Variation in Growth Rate between Arabidopsis Ecotypes
Is Correlated with Cell Division and A-Type
Cyclin-Dependent Kinase Activity1
Gerrit T.S. Beemster, Kristof De Vusser2, Evelien De Tavernier, Kirsten De Bock, and Dirk Inzé*
Department of Plant Genetics, University of Gent/Flanders Institute of Biotechnology, B–9000 Gent, Belgium
We used a kinematic analysis to investigate the growth processes responsible for variation in primary root growth between
18 ecotypes of Arabidopsis. Root elongation rate differed 4-fold between the slowest (Landsberg erecta, 71 ␮m h⫺1) and
fastest growing line (Wassilewskija [Ws]; 338 ␮m h⫺1). This difference was contributed almost equally by variations in
mature cortical cell length (84 ␮m [Landsberg erecta] to 237 ␮m [Ws]) and rate of cell production (0.63 cell h⫺1 [NW108] to
1.83 cell h⫺1 [Ws]). Cell production, in turn, was determined by variation in cell cycle duration (19 h [Tsu] to 48 h [NW108])
and, to a lesser extent, by differences in the number of dividing cells (32 [Weiningen] to 61 [Ws]). We found no correlation
between mature cell size and endoreduplication, refuting the hypothesis that the two are linked. However, there was a
strong correlation between cell production rates and the activity of the cyclin-dependent kinase (CDKA). The level of the
protein could explain 32% of the variation in CDKA. Therefore, it is likely that regulators of CDKA, such as cyclins and
inhibitors, are also involved. These data provide a functional link between cell cycle regulation and whole-plant growth rate
as affected by genetic differences.
The rate at which plants grow is an important
agronomic trait in cultivated plants, as well as an
adaptive trait under natural growth conditions.
Therefore, the physiological characteristics associated with fast and slow growth have been extensively
investigated (for an overview, see Lambers et al.,
1998). They involve the acquisition of growthsupporting substances (photosynthesis and nutrient
uptake) and their utilization (anatomy, chemical
composition, cell division, and cell expansion). In
contrast to the multitude of physiological investigations into the basis of growth rate differences, genetic
studies are scarce.
Arabidopsis, the model plant for genetic research,
has been collected from a wide range of habitats
distributed primarily over most of the northern
hemisphere. Genetic differences between local populations (commonly called ecotypes, despite that this
term does not conform strictly to its ecological definition [Pigliucci, 1998]) are presumably associated
with adaptation to the prevailing environmental conditions. Numerous ecotypes were collected by the
1
This work was supported by grants from the European Union
(no. QLG2–CT–1999 – 00454 and no. QLK5–CT–2001) and the Interuniversity Poles of Attraction Programme (Belgian State, Prime
Minister’s Office-Federal Office for Scientific, Technical and Cultural Affairs; no. P5/2).
2
Present address: Department of Molecular Biology, University
of Gent/Flanders Institute of Biotechnology, Ledeganckstraat 35,
9000 Gent, Belgium.
* Corresponding author; e-mail [email protected]; fax
32–9 –264 –5349.
Article, publication date, and citation information can be found
at www.plantphysiol.org/cgi/doi/10.1104/pp.002923.
854
pioneers of Arabidopsis research and have since been
use to investigate a wide range of physiological processes, through comparisons between ecotypes and
by genetic mapping using recombinant inbred lines
(Alonso-Blanco and Koornneef, 2000). With some notable exceptions (Li et al., 1998), little is published
about differences in the rate at which Arabidopsis
ecotypes grow when compared under standardized
laboratory conditions.
There are two complementary views on how root
growth is regulated (Silk, 1984). The spatial model
describes at what rate division and expansion occur
as a function of position along the root axis. Root
elongation rate in this model is determined by the
size of the growth zone and local rates of expansion.
According to this view, cell division merely subdivides cellular space provided by the expansion process. In an alternate manner, a cellular model can be
adopted whereby cell production in the meristem
drives growth by producing the cells that will subsequently expand to reach a given mature size (Fiorani et al., 2000). Data obtained from analyses of
ontogenetic acceleration of root elongation rate
(Beemster and Baskin, 1998), inhibition of root elongation in response to the stp1 mutation, and to externally applied hormones (Beemster and Baskin, 2000)
are easiest explained in terms of a cellular model,
implying a crucial role for cell division in determining organ growth rates. Contradictory to this proposition are observations from ␥-radiated seedlings and
transgenic lines overexpressing cell cycle genes that
show that cell division and organ growth can be
partly uncoupled (Haber, 1962; Hemerly et al., 1995;
De Veylder et al., 2001a). However, evidence from
Plant Physiology,
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Cell Cycle Regulation in Roots of Arabidopsis Ecotypes
transgenic plants overexpressing other cell cycle
genes conclusively shows that modulated cell division directly affects organ growth rates (Doerner et
al., 1996; Cockcroft et al., 2000; De Veylder et al.,
2001b). Based on detailed analyses of such transgenic
lines, as well as wild-type plants responding to environmental stimuli, we are now starting to unravel
the mechanism by which cell division activity in
higher plants organs is controlled and how this is
related to growth.
