using an AXR2/IAA7 domain-II

affinity column. Because the purification of native TIR1

yielded insufficient protein for robust MS analysis, we

utilized seedlings overexpressing the TIR1-myc transgene.

These experiments were designed to yield turnover rates

of CAND1 and TIR1 in both incorporation and dilution

experiments.

We analyzed changes in mass isotopomer distribution of

the peptides derived from trypsin digestion of CAND1 and

TIR1-myc by LC-MS/MS. Typical mass isotopomer distri-

butions of peptides from CAND1 and TIR1-myc are pre-

sented in Figure 8. During the incorporation experiments,

the mass isotopic distribution in the original and in the

newly synthesized peptides were resolved, although there

was some overlap in the distribution. These results are

consistent with the observations of Cargile et al. (2004). The

resolved mass isotopic distributions at the early time

points, and the observation that the total shape of the

(a) (b)

Figure 3. 2H2O altered gene expression.

(a) Overview of the 2H2O effect on genome expression shown by a cluster display. The color scale is shown at the bottom of the figure. Genes included on the figure

were altered by twofold or more, by at least one of the treatments.

(b) Functional categorization designates genes with increased (magenta) or decreased (blue) expression levels under different treatments.

Figure 4. Separation and detection of amino

acids from an Arabidopsis seedling extract as

analyzed by GC-MS.



mass isotopic distribution did not change significantly,

implies that deuterium incorporation into most amino acids

pools was almost complete, and reached a temporary

quasi-steady state over a period of 8 h. This interpretation

is consistent with the amino acid analyses.

The resolved mass isotopic distribution made data analy-

sis for the relative abundance of labeled peptides relatively

straightforward. The peptide peaks with the same m/z value

as the 0-h peaks were considered to be associated with the

original peptide, and the relative intensities were added

together: Pori. The relative intensities of the corresponding

peaks at higher m/z values associated with newly synthesized

peptide were similarly combined: Pnew. The relative abun-

dance of labeled peptide (Rt) is equal to Pnew/(Pori + Pnew). As

protein turnover is considered to be first order, a single

exponential curve was fitted to the set of Rt data from the

time-course experiment to generate a first-order rate con-

stant: k. The half-life of the particular peptide was calculated

as t1/2 = ln(2)/k. During the incorporation experiment, the

calculated half-life of CAND1 was approximately 34 h, and

the half-life of TIR1-myc was approximately 10 h. The half-

lives of different peptides from the same protein exhibited

substantial uniformity, as the standard deviations were less

than 10% of the mean value of the different peptide half-lives

(Figures 9a,c and 10a,c; Tables S4 and S5). We also analyzed

the half-lives of peptides of several native proteins that

co-migrated with TIR1-myc on PAGE during the incorpora-

tion analyses. These native proteins exhibited a large range

of different half-lives, which confirms that the 2H2O labeling

system effectively reflects different metabolic dynamics for

individual proteins (Table S6).

During the dilution experiments, peptide isotopic distri-

butions could not be calculated using a simple binomial

distribution. The original peptide spectrum and the spectral

envelope contributed by the newly synthesized peptide

overlap, confounded attempts to derive protein turnover

information using calculated isotopic distributions that rely

on random label incorporation. As an alternative approach,

we assumed that two conditions occur during the 2H2O

dilution experiments.

(i) Each peak of the mass spectral envelope of the original

peptide degrades at the same relative rate. Consequently,

the intensity ratios between different mass peaks result-

ing from the original peptide do not change during the

experiment.

(a) (b)

(c) (d)

(e) (f)

(g) (h)

Figure 5. Mass isotope distribution of alanine

(a–d) and leucine (e–h).

(a) Alanine and (e) leucine at 0 h in the incorpo-

ration (I) experiment. (b) Alanine and (f) leucine

at 96 h of incorporation. (c) Alanine and (g)

leucine at 0 h of dilution (D). (d) alanine and (h)

leucine from at 96 h of dilution.



Figure 6. Time course of changes in the relative isotope abundance of plants grown with 2H2O label during an incorporation experiment.

