CSIRO PUBLISHING
Functional Plant Biology, 2007, 34, 41–51
www.publish.csiro.au/journals/fpb
Ethylene modulates genetic, positional, and nutritional regulation
of root plagiogravitropism
Paramita BasuA , Yuan-Ji ZhangB , Jonathan P. LynchA,B and Kathleen M. BrownA,B,C
A
Intercollege Program in Plant Biology, The Pennsylvania State University, University Park, PA 16802, USA.
Department of Horticulture, The Pennsylvania State University, University Park, PA 16802, USA.
C
Corresponding author. Email: [email protected]
B
Abstract. Plagiogravitropic growth of roots strongly affects root architecture and topsoil exploration, which are important
for the acquisition of water and nutrients. Here we show that basal roots of Phaseolus vulgaris L. develop from 2–3 definable
whorls at the root–shoot interface and exhibit position-dependent plagiogravitropic growth. The whorl closest to the shoot
produces the shallowest roots, and lower whorls produce deeper roots. Genotypes vary in both the average growth angles
of roots within whorls and the range of growth angles, i.e. the difference between the shallowest and deepest basal roots
within a root system. Since ethylene has been implicated in both gravitropic and edaphic stress responses, we studied the
role of ethylene and its interaction with phosphorus availability in regulating growth angles of genotypes with shallow or
deep basal roots. There was a weak correlation between growth angle and ethylene production in the basal rooting zone,
but ethylene sensitivity was strongly correlated with growth angle. Basal roots emerging from the uppermost whorl were
more responsive to ethylene treatment than the lower-most whorl, displaying shallower angles and inhibition of growth.
Ethylene sensitivity is greater for shallow than for deep genotypes and for plants grown with low phosphorus compared
with those supplied with high phosphorus. Ethylene exposure increased the range of angles, although deep genotypes
grown in low phosphorus were less affected. Our results identify basal root whorl number as a novel architectural trait,
and show that ethylene mediates regulation of growth angle by position of origin, genotype and phosphorus availability.
Additional keywords: basal roots, gravitropism, Phaseolus vulgaris, phosphorus, root architecture.
Introduction
Gravistimulation of orthogravitropic organs is a standard system
for studying gravity responses, and has led to substantial
advances in understanding the mechanisms of gravity sensing
and response in plants. However, few plant organs are
actually orthogravitropic, but rather, grow at some other
angle with respect to gravity (i.e. they are plagiogravitropic).
Plagiogravitropism is poorly understood, but is characteristic of
most graviresponsive organs and has important ecological and
agricultural implications.
One important consequence of root plagiogravitropism is
its influence on root architecture and soil resource acquisition.
For example, phosphorus, a highly immobile nutrient in soil,
is distributed heterogeneously in most soils, with greatest
availability in upper soil layers and availability decreasing with
depth (Pothuluri et al. 1986). The seedling roots (basal roots)
of bean establish characteristic growth angles very early. Their
initial growth trajectory determines the vertical distribution
of root length in the soil, including not only the basal root
axes, but also lateral roots that develop from basal roots over
time (Liao et al. 2001). Overall root system depth determines
the efficiency of exploration for shallow resources such as
phosphorus (Lynch and Brown 2001) and deep resources such as
water (Ho et al. 2004, 2005). Analysis of contrasting bean and
© CSIRO 2007
maize genotypes indicates that shallowness of seedling roots is
closely correlated with phosphorus acquisition in soils with low
phosphorus availability (Bonser et al. 1996; Liao et al. 2001,
2004; Zhu et al. 2005).
In this paper, we examine regulation of basal root growth
angle in common bean (Phaseolus vulgaris L.). The bean root
system consists of a primary root, a variable number (8–16)
of basal roots (Lynch and van Beem 1993) originating from
the root–shoot interface, i.e. the region between lower part
of hypocotyl and upper part of primary root (Zobel 1986),
adventitious roots emerging from the subterranean hypocotyl,
and lateral roots developing from each of the other root classes.
The primary root reaches a length of 2–3 cm 2 days after seed
imbibition, the basal roots emerge 3 days after imbibition,
and the adventitious roots develop after about 12 days. Basal
roots, together with the primary root, constitute the major
scaffolding of the root system, since these root types appear
earliest. Basal root growth angles (BRGA) vary with genotype,
resulting in genotypic variation in effective rooting depth
(Bonser et al. 1996; Liao et al. 2001; Lynch and Brown 2001).
In some genotypes, basal roots grow shallower with decreased
phosphorus availability (Bonser et al. 1996; Liao et al. 2001),
indicating genetic variation for both growth angle and for its
plasticity in response to phosphorus availability.
10.1071/FP06209
1445-4408/07/010041
42
Functional Plant Biology
Ethylene, a plant hormone often associated with stress
responses, is likely to be important for basal root gravitropic
responses to low phosphorus availability (Lynch and Brown
2001). Ethylene is intimately involved with auxin in the control
of differential growth responses (Harper et al. 2000) and is
known to modulate gravitropic responses in roots and shoots
(Abeles et al. 1992; Philosoph-Hadas et al. 1996; Madlung et al.
1999; Edelmann 2002; Edelmann et al. 2002). Low phosphorus
availability increases ethylene production by bean and tomato
roots (Borch et al. 1999; Lynch and Brown 2001). Preliminary
evidence from our group indicates that ethylene treatment makes
common bean basal roots shallower, and ethylene inhibitors
make them deeper (Zhang 2002).