To study the regulation of plant growth rate in
Arabidopsis, we adapted the existing kinematic
framework for quantifying cell division and expansion in the growth zone of the primary root tip
(Beemster and Baskin, 1998). Root tips and intercalary meristems of monocotyledonous leaves grow
essentially linearly, and growth occurs in a welldefined region. Therefore, they are ideal model systems for investigating the relationship between cell
division and growth rate. The growth of these organs
can be analyzed in terms of a single representative
cell file. In such a file, cell production resulting from
co-occurring division and expansion is restricted to
the apical region bordering the quiescent center (root
tips; Fig. 1) and the basal region bordering the junction with the stem (monocotyledonous leaves). In
these meristems, the production of new cells causes a
flux of cells that increases with distance from the
quiescent center and leaf base (Beemster and Baskin,
1998). At the end of the meristem, cells stop dividing,
but continue to elongate, often at higher rates than in
the meristem (Beemster and Baskin, 1998). Cells will
reach the end of the growth zone, where cell expansion also stops and they have reached their final size.
In such a linear system, adopting the cellular view,
the steady-state rate at which an organ grows (E) is
determined by the final length cells reach when exiting the growth zone (lmat; primarily determined in
Figure 1. Localization of cell division and expansion in the cortical
cell file of primary root tip of Arabidopsis. Cortical cell files originate
from initials directly adjacent to the quiescent center (QC). Basal to
these initials is the meristematic region where new cells are produced by ongoing cell division and cell expansion, resulting in a flux
of cells away from the root tip (arrows). When cells leave the
meristem, they enter the elongation zone. Here, they no longer
divide, but continue to elongate, resulting in a rapid increase in
length as a function of position. Basal to the elongation zone, cells
are of constant size and considered mature. The size of the growth
zone (meristem ⫹ elongation zone) typically ranges between 1 and
2.5 mm in Arabidopsis roots grown on agar media (Beemster and
Baskin, 1998, 2000; De Veylder et al., 2001b).
Plant Physiol. Vol. 129, 2002
the elongation zone) and the number of cells produced
in the meristem per unit of time (P) according to:
E ⫽ lmat ⫻ P
(Eq. 1)
The cell production rate (P), in turn, is a function of
the number of cells in the meristem (Ndiv) and their
average cell cycle duration (Tc):
P ⫽ Ndiv ⫻
ln(2)
Tc
共Eq. 2; Ivanov, 1994)
Thus, we can decompose root elongation rate in three
functional parameters, lmat, Ndiv, and Tc. What could
be the mechanisms that regulate these parameters?
Mature cell size has frequently been correlated with
nDNA content in Arabidopsis (Melaragno et al.,
1993; Kondorosi et al., 2000) and other plants as well
as in animal species (Cavalier-Smith, 1978). The DNA
content of individual cells can be increased above the
basic 2C (G1) and 4C (G2) levels by the process of
endoreduplication, a modified cell cycle where cells
go through S-phase but then bypass mitosis to go
directly into G1. We hypothesize that cell cycle regulation controls the degree of endoreduplication,
which, in turn, determines mature cell size. As mature cell size partly determines organ growth rate
(Eq. 1), this would be a first link between cell cycle
regulation at the molecular level and root elongation
rates. A second link was recently demonstrated in
corn (Zea mays) leaves that respond to changes in
water deficit and temperature. The growth of such
leaves was closely correlated with cell division activity. It is interesting that local rates of cell division
were linearly related to the activity of the cyclindependent kinase (CDKA) (Granier et al., 2000). This
leads to the model where CDKA controls cell production rates and, thereby, the second component
that determines growth rate. The aim of this article is
to explore these putative roles of cell cycle regulation
in mediating genetically determined growth rate differences. We report that variation in mature cortical
cell length, average cell cycle duration, and number
of dividing cells all contribute to differences in the
elongation rate of the primary root between 18
ecotypes of the model plant Arabidopsis. Moreover,
we demonstrate the absence of a correlation between
endoreduplication and mature cell length, while confirming the relationship between CDKA and cell
production.
RESULTS
Variation in Root Elongation Rate
For our analysis we selected 18 ecotypes of Arabidopsis, comprising the most commonly used laboratory strains and some that originated from contrasting growth habitats (Table I). Under our growth
conditions (see “Materials and Methods”), we readily
observed large differences in whole-plant growth
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855
Beemster et al.
Table I. Overview of the ecotypes investigated
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
a
Abbreviation
Ler-0
Cvi-0
C24
Estonia (Est)
RLD1
S96
Dijon-M (Djn)
An-1
Be-0
Col-0
Lz-0
Nd-0
Nw108
Ty-0
Wt-4
Tsu-1
Wei-0
Ws-4
NASC Ecotype
Origina
NW20
N902
N906
N911
N913
N914
N919
N945
N965
N1093
N1355
N1391
unknown
N1573
N1610
N1640
N3110
N5390
Gorsow-Wilekoploski, Poland. Selected from x-ray mutagenised population
Cape Verdi Islands
Laboratory strain of unknown origin
Estonia
Rschew/Starize, Russia. Distinct from Rsch-0
Homozygous line derived from a cross Di (Dijon, France) ⫻ Li-2 (Limburg, Germany)
Dijon, France
Antwerpen, Belgium
Bensheim, Germany
Landsberg/Warthe, Poland (Selected from original Landsberg population)
Lezoux, Puy-de-Dome, France
Niedrezenz, Germany
Laboratory strain of unknown origin
Taynuilt, United Kingdom
Wietze, Germany
Tsu, Japan
Weiningen, Switzerland
Vasljevichi/Wassilewskija, Belarus
According to NASC listing (http://nasc.nott.ac.uk/).
rates between these lines (data not shown). These
differences were reflected by the rate of growth of
their primary root as determined by measurements
of the daily length increments (Table II). In contrast
to the situation on Hoagland medium (Beemster and
Baskin, 1998), the primary root of most ecotypes
grew at a constant rate on 1⫻ Murashige and Skoog.