The set of time points were 0, 1, 2, 4, 6, 8, 16, 24, 48, 72 and 96 h. Error bars indicate the standard deviation.



Figure 7. Time course of changes in the relative isotope abundance of plants grown with 2H2O label during a dilution experiment.

The set of time points were 0, 1, 2, 4, 6, 8, 16, 24, 48, 72 and 96 h. Error bars indicate the standard deviation.



(ii) As deuterium labeling is followed by growth on H2O, the

signal abundance within the isotope distributions pre-

dictability shifts to lower m/z components of the peptide

isotopic envelope. This leaves the higher m/z components

of the spectral envelope largely uncontaminated by new

signal. Thus, they are representative of the original

labeled envelope, and can be used for normalization.

Based on these assumptions, an algorithm was derived

and implemented in R (R development core team, 2010) that

inputs the relative intensity of m/z values for each measured

time point, and calculates how much of the relative intensity

might be caused by peptides that remained unchanged from

a given previous time point. Assuming that no new peptides

will be created with m/z values larger than a certain cut-off,

the cut-off value can be set up manually or determined by

R code. If determined by R code, the cut-off value was found

by minimizing the mean squared error. Any intensity at m/z

values larger than this cut-off must therefore result from

unchanged peptides. To illustrate this algorithm, consider

calculating how much of the relative intensity at 8 h remains

unchanged from 0 h. First, intensities from all peaks with m/z

greater than a given cut-off are summed for both the 0- and

8-h data, and the ratio of the sum from 8 h to the sum from

0 h is calculated. Because of measurement errors at low

intensities, the true relative intensities smaller than mini-

mally measurable values are measured as zero. To avoid

bias, only those m/z values at intensities greater than zero are

used at both time points. The cut-off is set so that any higher

m/z components are uncontaminated by the new signal, and

so the resulting ratio is the proportion of the 0-h intensities

that have remained unchanged at the 8-h time. Then, as it is

assumed that the spectral envelope of the original peptide

degrades at the same rate, the proportions of intensities of

each peak at 0 h are all multiplied by this ratio to obtain the

original labeled protein at 8 h (Pori). The newly synthesized

protein at 8 h (Pnew) is then found by subtracting Pori from

the total intensities. The relative abundance of labeled

peptide (Rt) is equal to Pori/(Pori + Pnew). The same calcula-

tion can be applied to any pair of time points. Figure 11

illustrates this approach.

During the dilution experiments, the calculated half-life of

CAND1 was approximately 30 h, and the half-life of TIR1-

myc was approximately 10 h. These values are quite similar

to those obtained in the incorporation assays. In most cases

the standard deviations were approximately 20% of the

mean values of the different peptide half-lives (Figures 9b,c

and 10b,c; Tables S5 and S6), which was slightly higher than

in the incorporation experiments.

DISCUSSION

Microarray data and design of 2H2O labeling experiments

2H2O stress is not found in natural environments, but is of

concern when 2H2O is utilized for experimental purposes.

The microarray data provides comprehensive information

about the effects of 2H2O labeling. 2H2O resulted in a mod-

erate stress response, especially involving defense genes.

When transferred from 2H2O to normal conditions, signifi-

cantly fewer genes were affected than for the 4-h 2H2O

treatment. This data should be useful in the design of

experiments to minimize 2H2O effects. The microarray data

also shows genes involved in particular signaling or meta-

bolic pathways, where the expression patterns are differ-

entially affected under the treatment conditions. This allows

the evaluation of the suitability of the 2H2O system for use

with a specific response pathway. For example, there are

more than 20 genes responsive to JA, ABA or SA with

altered expression in at least one of the treatments, whereas

fewer than five genes responsive to CK, auxin or GA were

changed.

Currently, a frequently used approach to measure rates of

protein degradation, one measure of half-life under steady-

state conditions, is to use a procedure known as cyclohex-

imide blocking (Zhou, 2004). Typically, plant material treated

with cycloheximide, to block protein synthesis, is analyzed

by immunoblotting, and the change in target protein abun-

dance is determined over time (see, for example, Dreher

et al., 2006). Cycloheximide, however, has significant toxic/

stress effects, as the plant is blocked for synthesis of all

proteins, including those critical to cellular function. Com-

pared with a cycloheximide-blocking experiment, the 2H2O

labeling system provides controlled and documented exper-

imental conditions that should reflect the dynamics of target

protein metabolism.