We hypothesise that ethylene may be involved in
genetic, positional and nutrition-induced variation of
BRGA. To investigate this hypothesis, we used common
bean genotypes contrasting in BRGA and recombinant
inbred lines (RIL) generated from two different populations
manifesting contrasting root architecture within the same
genetic background.
Materials and methods
Common bean (Phaseolus vulgaris L.) genotypes G19833 and
DOR364 were used to generate a population of F12 RIL
[obtained from International Center for Tropical Agriculture
(CIAT) Cali, Colombia]. G19833 is a large, black-seeded
genotype from the Andean gene pool that has an indeterminate
bush growth habit (Yan et al. 1995) and DOR364 is
of Mesoamerican origin (Singh et al. 1991) and has an
indeterminate bush habit (Type II), erect stems and small seeds
(Singh 1982). G19833 is better adapted to phosphorus-limited
conditions and has a shallower root system than DOR364 (Lynch
1995; Bonser et al. 1996; Beebe et al. 1997; Liao et al. 2001).
The two parental genotypes and six RIL were selected for these
experiments based on their growth angle. RIL were selected
that had shallow or deep basal roots, according to screening
under low phosphorus availability (Liao et al. 2004). We also
used the ‘L88’ RIL developed by Dr Jim Kelly (Michigan
State University) from a cross of B98311 and TLP19. B98311
is drought resistant Mesoamerican genotype from the MSU
breeding program that possesses a Type II growth habit and
a deep vigorous primary root (Frahm et al. 2004) and TLP19
was developed for tolerance to low phosphorus at CIAT and
also possesses a Type II growth habit. The RIL descending from
the cross between these two parents share a common genetic
background, yet segregate for root architectural traits as well as
adaptation to abiotic stress.
Seeds were surface sterilised with 6% sodium hypochlorite
for 5 min, rinsed thoroughly with distilled water and scarified
with a blade. Seeds were germinated at 28◦ C in darkness for
2 days in rolled germination paper (25.5 × 37.5 cm, Anchor
Paper Co., St Paul, MN, USA) moistened with either low or high
phosphorus nutrient solution, which was composed of (in µM)
3000 KNO3 , 2000 Ca(NO3 )2 , 250 MgSO4 , 25 KCl, 12.5 H3 BO3 ,
1 MnSO4 , 1 ZnSO4 , 0.25 CuSO4 , 0.25 (NH4 )6 Mo7 O24 and
25 Fe-Na-EDTA. For high phosphorus solutions, 1000 µM
NH4 H2 PO4 was added; for low phosphorus, 500 µM (NH4 )2 SO4
was added. Germinated seeds with radicals ∼2–3 cm long
were transferred to growth pouches consisting of a sheet of
P. Basu et al.
30 × 24 cm blue germination paper (Anchor Paper Co.) inserted
into a polyethylene bag of the same size with evenly spaced
(3 cm apart) holes for aeration. Pouches were open at the bottom
to allow direct contact with the nutrient solution containing
high (1 mM) or low (0 mM) phosphorus as described above. The
pouches were stiffened by attaching perforated plexiglass sheets
to stabilise the root system. Pouches containing seedlings were
suspended in nutrient solution and covered with aluminum foil
to prevent illumination of the roots. Containers with pouches
and nutrient solution were held at 25◦ C. The initial position of
each basal root tip was marked on the plexiglass at the time of
transplant, defined as time 0. Root systems were photographed
after 2 days growth in pouches and basal root angles were
determined using Matlab 7.0 (Mathworks Inc., Natick, MA,
USA). Growth angles of basal roots were measured as the angle
between the vertical axis and the line connecting the root tip
positions at 0 and 48 h, i.e. larger angles indicate shallower
basal roots. The range of growth angles for each plant was
calculated by subtracting the minimum growth angle from
the maximum growth angle exhibited by the basal roots of an
individual plant.
For ethylene measurement, fresh hypocotyls bearing basal
roots were harvested from 3-day-old seedlings. The segments
were separated into three basal root whorls with a razor blade
and enclosed individually in 9-mL vials capped with septa
at 25◦ C. Ethylene was sampled with a 1-mL syringe from
the headspace of the vials 2 h later and quantified by gas
chromatography (HP6890 gas chromatograph equipped with
a flame ionisation detector and an activated alumina column,
Hewlett-Packard Co., Wilmington, DE, USA). In a preliminary
experiment, we measured ethylene from the intact tissue (whole
segment of basal rooting zone) compared with tissues divided
into three whorls and found that dividing the root tissue did not
significantly affect ethylene production compared with intact
tissue (data not shown). In addition, we measured endogenous
ethylene production from the hypocotyl tissue (at the root–
shoot interface) separately from the basal roots and found that
endogenous ethylene production from this tissue does not vary
with whorl position or treatment, so differences shown are due
to ethylene production by root tissue.
In an initial study of ethylene sensitivity of basal roots, we
treated the parent genotypes, TLP19 and B98311 with ethylene
immediately after transfer to growth pouches containing
0 or 1 mM phosphorus. Seedlings in pouches were placed in
airtight chambers (53 × 36 × 31 cm) containing air or ethylene
(0.6 µL L−1 ) for 1 day at 25–26◦ C. Images of the root system
were recorded with a digital camera after 1 day of ethylene
treatment, and growth angles were measured between the vertical
and the line connecting the base of the basal root with the root tip.
Ethylene concentrations were monitored by gas chromatography.