On this medium, only three lines, which consequently grew fastest on d 9, were found to increase
their elongation rate over time (Ws, Est, and Tsu;
data not shown). On this day, there was over 4-fold
variation between the growth rate of the slowest
growing (Ler, 71 ␮m h⫺1) and the fastest growing
ecotype (Ws, 338 ␮m h⫺1; Table II). Although part of
the difference was associated with the ability of the
fastest lines to accelerate, there was also a nearly
3-fold difference in growth rate between steady-state
growing ecotypes (71 ␮m h⫺1 [Ler] to 201 ␮m h⫺1
[Wt]; Table II).
Relationships between Root Elongation Rate, Cell
Production, and Mature Cell Length
To investigate the relationship between cell cycle
regulation and plant growth rates, we first determined the respective contributions of mature cell size
Table II. Kinematic analysis of root growth rate differences between 18 Arabidopsis ecotypes
Average root elongation rate (E) ⫾ SE (n ⫽ 8 plates, each with 8 –10 seedlings). Mature cortical cell
length (lmat), cell production rate (P), average cell cycle duration (Tc), and the no. of dividing cells per
cortical cell file (Ndiv) ⫾ SE (n ⫽ 5 representative roots).
Ecotype
E
␮m h
Ler
Cvi
C24
RLD
NW
S96
Djn
Wei
Nd
Be
An
Lz
Col
Ty
Wt
Tsu
Est
Ws
856
lmat
⫺1
71 ⫾ 10
92 ⫾ 14
99 ⫾ 8
112 ⫾ 9
118 ⫾ 15
135 ⫾ 16
138 ⫾ 5
139 ⫾ 6
155 ⫾ 12
160 ⫾ 8
161 ⫾ 8
176 ⫾ 8
182 ⫾ 11
184 ⫾ 8
201 ⫾ 14
207 ⫾ 20
281 ⫾ 8
338 ⫾ 18
␮m
84 ⫾ 6
87 ⫾ 1
94 ⫾ 2
170 ⫾ 5
186 ⫾ 3
167 ⫾ 7
121 ⫾ 5
169 ⫾ 3
189 ⫾ 3
140 ⫾ 6
180 ⫾ 9
184 ⫾ 11
171 ⫾ 12
172 ⫾ 2
189 ⫾ 8
145 ⫾ 6
203 ⫾ 15
237 ⫾ 13
P
cells h
Tc
⫺1
0.86 ⫾ 0.05
1.05 ⫾ 0.01
1.06 ⫾ 0.02
0.66 ⫾ 0.02
0.63 ⫾ 0.01
0.83 ⫾ 0.04
1.15 ⫾ 0.04
0.83 ⫾ 0.02
0.83 ⫾ 0.01
1.15 ⫾ 0.05
0.90 ⫾ 0.04
0.97 ⫾ 0.05
1.09 ⫾ 0.09
1.07 ⫾ 0.01
1.07 ⫾ 0.04
1.55 ⫾ 0.06
1.45 ⫾ 0.08
1.83 ⫾ 0.09
Ndiv
h
36 ⫾ 4
25 ⫾ 1
25 ⫾ 1
36 ⫾ 2
48 ⫾ 0
35 ⫾ 1
27 ⫾ 3
27 ⫾ 2
38 ⫾ 1
30 ⫾ 5
33 ⫾ 2
36 ⫾ 5
29 ⫾ 3
30 ⫾ 2
33 ⫾ 3
19 ⫾ 1
26 ⫾ 4
24 ⫾ 2
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44 ⫾ 3
39 ⫾ 1
38 ⫾ 1
34 ⫾ 1
44 ⫾ 1
42 ⫾ 1
45 ⫾ 3
32 ⫾ 2
46 ⫾ 1
49 ⫾ 6
43 ⫾ 4
49 ⫾ 3
43 ⫾ 2
46 ⫾ 3
50 ⫾ 3
42 ⫾ 0
55 ⫾ 3
61 ⫾ 1
Plant Physiol. Vol. 129, 2002
Cell Cycle Regulation in Roots of Arabidopsis Ecotypes
and cell production, focusing on the cortical cell files.
Under steady-state growth, root elongation rate is the
product of cell production and mature cell size (see
Eq. 1). Under conditions where the root accelerates, a
small fraction of the cell production is “invested” in
the expansion of the meristem (Beemster and Baskin,
1998). As mentioned, this was the case for the three
fastest growing ecotypes, Ws, Est, and Tsu. To correct
for this phenomenon, we calculated the rate of
change of the number of cells in the region that
represents the meristem on d 9. This factor was calculated from the cell length data from d 7 and 11 and
was included in the total cell production (for a detailed description of this procedure, see Beemster and
Baskin, 2000). From the thus obtained cell production
rate and the number of dividing cells, the cell cycle
duration, Tc, was calculated (Eq. 2).