Differences in amino acid turnover rates

Analysis of amino acid metabolism with 2H2O has been used

for more than 30 years (Mitra et al., 1976). However, few

studies have addressed the turnover rates of amino acid

pools, knowledge essential for an accurate assessment of

Table 1 Half-lives of different amino acids (hours)

Incorporation Dilution

Ala 0.624 0.477Asp 0.68 0.71Glu 1.51 1.55Gly 3.17 2.79His 7.78 8.16Ile 40.53 12.25Leu 21.39 17.03Lys 29.75 39.16Met 6.39 4.29Phe 4.67 5.23Pro 14.81 12.14Ser 1.73 1.31Trp 6.65 7.09Tyr 25.29 10.45Val 4.63 7.38



protein turnover. We found that the 15 amino acids exam-

ined exhibited dramatic differences in turnover. The turn-

over rates of amino acids were consistent with their

placement in primary metabolism. Aspartic acid is the

precursor of isoleucine, methoinine and lysine, glutamic

acid is the precursor of proline and histidine, and serine is

the precursor of glycine. All three of these precursor amino

acids have much shorter half-lives than the amino acids

downstream in their respective metabolic pathway.

Except for Ala, Asp, Glu and Ser, the other amino acids

were difficult to fully label during the experimental time

course employed here, as they exhibited plateau values after

8–16 h. Similarly, two-phase kinetic behavior and plateau

values were observed by others (Huege et al., 2007). There

are three possible explanations for this phenomenon. One is

that the free amino acid pools contain not only newly

synthesized amino acids, but also amino acids produced by

the degradation of macromolecules such as proteins. Thus,

Figure 8. LC-MS/MS analysis of CAND1 and TIR1-myc during incorporation and dilution experiments.

One typical peptide was selected from CAND1 or TIR1-myc. The changed isotope distributions exhibited are shown in the diagram. During the incorporation

experiment, a set of higher m/z peaks emerged and increased over time. During the dilution experiments, the isotopic distributions shifted to lower m/z values as

label was lost from the protein population over time.



during the first rapid kinetic phase, the amino acid turnover

rates are likely to be mainly dependent on the ratio of

synthesis to degradation of amino acids themselves. Later

on, contributions to the free amino acid pools from the

degradation of proteins and other macromolecules results in

the slower second phase, as the pools reach a temporary

quasi-steady state, and the relative isotope abundance

values plateau. A second possible explanation would be

that the plateau results from pools of D-amino acid display-

ing much slower turnover rates. However, in the plants that

have been studied, D-amino acid levels for most of the

amino acids are well below values that would account for

the data observed, and several amino acids showing a

significant plateau have not been found as a D-enantiomer at

all (Bruckner and Westhauser, 2003). A third possible

explanation would be that the deuterium exists in two

forms: one is a pool that quickly exchanges with water, and

the other is a more slowly exchanging pool of deuterium in

other compounds, such as those in biochemical reductions.

If the two-deuterium pools concept were the dominant

explanation, one would predict that the plateau value

among the different amino acids would be relatively con-

sistent. As the plateau values were found to be amino acid

dependent and the D-amino acid pools seem too low to

account for the observed results, our working hypothesis is

that the first reason, amino acid recycling, is the primary

effect. In addition, an important advantage of 2H2O labeling

shown in this work is that, unlike other whole-plant labeling

strategies, it can measure half-lives of biological compounds

with relatively rapid turnover rates. For example, the calcu-

lated apparent half-life of alanine obtained from a 15N

labeling system was 28.08 h, which is approximately 50

times slower than that obtained with 2H2O (Table S3).

The 2H2O labeling system provides a system to study

individual protein turnover dynamics in plants

The determination of the turnover rate of a particular protein

provides important information about the function of spe-

cific regulatory pathways active during plant development,

and those that regulate responses to environmental signals.