To test the effect of ethylene inhibition, seedlings were treated
with the ethylene action inhibitor 0.43% 1-methylcyclopropane
(MCP; EthylBloc, Floralife Inc., Walterboro, SC, USA). The
seedlings were treated with MCP just after transplanting to the
pouch and the seedlings were kept in airtight growth chambers,
118 L in volume. MCP gas was released by adding EthylBloc
(4 mg EthylBloc per 0.08 mL buffer per L air space) to a plastic
dish affixed to the roof of the chamber and adding buffer to it
via a syringe inserted through a rubber septum. The seedlings
Ethylene modulation of root plagiogravitropism
were treated for 24 h and growth angles were measured from the
digital images of the basal roots.
Phosphorus content was measured in tissue harvested from
3-day-old seedlings of the deep and shallow genotypes of the
DOR364 × G19833 RIL. Fresh tissue containing basal roots
was harvested, dried at 60◦ C and weighed. Dried samples were
ground, ashed at 500◦ C for 10 h and analysed for phosphorus
content spectrophotometrically (Murphy and Riley 1962).
To study the effect of ethylene on BRGA, exogenous ethylene
was applied to germinated seedlings immediately after transfer to
growth pouches containing low or high phosphorus. Seedlings
in pouches were exposed to air or concentrations of ethylene
ranging from 0.1 to 0.8 µL L−1 for up to 48 h at 25–26◦ C.
Ethylene concentrations were monitored by gas chromatography.
The concentration of ethylene in control chambers ranged
from 0.028 µL L−1 to 0.041 µL L−1 . Digital images of the
roots were taken after 24 and 48 h and the basal root growth
angles were measured as the angle between the vertical and
the line connecting the root tip positions at 24 and 48 h using
Matlab 7.0 (Mathworks Inc., Natick, MA, USA). Since the roots
curve very little during each 24 h interval, the measurement
of root angle based on positions of the root tip at 24 and
48 h reflects the trajectory of the root, and therefore provides
accurate measurement of root angle. However, to accurately
measure the root growth (increase in length between 24 and
48 h), the roots were traced on the images and measurements
were made along the tracings from the same digital images.
The experiment was repeated three times with 2–3 plants per
genotype per treatment each time. Ethylene sensitivity was
estimated as the slope of the linear regression line fitted to
BRGA v. ethylene concentration data for each genotype, whorl
position and phosphorus treatment.
To assess the effect of exogenous ethylene on the range of
BRGA, we calculated growth angles as the angle between the
vertical axis and the line connecting the root tip positions at
0 and 48 h. From these measurements, the range of growth angles
was calculated by subtracting the minimum growth angle from
the maximum growth angle exhibited by the basal roots of each
individual plant.
Ethylene concentration was measured in the rhizosphere
around bean plants by inserting plastic tubes fitted with septa
into the soil within 15 cm of the base of bean plants growing
in fertilised, irrigated soil (typic hapludalf) in Central PA.
Accumulated ethylene in the tubes was collected with syringes
on several dates at two sites over two seasons.
Where statistical analyses were appropriate, the data were
analysed by analysis of variance (ANOVA) for the main effects
(phosphorus, ethylene, genotype and whorl of origin). Both
ANOVA and calculations of ethylene response functions were
performed with SPSS (Graduate Pack, Version 12 for Windows,
SPSS Inc., Chicago, IL, USA).
Results
Morphology of basal root production
Basal roots comprised a major part of the bean root system
(Fig. 1). These roots emerged within 3 days of germination
from distinct whorls at the root–shoot junction (Fig. 2 inset).
We designated the whorls bearing basal roots from top (closest
Functional Plant Biology
43
Fig. 1. Seedling root system of common bean TLP19 photographed 4 days
after germination and 2 days after transplanting to a pouch. Long basal roots
are visible at the base of the hypocotyls, and lateral roots are just emerging
at the upper part of the primary root.
to the shoot) to bottom as whorl 1, 2, and 3 successively.
Whorl 1 typically bore fewer roots than the lower whorls
(Table 1), but all roots emerged in a tetrarch pattern, i.e. in
four files (Figs 1, 2 inset), and on occasions when more than
four roots emerged from a given whorl, two emerged from the
same position. The number of basal roots per whorl varied
among genotypes. B98311, TLP19, and G19833 each typically
had three whorls of basal roots, but DOR364 typically had
only two whorls (Table 1). There was no significant effect of
phosphorus on the number of basal roots per whorl or the number
of whorls.
Basal root angle depends on genotype and position
of origin
The effects of genotype and phosphorus on basal root angle
were first tested on selected RIL derived from the cross of the
parent lines G19833 and DOR364 that exhibited differences
in BRGA in preliminary screening under low phosphorus
availability (Zhang 2002; Liao et al. 2004). Basal roots of
most genotypes of the G19833 × DOR364 RIL population grew
shallower under low phosphorus (data not shown). The extent
of the phosphorus effect (plasticity) varied with genotype, but
44
Functional Plant Biology
P. Basu et al.
100
Basal root angle (degrees from vertical)
whorl 1
whorl 2
whorl 3
80
60
40
20
TLP19
RIL15
RIL57
B98311
Shallow genotypes
RIL7
RIL76
Deep genotypes
Fig. 2. Effect of genotype and position of origin on basal root angle of common bean. Insert shows a close up view of a young seedling (3 days after
imbibition) showing three distinct whorls bearing emerging basal roots. All genotypes are from the L88 population. The growth angle of the basal roots was
measured after 2 days growth in pouches. The bars show mean basal root growth angles of 10–12 plants per genotype, with data pooled over phosphorus
treatments ± s.e.