To investigate the correlation between growth parameters, the data were log transformed and used for
multiple linear regression analysis (see “Materials
and Methods”). The data showed that the regression
model containing cell production rate and mature
cell length accounted for 99% of the variance in root
elongation rate (Fig. 2A; R2 value in Table III). Both
parameters contributed, given that both partial regression coefficients were significantly different from
0 (C1 and C2 in Table III). The data for the three
non-steady (and fastest) growing ecotypes slightly
deviated from the linear relationship (Fig. 2A). This is
due to the extra cells produced for the expansion of
the meristem, and causes the R2 to be 0.99 instead of
1.00. When analyzed separately, the model with cell
production (ranging from 0.63 [Nw] to 1.83 cells h⫺1
[Ws]; Table II) accounted for 47% of the variance in
root elongation rate, whereas the model with mature
cortical cell length (ranging from 84 [Ler] to 237 ␮m
[Ws-3]; Table II) could explain 62% of variation in
growth rate (R2 in Table III). For both explanatory
variables, the correlation with root elongation rate
was highly significant (Fig. 2, B and C; P values in
Table III). Hence, variation in mature cell size accounted for a little over one-half and, consequently,
cell production for a little less than one-half of the
variation in root elongation rate. There was no significant correlation between cell production and mature cell size, implying that cell growth in the elongation zone is independent from cell production in
the meristem.
Relationship between Cell Production, Number of
Dividing Cells, and Cell Cycle Duration
The number of dividing cells in each file and average cell cycle duration determine cell production (Eq.
2). After linearizing this relationship by log transformation (Eq. 4), it was possible to further analyze the
role of these two cell division parameters in cell
production by means of multiple linear regression
analysis. Given that the number of dividing cells and
Plant Physiol. Vol. 129, 2002
Figure 2. Variation in root elongation rate between 18 Arabidopsis
ecotypes is correlated with cell production and mature cortical cell
length. A, The complete model describing the correlation between
root elongation rate (E), cell production rate (P), and mature cell
length (lmat; Eq. 3). B, Partial model of the relationship between root
elongation rate (E) and cell production (P). C, Partial model of the
relationship between root elongation rate (E) and mature cortical cell
length (lmat). Details of the regression parameters are listed in Table III.
the average cell cycle duration are the only two parameters that determine overall cell production, the
regression model including them both accounted for
all variation in cell production (Fig. 3A; R2 of 1.00 in
Table IV). The model with the number of dividing
cells (Ndiv, ranging from 32 [Wei] to 61 [Ws]; Table II)
individually accounted for 37% of the variance in cell
production (Table IV). The model with average cell
cycle duration (Tc, ranging from 18.6 h [Tsu] to 47.7 h
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857
Beemster et al.
Table III. Multiple linear regression analysis of the relationship between root elongation rate (E) and
cell production (P) and mature cell length (lmat)
Model
Log (E) ⫽ C0 ⫹ C1 log (P) ⫹ C2 log (lmat)
Log (E) ⫽ C0 ⫹ C1 log (P)
Log (E) ⫽ C0 ⫹ C2 log (lmat)
a
C 0a
C 1a
C 2a
0.99
0.47
0.62
0.12N.S.
2.18***
0.0N.S.
0.85***
1.0**
–
0.94***
–
1.0***
Significance: * 0.01 ⬍ P ⬍ 0.05; ** 0.001 ⬍ P ⬍ 0.01; *** P ⬍ 0.001; not significant (N.S.).
[Nw]; Table II) explained 66% of the variation in cell
production (Table IV). The correlation was highly
significant for both models (Fig. 3, B and C; P values
in Table IV), implying that both parameters play a
role in determining differences in cell production
and, thus, in root elongation rates. There was no
correlation between average cell cycle duration and
the number of dividing cells, implying that these two
cell production parameters are independently regulated (data not shown).
Relationship between Mature Cell Size and
DNA Content
To test if the observed variation in mature cortical
cell size was correlated with nDNA content, we performed flow cytometry on the roots of all ecotypes.
For this analysis, nuclei were isolated from whole
root systems, which predominantly consist of mature
tissue. Flow diagrams typically contain peaks corresponding to 2C, 4C, 8C, and, in some cases, 16C DNA
content, whereby 2C DNA content corresponds to
cells in G1. The 4C population encompasses “normal” cells in G2 and cells that have gone through a
single round of endoreduplication and are in G1.
Therefore, only the 8C and 16C populations contain
cells that must have all undergone endoreduplication. The fraction of cells in these two populations
varied from only 1% [Nd] to nearly 40% [Nw]; Fig. 4),
indicating a considerable variation in the degree of
endoreduplication. However, in contrast to the hypothesis, we did not find a correlation between
nDNA content and mature cortical cell length (Fig. 4A),
indicating that these parameters were independent.
Relationships between CDKA, Cell Division, and
Endoreduplication
The current model for the molecular regulation of
cell division involves the regulation of CDKA (Mironov et al., 1999). In corn leaves, the rate of cell
division was tightly correlated with the spatial distribution of CDKA histone H1 kinase activity
(Granier et al., 2000). The Arabidopsis root apical
meristem is too small (500 and 700 ␮m for Col and
C24, respectively; Beemster and Baskin, 1998; De
Veylder et al., 2001b) to enable sampling of subsections that would be required to resolve the spatial
distribution of kinase activity. For this reason, we
harvested the apical 5 mm of 250 9-d-old primary
858
R2
root tips of each ecotype. This section includes
the entire apical meristem, the elongation zone (approximately 2 mm; Beemster and Baskin, 1998; De
Veylder et al., 2001b), and a section of mature tissue.