Thus, our goal was to investigate the feasibility of 2H2O

labeling to measure turnover rates not only of single

CAND1 incorporation chase CAND1 dilution chase Average half livesfrom different experiments

(a) (b) (c)

Figure 9. CAND1 turnover rates.

(a) Label ratio of different peptides from CAND1 during the incorporation experiment.

(b) Label ratio of different peptides from CAND1 during the dilution experiment.

(c) Average half-lives in the different experiments. I, incorporation experiments; D, dilution experiments. Error bars show standard deviations.

TIR1-myc incorporation chase TIR1-myc dilution chase Average half livesfrom different replications

(a) (b) (c)

Figure 10. TIR1-myc turnover rates.

(a) Label ratio of different peptides from TIR1-myc during the incorporation experiment.

(b) Label ratio of different peptides from TIR1-myc during the dilution experiment.

(c) Average half-lives in the different experiments. I, incorporation experiments; D, dilution experiments. Error bars show standard deviations.



proteins, but as a method that might be applicable to a more

global analysis of protein dynamics. During incorporation

experiments the mass isotopic distribution at the 4- and 8-h

time points were well resolved, and both CAND1 and TIR1-

myc exhibited better consistency among different peptides

and replicates than was found with dilution experiments.

This suggested an experimental design employing 2H2O

incorporation, which is practical and easy to perform.

However, because the microarray data indicated that more

than 700 genes were altered during incorporation, it is

important to weigh precision against minimizing biological

perturbations when selecting an experimental design. A

future improvement would be to develop an algorithm

to combine the measured amino acid turnover rates and

protein turnover rates to reduce the measured variation.

Spectral data obtained with the dilution experiments did

not fit well with classical binomial distribution models. This

may be because of the assumption implicate in the binomial

distribution model that every position with a hydrogen atom

has the same likelihood of being replaced by deuterium.

However, the analysis of the different amino acids showed

that individual amino acids exhibited different turnover

rates, and thus hydrogen atoms within amino acids with

slow turnover rates have a lower probability of being

replaced by deuterium than hydrogen atoms from amino

acids with faster turnover rates.

A number of different implementations of 15N-labeling

systems and stable isotope labeling using amino acid (such

as SILAC) systems have been extensively used in animals,

with single-celled organisms as well as cultured cells from

more complex organisms. These two labeling systems have

found utility in studies with plants (Gruhler et al., 2005;

Engelsberger et al., 2006; Schaff et al., 2008); however, they

have focused, in general, on the proteomics of relative

quantification, comparing protein abundance under differ-

ent conditions. Careful control analyses of the protein or

gene expression changes related solely to the experimental

strategy were often not presented. However, in seedling

experiments applied to the measurements of the dynamics

of protein turnover, the analytical and biological needs

warrant these additional considerations. Many questions

about seedling functional biology cannot be answered using

cell culture, tissue pieces or with labeled compounds that

differentially accumulate in specific cells or tissues (Gruhler

et al., 2005; Schaff et al., 2007). In addition, because seed-

lings contain stored nutrients, and organic nitrogen is

recycled in plant cells, measurable changes of mass isotopic

distribution using either labeled amino acids or 15N-salt

labeling strategies can require significant labeling times,

from several days to several weeks, making the approach

biologically imprecise when applied to studies of rapidly

growing seedlings. For amino acid labeling of cell culture,

70–80% labeling using leucine or arginine could only be

achieved after 8 days (Gruhler et al., 2005), probably

Figure 11. Graphical representation of the algorithm output showing sepa-

ration of the newly synthesized (dark gray) and the older original part (light

gray) of the peptide pool.

‘C’ indicates the cut-off position in the m/z window.



because of endogenous amino acid synthesis. Because

exogenous amino acids can feedback-inhibit endogenous

amino acid synthesis, and developmental and environmen-

tal influences may change over time, significant and variable

bias can be introduced into the calculation of the ratio of

labeled or unlabeled proteins. Some studies have suggested

using a lower concentration of 2H2O to decrease the effect of

treatment on the growth of the seedlings. However, low-

level treatments have a similar problem with equilibration

time, with about 8 days required for the mass isotopic

distribution of newly synthesized peptide to become suffi-

ciently different from the distribution in the unlabeled

peptide (Xiao et al., 2008). The longer labeling periods of

these methods reduce the ability to capture more rapid

events of metabolic flux of both amino acids and proteins.