Table 1.
Average number of basal roots per whorl in four parent
genotypes
The numbers designate mean numbers of basal roots of 6–8 plants ± s.e.
The upper whorl is designated as whorl 1, and the lower whorl as whorl 3
Genotype
Whorl 1
Whorl 2
Whorl 3
B98311
TLP19
G19833
DOR364
2.5 ± 0.2
2.3 ± 0.1
3.2 ± 0.2
3.1 ± 0.2
2.7 ± 0.1
3.2 ± 0.1
3.9 ± 0.2
3.9 ± 0.1
3.5 ± 0.1
3.9 ± 0.1
4.1 ± 0.1
–
shallow genotypes as a group were not significantly more
responsive to phosphorus treatment than deep genotypes (data
not shown). Genotype had a much greater effect on BRGA than
phosphorus treatment (F-values from ANOVA were 283 and
11.7, respectively). Analysis of regulation of growth angles of
RIL in the G19833 × DOR364 population might be complicated
by the fact that G19833 differs from DOR364 in the number of
whorls, and the RIL population showed segregation for whorl
number. The majority of experiments were, therefore, performed
using L88, a population with uniform whorl number (Table 1).
The parents of the L88 population were the phosphorus-efficient
genotype TLP19 and the drought-tolerant genotype B98311.
RIL with contrasting basal root angles were selected from
this population based on preliminary experiments. As expected,
TLP19 had shallower basal roots, and B98311 had deeper basal
roots (Fig. 2). RIL 15 and 57 had shallower basal roots than
RIL 7 and 76 (Fig. 2). The growth angle of basal roots of all
genotypes varied with position of origin (Fig. 2). Basal roots
Table 2. Range of growth angles of basal roots per plant in six
genotypes (three deep and three shallow) from the L88 population
The three deep genotypes used for the experiment of growth angle
measurement are B98311, RIL7 and RIL76, whereas the three shallow
genotypes are TLP19, RIL15 and RIL57. N = 4–7 plants per genotype
Genotypes
Mean
angle
Standard
deviation
Range
of
angles
Minimum
growth
angle
Maximum
growth
angle
Deep
Shallow
41.7
56.4
14.0
18.0
39.3
54.5
21.3
28.5
60.6
82.9
emerging from whorl 1 were consistently shallower than those
from whorl 3.
Range of basal root growth angles
The range of basal root growth angles varied with genotype,
and was smaller for deep genotypes than for shallow genotypes
(Table 2). There was no significant effect of phosphorus
treatment on angle range, so the range data for high and low
phosphorus treatments were pooled.
Effect of genotype, phosphorus treatment and position
of origin on ethylene production
To test the hypothesis that greater ethylene production results in
shallower basal root growth, we measured ethylene production
rates in basal roots of different genotypes. Since the growth
angle of basal roots can be determined at a very early stage
of development, ethylene production was measured just as the
Ethylene modulation of root plagiogravitropism
Functional Plant Biology
Basal root elongation depends only on root position
of origin
Roots from lower whorls elongated significantly faster (F = 111,
P < 0.001) than those from the uppermost whorl, regardless
of phosphorus treatment and genotype (Fig. 4). Phosphorus
treatment did not affect elongation of these genotypes, except the
phosphorus-inefficient genotype DOR364, which had only two
whorls (Fig. 4). Root elongation rate exhibited a weak negative
correlation with ethylene production (r2 = 0.123, P < 0.001).
Similar results were obtained when the elongation rate of basal
roots was assessed between 24 and 48 h (data not shown).
Ethylene treatment alters basal root growth angles,
range and root growth
Ethylene treatment significantly increased the shallowness of the
L88 parent genotypes (TLP19, B98311) in all whorls, and the
(B)
(A)
Ethylene production nL–1 h–1 g–1 FW
ethylene action inhibitor MCP made the roots from whorls 1 and
2 significantly deeper (F = 309, P < 0.001) (Fig. 5). There was
no significant effect of MCP on roots from whorl 3, and its effect
was smaller on the deeper genotype, B98311, than on TLP19.
There were no significant phosphorus effects or interactions.
Neither genotype, phosphorus treatment, nor ethylene
treatment had a significant effect on the internal phosphorus
content of tissue bearing basal roots from 3-day-old
seedlings (mean phosphorus content = 198 µmol g−1 dry
weight), although seedlings grown a few days longer in high
phosphorus accumulate ∼10% more phosphorus than those
grown without phosphorus (Bonser et al. 1996).
For a more detailed examination of the effects of ethylene
on BRGA, seedlings of three shallow and three deep genotypes
from the L88 population were exposed to a range of ethylene
concentrations to generate dose–response functions. An example
of ethylene dose–responses for the shallow parent (TLP19)
grown in low phosphorus nutrient solution is provided in Fig. 6.
Ethylene sensitivity was defined as the slope of the ethylene
response function for each genotype, whorl and phosphorus
treatment. Ethylene sensitivity was greater in shallow genotypes
compared with deep genotypes, and the basal roots growing
from the upper whorl were more responsive than the basal
roots of lower whorls (Fig. 7; Table 3). The basal roots were
more responsive to exogenous ethylene treatment when grown
with low phosphorus compared with high phosphorus (Fig. 7;
Table 3). Ethylene sensitivity was strongly correlated with
growth angle with high phosphorus availability, but less so
with low phosphorus availability (Fig. 8). Most basal roots
grown with low phosphorus were highly responsive to ethylene
treatment, but in high phosphorus, responsiveness increased with
shallowness.