However, it did not comprise lateral root meristems
that contain dividing cells not contributing to the
elongation of the primary root. For 11 ecotypes,
CDKA was determined in two independent experiments, and we found a 5-fold variation (Fig. 5). As
our samples contained the entire root apical meristem, it is appropriate to correlate kinase activity with
the total cell production over the whole of the meristem, effectively integrating cell cycle duration and
number of dividing cells (Eq. 2). Using this approach,
we found a significant positive correlation between
CDKA and cell production rate that accounted for
56% of the observed variation (Fig. 5A). It must be
noted that this correlation is only clear for the three
ecotypes with the highest cell production (Ws, Tsu,
and Djn) relative to a large group of ecotypes with a
lower cell production rate. The lack of correlation
between cell production and kinase activity within
this latter group of ecotypes illustrates the sensitivity
limits of the kinase essay in these small samples. In
contrast, no correlation was found between endoreduplication and kinase activity (Fig. 4B). To test
if CDKA levels could explain variation in kinase
activity, we conducted a western-blot analysis. A
positive correlation between CDKA protein concentration and kinase activity was found (Fig. 5B), albeit that it was not highly significant (P ⫽ 0.089) and
only explained 32% of the observed variation in
kinase activity, suggesting that additional regulatory mechanisms play a role.
DISCUSSION
Our results highlight the large inherent variation in
growth between Arabidopsis ecotypes. As a first step
resolving the relationship between cell cycle regulation and growth, we investigated the cellular basis of
the observed growth rate differences, using the primary root as a model system. The data presented
show that the differences are not attributable to a
single growth parameter. Rather, variation in cell
production and mature cell length contribute
roughly equally to the total variation of root elongation rate. The rate of cell production, in turn, was
primarily correlated with the average cell cycle duration, although the number of dividing cells also
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Plant Physiol. Vol. 129, 2002
Cell Cycle Regulation in Roots of Arabidopsis Ecotypes
processes might compete for limited resources. Second, under the spatial hypothesis, the process of cell
expansion is independent upon cell division (Fiorani
et al., 2000). Therefore, increasing cell division activity would not affect overall growth, but instead lead
to smaller cells being produced. It could have been
that differences in “general vigor” genes would affect
both processes in a similar way. However, the observed independence implies that under the conditions used the two processes do not seem to compete
for limited resources. Given that we analyzed genetic
differences, these results also mean that separate
genes probably regulate individual growth parameters. This result agrees with the observations that
individual growth parameters are differentially affected by auxin, cytokinin, and the stp1 mutation
(Beemster and Baskin, 2000) and by overexpression
of the cell cycle gene CKS1 (De Veylder et al., 2001b).
Independence of cell production and mature cell
length shows that high cell production does not result in a smaller cell size. This would be predicted
from a spatial model where growth results from expansion and cell division merely subdivides existing
cellular compartments (Green, 1976). Therefore, the
cellular model that includes cell division as a driving
process is consistent with our observations. This validates the use of Equations 1 and 2 as a basis for the
kinematic analysis performed here.
Kinematic Methodology
Figure 3. Variation in the rate of cell production in the primary root
meristem of 18 Arabidopsis ecotypes is correlated with the number of
dividing cells and their average cell cycle duration. A, The complete
model describing the correlation between cell production (P) in the
meristem, the number of dividing cells (Ndiv), and mature cell length
(lmat; Eq. 4). B, Partial model of the relationship between cell production rate (P) and number of dividing cells (Ndiv). C, Partial model
of the relationship cell production rate (P) and mature cell length
(lmat). Details of the regression parameters are listed in Table IV.
contributed. It is significant that we could not discover any correlation between variations in mature
cell length, cell cycle duration, and the number of
dividing cells, which suggests that they are all independent. For several reasons, correlation between cell
production in the meristem and cell expansion in the
elongation zone could be expected. First, the two
Plant Physiol. Vol. 129, 2002
To enable multiple linear regression analysis for
estimation of the relative contribution of various
growth parameters to variation in root elongation
rate between ecotypes, a sufficient number of lines
needed to be analyzed. We arbitrarily chose 18,
which proved to be sufficient. It is unfortunate that
the kinematic method developed earlier for Arabidopsis roots is rather laborious, as it entails timelapse and cell length measurements on each replicate
root (Beemster and Baskin, 1998). The amount of time
required for this analysis probably explains why only
a limited number of treatments have been analyzed
to date. Automation may ultimately help to resolve
this bottleneck. However, to reduce the amount of
work involved with the manual analysis, we decided
to omit the time-lapse analysis and base the calculations on cell length profile and overall root elongation rate only, analogous to Baskin et al. (1995). Crucial for this approach is the determination of the
basal margin of the meristem. We defined it as the
position where cortical cells reach a length of 40 ␮m,
a value representative for earlier experiments (Beemster and Baskin, 1998, 2000; De Veylder et al., 2001b).
How sensitive are the results for fallacies of this
assumption? Published data for the Col ecotype
(Beemster and Baskin, 1998) show that the cell length
of 40 ␮m occurs in a region where cell length changes
rapidly over a relatively small distance. Based on
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859
Beemster et al.
Table IV. Multiple linear regression analysis of the relationship between cell production rate (P) and
number of dividing cells (Ndiv) and average cell cycle (Tc)
Model
Log (P) ⫽ C0 ⫹ C1 log (Ndiv) ⫹ C2 log (Tc)
Log (P) ⫽ C0 ⫹ C1 log (Ndiv)
Log (P) ⫽ C0 ⫹ C2 log (Tc)
a
C 0a
C 1a
C 2a
1.00
0.37
0.66
⫺0.19***
⫺1.8**
1.5***
1.02***
1.1**
–
⫺0.99***
–
⫺1.0***
Significance: * 0.01 ⬍ P ⬍ 0.05; ** 0.001 ⬍ P ⬍ 0.01; *** P ⬍ 0.001; not significant (N.S.).
these data, we can estimate the consequences of overand underestimation of the length at which cells
leave the meristem. If cortical cells stop dividing at a
length of 20 ␮m, one-half of what we expected, the
meristem would have been about 100 ␮m shorter,
resulting in an underestimation of the number of
meristematic cells by 6% (49 rather than 52). In a
converse manner, when cells were to leave the meristem at a length of 80 ␮m, or double the expected
value, the meristem would have been roughly 150
␮m longer but contain only 4% more cells (54 cells).