As we have demonstrated with 30% 2H2O labeling, the

higher concentration and the abundance of hydrogen atoms

in peptides allow newly synthesized peptides to be resolved

in 4 h (Figure S1).

Dynamics of CAND1 and TIR1 proteins

Whereas a number of studies have focused on the rate of

turnover of the targets of the Skp1-Cullin-F-box protein

(SCFTIR1) complex, the AUX/IAA proteins (Gray et al., 2001),

there has been less attention paid to the component proteins

of the degrading machinery itself. However, the stability of

SCF complex subunits is known to be under cellular control.

In animals and fungi, the stability of F-box proteins and other

SCF subunits are subject to regulation by the COP9 signalo-

some (Wu et al., 2005; Cope and DeShaies, 2006; He et al.,

2009). Our development of a 2H2O labeling system with

Arabidopsis seedlings and the determination of a baseline

turnover rate for TIR1 in wild-type plants will enable the

testing of this hypothesis in plants. Similarly, CAND1 has

been suggested to regulate F-box protein stability by con-

trolling SCF complex assembly (Zheng et al., 2002; Feng

et al., 2004; Zhang et al., 2008; Schmidt et al., 2009). Clearly,

studies to sort out the roles of individual components of

complex protein assemblies can benefit from techniques that

allow the actual rates of turnover to be determined with

confidence. Results presented here establish baseline rates

of turnover of two key components, with CAND1 showing a

turnover time over three times longer (approximately 34 h)

than that of TIR1 (approximately 10 h), and these rates will be

important in future studies where additional components of

the system are analyzed in wild-type and mutant seedlings.

This work expands on previous studies by providing a

method for the measurement of protein turnover in plants

using 2H2O labeling, backed by microarray evaluation of its

feasibility for specific experimental systems. This study

provides insight into the potential utility for high-throughput

analysis of large numbers of proteins, as well as describes

important potential limitations of utilizing 2H2O labeling as a

tool for investigating protein turnover. Based on this study,

2H2O labeling can be optimized for more extensive applica-

tions in order to provide robust information on the actual

protein turnover rates of plant proteins under specific

environmental conditions, different developmental stages,

and with mutant or other altered genetic backgrounds.

EXPERIMENTAL PROCEDURES

Plant materials, treatment and growth conditions

The Arabidopsis thaliana Columbia ecotype was used, seedlingswere germinated on ATS media (Lincoln et al., 1990) with or without30% 2H2O (Cambridge Isotope Laboratories, http://www.iso-tope.com), and were grown under continuous light at 22 � 2�C.Seven-day-old seedlings were transferred onto fresh ATS mediawith or without 30% 2H2O. For the analysis of amino acid turnoverrates, seedlings were collected at 0, 1, 2, 4, 6, 8, 16, 24, 48, 72 and96 h. For microarray analyses, seedlings were collected at 0 and 4 h.To construct the PCAND1:CAND1-HA-StrepII binary vector, bridgingPCR was used to introduce the HA and StrepII tags and nopalinesynthase (NOS) terminator sequences from pXCS-HA-Strep (Witteet al., 2004) in frame with the last codon of CAND1. The resultingapproximately 10-kb fragment, including 2.65 kb of CAND1 pro-moter sequence, was cloned as an XbaI–KpnI fragment into pBIN19(Bevan, 1984), and introduced into Arabidopsis plants by Agrobac-terium-mediated transformation. The PCAND1:CAND1-HA-StrepIIfusion gene complemented the eta2-1 mutant phenotype(W. Zhang, unpublished data). For CAND1 protein purification,seedlings of PCAND1:CAND1-HA-Strep transgenic lines were col-lected at 0, 8, 24, 48 and 72 h. For TIR1-myc protein purification,30 lM dexamethasone was provided 24 h before transfer and dur-ing the following experimental period (Gray et al., 1999). Seedlingsof TIR1-myc transgenic lines were collected at 0, 4, 8, 24, 48 and72 h. In experiments where 15N-labeling was employed rather than30% 2H2O, ATS media was prepared with 98.5 atom% 15N-labeledsalts, K15NO3 and Ca(15NO3)2 (Cambridge Isotope Laboratories) inplace of the normal unlabeled salts. Seedlings were collected at thesame time points as used for the 2H2O labeling experiments, and allother conditions were the same.