Elongation of most basal roots was significantly reduced
by the low concentrations of ethylene used in this experiment
whorl 1
whorl 2
whorl 3
30
20
0.36
0.24
10
0.12
0
Ethylene production nL–1 h–1 per basal root
basal roots were emerging and the roots were 0.6–2.6 cm long. In
both shallow and deep genotypes, whorl 1 produced significantly
more ethylene than the two lower whorls whether ethylene
production was expressed on a fresh weight basis (Fig. 3A) or
per basal root (Fig. 3B). Ethylene production was significantly
higher in the uppermost whorl when ethylene production was
expressed per g fresh weight (F = 61, P < 0.001) or per basal
root (F = 23, P < 0.001). Ethylene production per basal root, but
not per g fresh weight, was significantly less in deep than shallow
genotypes (F = 6.1, P < 0.05) and higher with low phosphorus
treatment (F = 91, P < 0.001). There was a positive correlation
between ethylene production and growth angle of basal roots
(r2 = 0.234, P < 0.001), which resulted from greater ethylene
production and larger angles in whorl 1. Ethylene production
was not correlated with genotypic and phosphorus-related angle
differences.
45
0
Shallow
genotypes
Deep
genotypes
Low P
High P
Shallow
genotypes
Low P
High P
Deep
genotypes
Fig. 3. Endogenous ethylene production per gram fresh weight (pooled over phosphorus treatments (A) and per basal root (separately for phosphorus
treatments) (B) by segments of the root-shoot junction bearing basal roots. Segments were harvested 3 days after imbibition. Values shown are means of eight
plants from each of three shallow and three deep genotypes from the L88 population ± s.e.
46
Functional Plant Biology
P. Basu et al.
3
whorl 1
whorl 2
Growth rate (cm day–1)
whorl 3
2
1
0
Shallow
Deep
G19833
L88 genotypes
High P
Low P
DOR364
Fig. 4. Growth rate of basal roots measured during the first 24 h growth in pouches. Values shown are means ± s.e. of eight plants from each of three shallow
and three deep genotypes from the L88 population, the shallow genotype G19833, and the deep genotype DOR364, which has only two whorls. Phosphorus
effects are significant only for DOR364 (F = 5.7, P = 0.022) and values were pooled over phosphorus treatments for the other genotypes. Growth rate is
significantly affected by whorl of origin in all genotypes (L88: F = 125, P < 0.001; DOR364: F = 19, P < 0.001; G19833: F = 8.5, P < 0.001).
120
control
Basal root growth angle (degrees from vertical)
MCP
ethylene
100
80
60
40
20
whorl 1
whorl 2
TLP19
whorl 3
whorl 1
whorl 2
whorl 3
B98311
Fig. 5. Effect of 0.43% 1-methylcyclopropane (MCP) and 0.6 µL L−1 ethylene on basal root angle of a shallow (TLP19) and deep (B98311) genotype.
The plants were treated with either MCP or ethylene for 24 h immediately after transferring to the pouch. Values shown are means of 10–12 plants per
genotype ± s.e., with data pooled over both high and low phosphorus treatments.
Ethylene modulation of root plagiogravitropism
Functional Plant Biology
Table 3. ANOVA of growth angle and growth response of basal roots
from contrasting genotypes (shallow and deep) of the L88 population as
affected by exogenous ethylene treatment
The three deep genotypes were B98311, RIL7 and RIL76, and the three
shallow genotypes were TLP19, RIL15 and RIL57
100
whorl 1
Growth angle (degrees from vertical)
47
80
Effect
df
Genotype
Phosphorus
Ethylene
Whorl
Genotype × phosphorus
Genotype × ethylene
Genotype × whorl
Phosphorus × ethylene
Phosphorus × whorl
Ethylene × whorl
1
1
5
2
1
5
2
5
2
10
whorl 2
60
whorl 3
40
Growth angle
F-value P-value
701.9
0.178
220.0
2218
2.741
2.620
64.83
11.34
3.484
4.957
<0.001
0.673
<0.001
<0.001
0.098
0.023
<0.001
<0.001
0.031
<0.001
Growth rate
F-value P-value
25.29
4.102
118.1
730.5
0.193
0.642
0.515
0.193
3.854
11.54
<0.001
0.046
<0.001
<0.001
0.662
0.718
0.584
0.965
0.021
<0.001
50
20
0
0.1
0.2
0.4
0.6
0.8
low P
1
y = 0.2873x + 14.625
Ethylene concentration (µL L–1)
R 2 = 0.5075, P = 0.05
40
40
Ethylene sensitivity
Ethylene sensitivity of growth angle
(degrees per µL L–1) (slope of the response curve)
Fig. 6. Example of the calculation of ethylene sensitivity showing ethylene
dose–response of basal root angles for whorls 1, 2 and 3 of a shallow genotype
(TLP19) grown in low phosphorus. Ethylene sensitivity was defined as the
slope of the ethylene response function. The basal root growth angle was
measured for the growth occurring between 24 and 48 h. Values shown are
means of basal roots of 5–7 plants per ethylene treatment ± s.e. Ethylene
and whorl significantly affected BRGA (F-values = 128 (ethylene) and 243
(whorl), P < 0.001). R2 values for linear functions were whorl 1 : 0.69, whorl
2 : 0.62; whorl 3 : 0.79.