As average cell cycle duration was derived from
overall cell production, which is not affected by the
choice of the meristem boundary, and the number of
meristematic cells, the errors involved in this parameter are similarly small. Therefore, an important aspect to this paper is that it presents a less laborious
alternative to the full-blown kinematic analysis,
which is relatively accurate, particularly with respect
to the most important cell division parameters. However, there are some trade-offs. It is obvious that by
using a predefined cell length for estimating the basal
margin of the meristem, the actual size of cells exiting
the meristem cannot be determined, nor is estimation
of the physical size of the meristem very accurate (see
above). As the total number of cells in the growth
zone as a whole is independently estimated, the
choice of the margin of the meristem partitions the
same cells between meristem and elongation zone.
Relative to the number of cells in the meristem, the
number of cells in the elongation zone (Nel) is rather
small. Therefore, the same absolute difference in cell
number will create larger relative errors. In the above
example, overestimating the size of the meristem by
three cells (cells are 20 ␮m rather than 40 ␮m when
they exit the meristem) will result in underestimation
of the number of rapidly elongating cells by 16% (16
instead of 19). In a similar manner, underestimating
the number of meristematic cells by two (if cells
exiting the meristem are 80 ␮m) will cause overestimation of the number of rapidly elongating cells by
14% (16 instead of 14). Residence time in the elongation zone can be calculated the ratio between Nel and
P (Beemster and Baskin, 1998). Because P is independent of the size of the meristem, the accuracy of the
calculated residence time is affected to the same extent as Nel. In conclusion, this approach is relatively
accurate in terms of meristem parameters, whereas
the precision of elongation zone parameters is
compromised.
860
R2
Molecular Basis of Differences in Cell Production
As a second step toward linking cell cycle regulation to organ growth rates, we tested if the correlation between cell division and kinase activity in the
response to environmental conditions observed in
corn leaves (Granier et al., 2000) could also be validated by genetic variations and in Arabidopsis roots.
The data suggest that this is the case. In fact, we
recently found that even in transgenic lines that are
inhibited in cell production as a consequence of the
overexpression of the cell cycle gene CKS1At (De
Veylder et al., 2001b), kinase activity and cell production are reduced simultaneously (G.T.S. Beemster
Figure 4. The absence of a relationship between endoreduplication
and mature cortical cell size (A) and CDKA (B) in the roots of 18
Arabidopsis ecotypes. The fraction of endoreduplicated cells was
estimated from 10 complete root systems from which the nuclei were
isolated and analyzed by flow cytometry. Mature cortical cell length
was determined as the average over all positions in the mature part
of the root (see “Materials and Methods”).
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Plant Physiol. Vol. 129, 2002
Cell Cycle Regulation in Roots of Arabidopsis Ecotypes
pressed in C24 background (Hemerly et al., 1995). In
these plants, kinase activity in the root tips was, in
contrast to what was seen at the whole-plant level,
not higher than wild type (G.T.S. Beemster and L. De
Veylder, unpublished data). It may well be that when
the same construct is overexpressed in an ecotype
with intrinsically low and rate-limiting CDKA expression levels, it will result in increased kinase activity and cell division. Nevertheless, additional
mechanisms are likely to operate in plants with relatively high CDK levels. It can be presumed that
many of the CDK interacting proteins could fulfill
this role. It has been demonstrated that overexpression of B- and D-type cyclins can enhance plant
growth rates, presumably by increasing cell production in the meristems (Doerner, 1994; Cockcroft et al.,
2000). In a similar manner, overexpression of the
CDK inhibitor KRP2 was shown to inhibit kinase
activity and cell division rates in Arabidopsis leaves
(De Veylder et al., 2001a).
Endoreduplication and Mature Cell Size
Figure 5. Cell production rates are correlated with kinase activity
and CDKA levels in the root tip of 11 Arabidopsis ecotypes. A,
Relationship between kinase activity (A) and cell production (P;
regression, P ⫽ ⫺0.47 ⫹ 0.7 A; R2 ⫽ 0.56, P ⬍ 0.001). B, Relationship between CDKA levels (C) and kinase activity (regression, C ⫽
0.02 ⫹ 0.19 A; R2 ⫽ 0.32, P ⫽ 0.09). Kinase activities were calculated relative to the average of all samples from each individual
experiment and then averaged between two replicate experiments.