Amino acid extraction, derivatization and GC-MS analysis

Free soluble amino acids were extracted using 0.01 M HCl, and thenpurified by solid-phase extraction using strong cation exchangecolumns (Extract-Clean; Grace/Alltech, http://www.discovery-sciences.com). Amino acids were eluted by 2 ml 4 M NH4OH andlyophilized (Persson and Nasholm, 2001; WP Chen, unpublished).

Dried free amino acid extracts of seedlings were redissolved in asolution of 200 ll methanol and 200 ll 1% (w/v) NaOH. A 100-llvolume of amino acid solution was transferred into a silanizedscrew-capped 1-ml vial, 20 ll of pyridine and 20 ll of methylchloroformate (MCF) were added sequentially, and the mixture wasshaken for 1 min. The MCF derivatives were extracted by adding200 ll of chloroform and 200 ll of 50 mM Na2CO3 solution, andvortexing for 30 s. After phase separation, the chloroform phasewas transferred into a new silanized screw-capped 1-ml vial, andresidual water was removed by adding solid anhydrous Na2SO4

with a 30-min incubation. After dehydration, 2 ll of the chloroformphase was injected onto the GC-MS system in splitless mode.

All GC-MS analyses were performed using a Hewlett-Pack-ard 5890 (GC)/5970 (MS) system in electron impact (EI) mode at70 eV. The GC was equipped with a fused silica capillary column(HP-5MS, 30 m · 25 mm inner diameter, 0.25-mm film thickness;Agilent, http://www.chem.agilent.com). The oven temperature wasinitially held at 70�C for 3 min. Thereafter the temperature was



raised at 25�C min)1 until 280�C, and this temperature was held for5 min. Helium was used as the carrier gas, and was delivered at aconstant flow rate of 1 ml min)1. The injection temperature was setat 240�C and the interface temperature was set at 290�C. Theanalyses of the MCF-derivatized amino acids were obtained in thefull scan mode (Zampolli et al., 2007; WP Chen, unpublished).

Calculations

For specific amino acids the theoreticalmass isotopomer distributionwas calculated according to a binomial distribution, corrected by themeasured natural abundance. The details of mass isotopomer dis-tribution analysis (MIDA) were based on the review previouslydescribed by Hellerstein and Neese (1999), with minor modifications.

GC-MS data were used to determine the relative proportions ofthe mass isotopomers: Mi (i from 0 to n). Isotope incorporation intoMi was calculated by subtracting the corresponding fractionalabundances for unlabeled standards. The weighted isotope incor-poration into Mi was calculated by subtracting the correspondingfractional abundances for unlabeled standards, and the result wasmultiplied by i. An algorithm for calculating the deuterium relativeisotope abundance (Rt) is as follows:

Rt ¼

Pn

i¼1

ðEMi � SMi Þ � i

Pn

i¼1

ðTMi � SMi Þ � i

ð1Þ

where n is the number of hydrogen atoms that can be labeled in aspecific amino acid, EMi is the experimentally determined relativeproportion of the mass isotopomers within the sample, SMi is therelative proportion of mass isotopomers in the standards, and TMi isthe theoretical relative proportions of the mass isotopomers.