30
20
high P
low P
30
10
high P
20
y = 0.4973x – 7.062
R 2 = 0.8042, P < 0.001
10
0
20
35
50
65
80
Growth angle (without ethylene)
0
Whorl 1 Whorl 2 Whorl 3
Shallow genotypes
Whorl 1 Whorl 2 Whorl 3
Deep genotypes
Fig. 7. Ethylene sensitivity of basal root growth angle as a function of
genotype, whorl and phosphorus treatment (low and high P) in three shallow
and three deep genotypes from the L88 population. Ethylene sensitivity was
measured as the slope of the response functions as illustrated in Fig. 6.
Statistical analysis corresponding to these data is shown in Table 3.
(up to 0.8 µL L−1 ), but this effect depended on the position
of origin (Table 3). The ethylene sensitivity of the growth
Fig. 8. Correlation between ethylene sensitivity and growth angle of basal
roots of six L88 genotypes grown in low (low P) and high (high P) phosphorus
treatments. Angles on the x-axis are of control plants without ethylene.
response was calculated as the slope of the dose–response
function (Fig. 9). Basal roots from whorl 3 were considerably
less sensitive to ethylene than roots originating from the upper
whorls. There was a small but significant phosphorus × whorl
interaction originating primarily from the greater ethylene
sensitivity of low-phosphorus roots from whorl 1 (Fig. 9).
48
Functional Plant Biology
Shallow genotypes
Ethylene sensitivity of growth response
(cm per µL L–1) (slope of the response curve)
whorl 1
whorl 2 whorl 3
P. Basu et al.
Deep genotypes
whorl 1
whorl 2
whorl 3
0
–0.08
Discussion
–0.16
–0.24
high P
low P
Fig. 9. Ethylene sensitivity of growth response of basal roots as a function
of genotype, whorl and phosphorus treatment (low and high P) in three
shallow and three deep genotypes from the L88 population. Growth was
measured between 24 and 48 h. Ethylene sensitivity was calculated as the
slope of the response curve (ethylene concentration v. growth).
Range of growth angle (degrees)
70
60
50
40
deep genotypes high P
deep genotypes low P
shallow genotypes high P
shallow genotypes low P
30
20
0
treatments (Fig. 10). Phosphorus deficiency reduced the range
of growth angles for the deep genotype only at high ethylene
concentrations (Fig. 10). Ethylene treatment resulted in a larger
increase in range of shallow genotypes than deep genotypes
(Fig. 10).
0.1
0.2
0.6
0.4
Ethylene concentration (µL L–1)
0.8
Fig. 10. Effect of exogenous ethylene on the range of growth angles of
three shallow and three deep genotypes from the L88 population grown
in low (low P) or high (high P) phosphorus. Angles were measured for
growth occurring between 0 and 48 h. The range of growth angles for each
plant was calculated by subtracting the minimum angle from the maximum
angle produced by the basal roots of each plant. Values shown are means
of the range of growth angles of 4–7 plants per genotype per ethylene
treatment ± s.e.
Shallow genotypes were somewhat less sensitive to ethylene
inhibition of growth than deep genotypes. The growth rate of
the basal roots showed a strong negative correlation with growth
angle (r2 = 0.51, P < 0.001 for treatments shown in Fig. 9).
When ethylene treatments were excluded, the correlation was
0.30 (P < 0.001).
As well as reducing growth and increasing basal root
angle, exogenous ethylene treatment increased the range of
growth angles of shallow genotypes under both phosphorus
The growth angle of basal roots is a primary determinant of the
vertical distribution of roots in soil (Bonser et al. 1996; Ge et al.
2000; Liao et al. 2001). Basal roots arise from a region of ∼1 cm
at the root–shoot interface. In this study we showed that basal
roots emerge from 2–3 distinct whorls in this region, and there is
genetic variation for whorl number and correspondingly, number
of basal roots (Fig. 2; Table 1). There are typically 3–4 basal
roots per whorl in the lower whorls and 2–3 basal roots in the
upper whorl. The diversity in root architecture of common bean
is generated partly by the variation in the number of basal roots
as well as by variation in the growth angles of basal roots.
Neither the number of basal roots nor the number of whorls
was affected by phosphorus availability, which is not surprising
because basal roots emerge while seedling growth is still
dependent on cotyledonary reserves. It is possible that maternal
nutrition affects these variables, but this was not tested in this
study. The seeds used in these experiments were produced in
fertilised fields.
In this study, we show that the position of root origin has
more influence on BRGA than the previously reported effects
of genotype and phosphorus availability. Within a root system,
basal roots grew at increasingly deeper angles from the upper
to the lower whorls (Figs 1, 2). Genotypes varied in both the
mean angle of growth from each whorl and the range of basal
root angles within root systems (Figs 2, 10; Table 2). Thus, the
distribution of basal roots within the soil would be skewed
towards shallower or deeper soil layers by larger or smaller
growth angles, and the vertical distribution would be greater in
genotypes with a larger range of angles (Table 2). A large range
of BRGA could be useful in environments when both shallow
resources, such as phosphorus, and deep resources, such as water,
are limiting (Ho et al. 2005).
Ethylene production did not explain variation in basal root
angles. Ethylene production was not correlated with BRGA, and
there was only a weak negative correlation between ethylene
production and basal root growth rate in the concentration range
used here. Neither ethylene production nor growth rates were
related to variation in growth angles among genotypes. Similarly,
earlier experiments on ethylene production from excised root
tips from eight genotypes grown for 6 days showed no significant
effect of phosphorus or genotype on ethylene production despite
large differences in growth angles (Zhang 2002).