Error bars denote SEs (n ⫽ 2). CDKA levels were determined by
measuring the integrated intensity on western blots. The regressions
were obtained from multiple linear regression analysis.
and L. De Veylder, unpublished data). Therefore, we
conclude that evidence is mounting to support a
pivotal regulatory role for kinase activity in plant
growth regulation, in response to the environment
(Granier et al., 2000) and genetic predisposition (this
paper). Important in this context is how CDKA is
regulated. Our data show a significant correlation
between CDKA protein levels and kinase activity,
implicating differences in transcription or protein
stability. However, there was also considerable variation in CDKA levels that resulted in similar kinase
activities (Fig. 5B), suggesting that CDKA levels
could be limiting in some, but not all ecotypes. The
latter situation would be analogous to that of tobacco
(Nicotiana tabacum) Bright Yellow 2 suspension cultures, where most of the CDKA protein was found to
be in its inactive, monomeric form (Porceddu et al.,
2001). This could well explain the absence of a
growth stimulatory effect when CDKA was overexPlant Physiol. Vol. 129, 2002
Our data shows that between the 18 ecotypes analyzed, there was large variation in mature cortical cell
size. We investigated the hypothesis that differences
in cell size would be associated nDNA content, determined by the number of endoreduplication cycles
each cell undergoes. However, we found no correlation between the fraction of cells that had undergone
endoreduplication and mature cortical cell length.
This is in contrast with other observations in Arabidopsis on leaf epidermal cells (Melaragno et al.,
1993), hypocotyls (Gendreau et al., 1997), and
trichomes. In addition, we recently reported that
overexpression of the cell cycle gene KRP2 in Arabidopsis results in a large increase in the size of most
cell types, whereas nDNA content in the same leaves
was reduced (De Veylder et al., 2001a). Together,
these data refute the conception that endoreduplication would drive or is even required for obtaining a
large cell size. Rather, it seems that endoreduplication and mature cell size are genetically independent
phenomena in these ecotypes. As for the regulation
of the endoreduplication process itself, our data
showed no correlation between CDKA kinase activity and endoreduplication levels (Fig. 4B). This could
be explained in two ways. First, the kinase activity
signal associated with endoreduplication is independent of and obscured by the much higher activity
associated with cell production. Second, it is conceivable that endoreduplication is regulated by other
CDKs such as CDKB (Yoshizumi et al., 1999) or depends upon the relative activity of a combination of
different CDK types. Further research targeted directly at the regulation of this process should deliver
more insight as to the validity of each of these explanations. With regard to the molecular mechanism
that determines mature cell size, the processes asso-
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Copyright © 2002 American Society of Plant Biologists. All rights reserved.
861
Beemster et al.
ciated with cell wall formation and expansion are
obvious candidates. Most progress in this field has
been made with regards to expansins. They have first
been shown to be correlated with cell expansion
(Cosgrove and Li, 1993), and more recently, it was
also demonstrated that their overexpression alters
plant growth, a change that is correlated with differences in mature cell size (Cho and Cosgrove, 2000).
Therefore, it would be interesting to investigate to
what extent differences in mature cell length observed here are associated with the activity and expression levels of these proteins.
Prospects
With the availability of the complete genome sequence of Arabidopsis (The Arabidopsis Initiative,
2000) and several mapping populations (AlonsoBlanco and Koornneef, 2000), it seems feasible to map
and clone the genes that are responsible for the observed variations in growth parameters. One interesting aspect from such an approach is that from all
the genes that potentially affect plant growth rate in
some way, it would identify the ones that actually do
so in natural conditions. However, the fact that such
genes result in variation in cell cycle or cell expansion
should not be taken as evidence that they are directly
involved in these processes. It is very well possible
that they involve crucial upstream events that, in
turn, impact on particular growth processes. Finding
the genes and functions that underlie variation in
growth characteristics is going to be an exciting challenge for future research. Such work holds great
promise for improvements of growth characteristics
of agronomically important crop species.
MATERIALS AND METHODS
Root Growth and Microscopy
Seeds of each of 18 ecotypes (see Table I) were sterilized
in 12.5% sodium hypochloride for 10 min, and eight were
subsequently plated approximately 1 cm from the edge of
each of 10 replicate square (9 ⫻ 9 cm) petri dishes (Greiner
Biochemica, Flacht, Germany), containing 1⫻ Murashige
and Skoog medium (Duchefa, Haarlem, The Netherlands),
1 g L⫺1 Suc (Merck Eurolab, Darmstadt, UK), and 0.8 g L⫺1
plant tissue culture agar (Lab M, Bury, UK). After sowing,
the plates were stored in a refrigerator at 4°C for 3 d, and
were subsequently placed near vertically in a growth
chamber set at 22°C and with constant light (PAR ⫽ 80 ␮E
m⫺2 s⫺1) supplied by a bank of cool-white fluorescent
tubes. Directly after germination, the position of the root
tip was marked daily on the back of the dishes with a
razorblade. At d 7, 9, and 11, 10 roots were harvested from
two representative plates. These roots were mounted on a
microscope slide in the same culture medium without the
agar. Two strips of scotch tape were used as spacers to
reduce pressure from the coverslip. Cortical cells were
visualized using differential interference contrast optics of
862
a microscope (DMLB; Leica, Wetzlar, Germany) fitted with
a 20⫻ lens (HL PL Fluotar; Leica, n.a. ⫽ 0.50), and an
imaging system encompassing a CCD camera (4910 CCIR;
Cohu, San Diego) and frame grabber board (LG3 CCIR;
Scion, Frederick, MD) fitted in a PentiumII PC running the
image analysis program ScionImage (WinNT version b3b;
Scion). Using this imaging software, a series of overlapping
images spanning 2 to 3 mm of the root apex, covering the
entire growth zone and well into the mature region were
obtained. The remaining plates were digitized at a resolution of 5.9 pixels mm⫺1 using a flatbed scanner (Scanjet
4C/T; Hewlett-Packard, Palo Alto, CA). On the latter images, the growth rate of individual roots was determined
from daily length increments, i.e. its length between subsequent marks, divided by the corresponding time interval.