For amino acids and proteins in incorporation experiments,turnover rates were estimated by nonlinear curve fitting of the plotsof Rt against time, assuming a single exponential rise to thetheoretical value:

Rt ¼ 1� e�kt ð2ÞIn dilution experiments, turnover rates were estimated by nonlinearcurve fitting of the plots of the value of Rt against time, assuming asingle exponential decay:

Rt ¼ R0e�kt ð3Þ

RNA extraction and microarray

Seedlings were frozen in liquid N2 and total RNA was extractedusing the RNeasy plant kit (Qiagen, http://www.qiagen.com), fol-lowing the manufacturer’s instructions. For each control and treat-ment, three independent biological samples were used for RNAisolation and microarray hybridization. Total RNA samples werekept at )80�C and sent to the Microarray Facility of the BioMedicalGenomics Center (University of Minnesota), where synthesis andlabeling of cRNA as well as the hybridization to the ATH121501 chip(Affymetrix, http://www.affymetrix.com) and analysis scans wereperformed.

Microarray data analysis

Microarray data analyses were performed with R (http://www.r-project.org), dCHIP (http://biosun1.harvard.edu/complab/dchip)(Li and Wong, 2001) and TIGR MeV (http://www.tm4.org/mev.html)software. The original microarray data underwent backgroundcorrection, normalization, and log2 transformation using the RMAmethod (Irizarry et al., 2003a,b). The log-transformed data were

used for further analyses. To identify differentially expressed genes,P < 0.05 was used as the cut-off, and a twofold change thresholdwas used between control and 2H2O treatment. For hierarchicalclustering, a Pearson un-centered correlation distance and averagelinkages were used. GO annotation (http://www.arabidopsis.org/tools/bulk/go/index.jsp) was used for gene functional classification(Berardini et al., 2004). For metabolism pathway analyses, we per-formed further classification following the procedures at the AraCycwebsite (Mueller et al., 2003; http://www.arabidopsis.org:1555/ARA/expression.html).

Protein purification

For CAND1 purification, 7-day-old seedlings were homogenized inan extraction buffer of 100 mM Tris–HCl (pH 8.0), 5 mM EGTA, 5 mM

EDTA, 150 mM NaCl, 10 mM DTT, 1 mM phenylmethylsulfonylfluoride (PMSF) (Witte et al., 2004), 0.5 · protease inhibitor cocktail(Pierce, http://www.piercenet.com), 0.5% Triton X-100 and10 lg ml)1 avidin. Homogenates were centrifuged at 17 000 g for15 min at 4�C, and supernatants were applied to strep-II columns(IBA GmbH, http://www.iba-go.com) following the manufacturer’sinstructions.

For TIR1-myc purification, we first expressed AXR2 domain II as a6 x His fusion protein in Escherichia coli, and purified the expressedprotein on Ni beads to make the affinity column. TIR1-myctransgenic seedlings (Gray et al., 1999) between 7- and 9-days-oldwere homogenized in extraction buffer containing 50 mM Tris–HCl(pH 7.5), 1 M EDTA, 150 mM NaCl, 1 mM DTT, 1 mM PMSF,0.5 · protease inhibitor cocktail, 0.5% Triton X-100 and 10% glyc-erol. Homogenates were centrifuged at 17 000 g for 15 min at 4�C,and supernatants were applied to affinity columns and eluted byextraction buffer containing 6 M urea.

Protein digestion and LC-MS/MS analysis

Protein bands of interest were excised manually after visualizationwith Deep Purple Total Protein Stain (GE Healthcare, http://www.gehealthcare.com). Excised bands were trypsin digested(Rosenfeld et al., 1992; Hellman et al., 1995) on the ProPrepTM

System (Digilab, Inc., http://www.digilabglobal.com/). Briefly, pro-tein bands were subjected to two rounds of dehydration andhydration by the addition, incubation and removal of acetonitrile,followed by the addition, incubation and removal of 25 mM

NH4HCO3. Gel plugs were then subjected to reduction in the pres-ence of 10 mM DTT/25 mM NH4HCO3 at 56�C for 30 min. The DTTsolution was aspirated and a 55 mM iodacetamide/25 mM NH4HCO3

solution was added for 30 min at room temperature. The iodace-tamide solution was aspirated, followed by two series of dehydra-tion and hydration steps, as described above. Protein bands werethen subjected to tryptic digestion with 12 ng ll)1 trypsin (Sigma-Aldrich, http://www.sigmaaldrich.com) in 25 mM NH4HCO3, 5 mM