Tissue sensitivity to ethylene seems to be far more important
in determining the BRGA than the amount of ethylene produced
by the basal roots. Ethylene and position of origin significantly
affected BRGA of two parent genotypes (Fig. 5). Detailed
studies of six L88 genotypes showed that ethylene sensitivity
(change in growth angle) was greater with low phosphorus
availability, in genotypes with shallower root systems, and in
roots from upper whorls (Figs 7, 8; Table 3). Thus, there was
a strong correlation between ethylene sensitivity and growth
Ethylene modulation of root plagiogravitropism
angle (Fig. 8), which supports the hypothesis that growth angle
might be partially regulated by ethylene, and that differences in
ethylene sensitivity may explain variation in growth angle with
whorl, genotype and phosphorus availability.
Basal roots from whorls 1 and 2 responded to ethylene by
reducing elongation (Fig. 9), a well-known root response to
ethylene (Abeles et al. 1992). However, the deepest, fastest
growing roots, which emerged from whorl 3, were remarkably
insensitive to ethylene inhibition of elongation (Fig. 9). The high
correlation between BRGA and root elongation rate suggests
that these processes are linked. This link is probably indirect,
because low phosphorus plants did not show consistently
higher ethylene sensitivity for elongation, but did for BRGA,
and the difference in ethylene sensitivity between shallow
and deep genotypes was larger for angle than for growth
(Figs 7, 9).
The mechanism by which ethylene modulates gravitropic
responses is unknown, but there are several possibilities.
The current conception of root gravitropism, based on
gravistimulation responses, is that gravisensing results from
perception of amyloplast movement within the columella cells
of the root cap, possibly by more than one mechanism
(LaMotte and Pickard 2004; Perrin et al. 2005). Long-term
exposure to ethylene reduces starch accumulation in the
columella cells (Guisinger and Kiss 1999), which would
reduce graviresponsiveness. Our seedlings were treated with
ethylene for both 24 and 48 h in separate experiments and we
observed no changes in root diameter or morphology, but since
ethylene treatment occurred during the early stages of basal
root development, relevant changes to root cap anatomy are
possible and have been observed in maize seedling roots treated
with the ethylene precursor 1-aminocyclopropane-1-carboxylic
acid (ACC) (Ponce et al. 2005). However, it seems more
likely that ethylene acts downstream of the initial gravisensing
mechanisms.
Gravisensing is followed by transmission of a signal from
the root cap to the elongating cells, which leads to differential
growth and bending (Boonsirichai et al. 2002; Blancaflor
and Masson 2003). Auxin transport is a key feature of the
downstream gravistimulation response, because lateral auxin
transport must be asymmetric for creation of the auxin gradients
responsible for differential growth, and longitudinal auxin
transport from the apex to the elongation zone is required
for root elongation (Perrin et al. 2005). Many studies have
shown ethylene inhibition of polar auxin transport in roots
and shoots (Beyer 1973; Lee et al. 1990; Sanyal and Bangerth
1998; Suttle 1988), which would retard gravitropic responses.
A recent study with gravistimulated Arabidopsis roots provided
a possible mechanism for ethylene inhibition of auxin transport:
stimulation of flavonoid synthesis (Buer et al. 2006). Flavonoids
reduce the rate of auxin transport (Jacobs and Rubery 1988;
Murphy et al. 2000; Brown et al. 2001). Treatment of
gravistimulated Arabidopsis plants with ACC delayed root
curvature and inhibited the transient burst of flavonoid
synthesis normally occurring 2 h after gravistimulation, but
from 4 h on, it increased flavonoid accumulation (Buer et al.
2006). ACC also accelerated the early stages of curvature
during gravistimulation response in flavonoid-deficient
mutants via an unknown mechanism (Buer et al. 2006). Even
Functional Plant Biology
49
in non-gravistimulated roots, as reported here, ethylene might
be regulating auxin transport via flavonoid synthesis, changing
the distribution of auxin and therefore the rates of root growth
and curvature. Other potential but less well established targets
of ethylene on auxin include altered IAA conjugation (Abeles
et al. 1992; De Paepe et al. 2004), and down-regulation
of auxin signal repressor proteins (De Paepe et al. 2004).
Ethylene may also influence gravifacilitation mechanisms
that have been proposed to account for plagiogravitropic
behaviour and some aspects of gravistimulation responses
(LaMotte and Pickard 2004).
The concentration of ethylene in soil varies with biological,
physical and chemical processes such as soil moisture, soil
organic matter, soil texture and soil temperature (Arshad and
Frankenberger 2002). Ethylene concentration varies with soil
depth, with greatest concentrations in the surface 10 cm of soil
(Campbell and Moreau 1979). Our measurement of ethylene
concentration in agricultural soils (typic hapludalfs) in central
Pennsylvania produced values ranging from undetectable to
1.08 µL L−1 in the root zone around bean plants. In some soils,
ethylene concentrations of up to 10 µL L−1 have been reported,
and stress conditions (e.g. nutrient stress, water logging,
flooding) may result in even higher concentrations (Abeles et al.
1992; Arshad and Frankenberger 2002). Soil ethylene could
be important in natural and agricultural ecosystems because
even low concentrations in the root zone could affect plant
growth and development. Our ethylene sensitivity study shows
that basal roots grow shallower even at very low ethylene
concentrations, 100–200 nL L−1 , whereas higher concentrations
have a larger effect on growth angle and also reduce basal root
elongation (Figs 7–9). Therefore, it is likely that ethylene in soil
has an important role in regulating root development, including
growth angle.