These rates were subsequently averaged for all observed
roots (E).
Kinematic Analysis
In case root growth was steady state, the cell length
profile of only d 9 roots was determined. In case root
growth accelerated over time, this was also done for d 7
and 11. For this, a composite image was created by pasting
overlapping sections together using the imaging program
Photoshop (version 5.0; Adobe Systems, Mountain View,
CA). On these composite images the length of all cortical
cells was measured per file and was expressed as a function
of their position relative to the base of the quiescent center.
The data of all files of a root were combined and interpolated into 25-␮m spaced points using a kernel smoothing
routine described earlier (Beemster and Baskin, 1998) implemented as a macro in Excel (version 97; Microsoft, Redmond, WA). From these data, the number of meristematic
cells (Ndiv) as well as the mature cell length (lmat) was
estimated for each root individually. To this end, the base
of the meristem was defined as the position where the cells
reach a length of 40 ␮m, a value typically observed in
earlier experiments (Beemster and Baskin, 1998, 2000; De
Veylder et al., 2001b). Average cell division rates and cell
cycle duration were calculated using the equations described earlier (Beemster and Baskin, 2000).
Multiple Linear Regression Analysis
The relationships between growth parameters were investigated by multiple linear regression analysis. For this,
we log transformed the data, effectively changing Equations 1 and 2 into Equations 3 and 4, respectively:
log共E兲 ⫽ C0 ⫹ C1log共lmat兲 ⫹ C2log共P兲
(Eq. 3)
log共P兲 ⫽ C0 ⫹ C1log共Ndiv兲 ⫹ C2log共Tc兲
(Eq. 4)
The regression analysis itself was performed using the
add-in analysis toolpack of Excel (Microsoft).
Flow Cytometry
To determine DNA content of root cells, approximately
25 complete root systems of 9-d-old seedlings were har-
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Copyright © 2002 American Society of Plant Biologists. All rights reserved.
Plant Physiol. Vol. 129, 2002
Cell Cycle Regulation in Roots of Arabidopsis Ecotypes
vested after the plates were scanned for evaluation of root
elongation rate (see “Root Growth and Microscopy”) and
prepared by chopping the roots in 300 ␮L of Galbraith
buffer (Galbraith et al., 1991) using a razor blade. The
resulting solution was filtered using a 30-␮m mesh after
which 1 ␮L of a 1 mg mL⫺1 stock of 4⬘,6-diamidino-2phenylindole was added. The DNA content distribution
was analyzed with the aid of a flow cytometer and accompanying software (Bryte HS and WinBryte, respectively;
Bio-Rad, Hercules, CA).
CDKA and Concentration Assays
Histone H1 kinase activity was analyzed in two separate
experiments on samples of 5-mm tips from approximately
250 roots grown on 10 plates as described above. These were
cut, immediately frozen, and stored in liquid nitrogencooled 2-mL Eppendorf tubes. Protein extracts were prepared by grinding the frozen tissue in a cooled ball mill
(mm20; Retsch, Haan, Germany), after which the protein
fraction was isolated by centrifuging in a homogenization
buffer (50 mm Tris-HCL, pH 7.2, 60 mm ␤-glycero-phosphate, 15 mm nitrophenyl phosphate, 15 mm EGTA, 15 mm
MgCl2, 2 mm dithiothreitol, 0.1 mm vanadate, 50 mm NaF, 20
␮g ml⫺1 soybean trypsin inhibitor, 100 ␮m benzamidine, 1
mm phenylmethylsulfonylfluoride, and 0.1% Triton X-100).
Total protein concentration in the extracts was determined
with a protein assay kit (Bio-Rad, Hercules, CA). CDKA
complexes were purified from total plant extracts by affinity
binding to p9CKS-Sepharose beads, which specifically bind
A-type CDKs (Stals et al., 2000). Otherwise, the protocol
described earlier was followed (Hemerly et al., 1995). To
ascertain fidelity of the kinase data, we repeated the kinase
assay twice in independent experiments.
CDKA protein levels were determined by western-blot
analysis. For this, total protein extract was denatured for 10
min at 95°C in SDS loading buffer, separated on a 12.5%
SDS-PAGE gel, and blotted on a nitrocellulose membrane
(Hybond-C⫹; Amersham Pharmacia Biotech, Piscataway,
NJ). Filters were blocked overnight with 2% milk powder
in phosphate-buffered saline (PBS) containing 0.1% Tween
20 (PBST), washed five times with PBST, and probed for 2 h
with anti-CDKA;1 (diluted 1/5,000 in blocking solution).
The blots were rinsed five times with PBST and were
incubated for 1 h with anti-rabbit horseradish peroxidaseconjugated antibodies (Amersham Pharmacia Biotech) diluted in blocking solution. The membranes were washed
five times with PBST, and signals were developed using a
chemiluminescent detection kit (PerkinElmer Life Sciences,
Boston). The integrated intensity of the bands was measured correcting for background intensities by image analysis using the image analysis program ScionImage.
ACKNOWLEDGMENTS
The authors are grateful to Gerrit West for expert help
with the kinase assays, Tom Beeckman for his support with
the microscopy, and Marnik Vuylsteke for helpful suggestions. Lieven de Veylder, Marnik Vuylsteke, Tom BeeckPlant Physiol. Vol. 129, 2002
man, Kristiina Himanen, and Fabio Fiorani are gratefully
acknowledged for their constructive comments on earlier
versions of this manuscript.
Received January 18, 2002; returned for revision March 11,
2002; accepted March 22, 2002.
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