CaCl2 at 37�C for 10 h. The reaction was stopped with the addition offormic acid to a final concentration of 0.1%. Sample digests weremanually aspirated and dispensed into 1.5 ml microcentrifugetubes, with subsequent extraction by addition, incubation and re-moval to respective tubes of 70% acetonitrile and 0.1% formic acid.Extracts were dried (SC210A SpeedVac� Plus; Thermo Scientific,http://www.thermoscientific.com) and resuspended in LC-MS/MSloading buffer (98% H2O, 2% acetonitrile and 0.1% formic acid).

The chromatography system (LC Packings/Dionex, http://www.dionex.com) was online with a QSTAR Pulsar i quadrupoletime-of-flight (TOF) MS instrument (Applied Biosystems, http://www.appliedbiosystems.com), which was equipped with Protana’s(MDS Proteomics Inc., http://www.mdsinc.com/) nanoelectrospraysource (Kapphahn et al., 2003). Peptides were eluted with a linear



gradient from 0 to 35% B (0.1% formic acid in solution of 95%acetonitrile and 5% water) over 45 min, followed by 35–80% B over2 min, and held isocratic at 80% B for 10 min. Solvent A was 0.1%formic acid in a solution of 95% water and 5% acetonitrile. Production spectra were collected in an information-dependent acquisition(IDA) mode, using continuous cycles of one full scan TOF MS from400 to 1200 m/z (1 s) plus four product ion scans from 50 to 2000 m/z(2 s each). Precursor m/z values were selected starting with the mostintense ion, using a selection quadrupole resolution of 3 Da. Therolling collision energy feature was used, which determines colli-sion energy based on precursor m/z and charge state. The dynamicexclusion time for precursor ion m/z values was 60 s.

Peptides with monoisotopic peaks were identified using Protein-Pilot (Applied Biosystems). For data obtained from 48- or 72-h timepoints, peptides without monoisotopic peaks were identified by theretention time and the similarity of their mass isotopic envelopebetween the 48- or 72-h samples and the early time points.

Accession codes

NCBI Gene Expression Omnibus (GEO): microarray data have beensubmitted under accession number GSE18153.

ACKNOWLEDGEMENTS

This work was funded by NSF Plant Genome Research Programgrant DBI-0606666. Protein mass spectrometry was conducted at theCenter for Mass Spectrometry and Proteomics, and we thank Lee-Ann Higgins, Bruce Witthuhn, Todd Markowski, Thomas MacGo-wan and Thomas Krick for their help with obtaining the raw proteinLC-MS/MS data. We would also like to thank Wenjing Zhang forgenerating the PCAND1:CAND1-HA-StrepII transgenic plants used forthe analysis of CAND1 turnover rates.

SUPPORTING INFORMATION

Additional Supporting Information may be found in the onlineversion of this article:Figure S1. Mass spectrum of tryptic peptide (LNVEVIDER) of TIR1-myc protein identified in LC-MS/MS analysis.Table S1. Number of genes responding to growth on deuteriumoxide that are also related to other stress conditions.Table S2. Changes in levels of expression (as fold changes) inresponse to growth on deuterium oxide supplemented media.Table S3. Half-lives of different amino acids as determined using15N and 2H2O labeling systems.Table S4. Calculated half-lives of peptides derived from the CAND1protein after deuterium oxide labeling using either isotopic incor-poration or dilution methods for analysis.Table S5. Calculated half-lives of peptides derived from the TIR1-myc protein after deuterium oxide labeling using either isotopicincorporation or dilution methods for analysis.Table S6. Half-lives of peptides from other proteins that co-purifiedwith the TIR1-myc protein, as determined after deuterium oxidelabeling.Please note: As a service to our authors and readers, this journalprovides supporting information supplied by the authors. Suchmaterials are peer-reviewed and may be re-organized for onlinedelivery, but are not copy-edited or typeset. Technical supportissues arising from supporting information (other than missingfiles) should be addressed to the authors.

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Measuring the turnover rates of Arabidopsis proteins using