This report provides evidence that ethylene plays a significant
role in regulating root architectural responses to low phosphorus
availability. Both low phosphorus and ethylene affect root
traits likely to influence phosphorus acquisition and utilisation,
including aerenchyma formation, basal root growth angle, lateral
root density and root hair development (He et al. 1992; Lynch
and Brown 1997; Borch et al. 1999; Fan et al. 2003; Zhang
et al. 2003). In several cases, ethylene and phosphorus interact
in a manner that suggests ethylene mediation of responses to
low phosphorus availability. Ethylene action was required for a
subset of low-phosphorus-induced events leading to increased
root hair length and density in Arabidopsis, and ethylene
had different effects at high and low phosphorus availability
(Zhang et al. 2003). Likewise, in Arabidopsis primary roots, the
ethylene action inhibitor MCP increased cell elongation in the
growth zone of plants growth with high phosphorus but reduced
it when phosphorus availability was low (Ma et al. 2003).
In common bean, an ethylene synthesis inhibitor increased
main root elongation and reduced lateral root density under
high phosphorus availability, but did the opposite under low
phosphorus availability (Borch et al. 1999). Roots of 5-weekold common bean plants subjected to phosphorus deficiency
produced twice as much ethylene per unit dry weight as
roots supplied with adequate phosphorus (Borch et al. 1999).
We suggested that increased ethylene production and altered
ethylene sensitivity could play a significant role in root responses
50
Functional Plant Biology
to phosphorus deficiency (Borch et al. 1999). In the experiments
with much younger plants reported here, we did not observe
a significant effect of phosphorus treatment on endogenous
ethylene production in the basal rooting zone, but basal roots of
plants grown with low phosphorus maintained elongation rates
equivalent to phosphorus-treated plants, and these plants did not
manifest reduced phosphorus content at this early stage. Despite
this, plants grown with low phosphorus availability were more
responsive to increasing ethylene by producing shallower growth
angles than plants grown with high phosphorus (Fig. 7). Thus,
ethylene perception may mediate the regulation of BRGA by
phosphorus availability.
BRGA has important implications for resource acquisition.
Results from geometric modelling, growth studies in controlled
environments, and field experiments show that shallow-rooted
genotypes are better adapted to low phosphorus availability than
deep-rooted genotypes (Bonser et al. 1996; Liao et al. 2001,
2004; Ho et al. 2005; Zhu et al. 2005). Shallow basal roots not
only increase topsoil exploration, but produce less intraplant
and interplant competition for phosphorus (Ge et al. 2000;
Lynch and Brown 2001; Rubio et al. 2001, 2003). The results
reported here show that genotypic or low phosphorus-induced
increases in ethylene sensitivity of basal roots result in shallower
roots. This would be beneficial for phosphorus acquisition
by increasing topsoil exploration and reducing overlap of the
phosphorus depletion zones within a root system (Ge et al.
2000; Lynch and Brown 2001). Since ethylene is normally
present in soil, alteration in BRGA would be a typical feature
of field performance, with differential responsiveness based
on the genotype and position of origin, i.e. whorl. A second
effect of ethylene response in the field would be greater range
of growth angle of basal roots (Fig. 10). Under low phosphorus
availability, and especially in the presence of ethylene, shallow
genotypes produce more dispersed basal roots compared with
deep genotypes, which would facilitate efficient phosphorus
acquisition from the topsoil. Although basal roots from the
upper whorls would exploit upper soil horizons, basal roots
from lower whorls, which are less responsive to ethylene, would
grow progressively deeper and explore different soil domains.
This has important implications for water acquisition, which can
pose a problem for shallow-rooted genotypes (Ho et al. 2005).
A greater range of BRGA would increase the depth of soil
exploration and, therefore, the acquisition of heterogeneously
distributed resources, including phosphorus and water
(Ho et al. 2004).
Our results indicate that ethylene may be a modifier of root
responses to nutrient availability and that ethylene perception
may be a central aspect of the response of basal roots to low
phosphorus availability (Lynch and Brown 1997). In addition,
our study shows that the position of emergence of basal roots
plays a key role in determining the direction of plagiogravitropic
growth, and acts in concert with environmental cues such
as phosphorus and endogenous signals such as ethylene. The
observed variation in basal root growth angle within closely
related genotypes under phosphorus stress and in response to
ethylene increases the scope for selection and breeding of crops
with improved adaptation to low soil phosphorus availability,
an enterprise of considerable importance in global food security
(Lynch 2006).
P. Basu et al.
Acknowledgements
The authors gratefully acknowledge support from US-AID Bean-Cowpea
CRSP.
References
Abeles FB, Morgan PW, Saltveit ME (1992) ‘Ethylene in plant biology.’
(Academic Press Inc.: San Diego)
Arshad M, Frankenberger WTJ (2002) ‘Ethylene: agricultural sources and
applications.’ (Kluwer Academic: New York)
Beebe S, Lynch J, Galwey N, Tohme J, Ochoa I (1997) A geographical
approach to identify phosphorus-efficient genotypes among landraces
and wild ancestors of common bean. Euphytica 95, 325–336.
doi: 10.1023/A:1003008617829
Beyer E (1973) Abscission: support for a role of ethylene modification of
auxin transport. Plant Physiology 52, 1–5.
Blancaflor EB, Masson PH (2003) Plant gravitropism. Unraveling the ups
and downs of a complex process. Plant Physiology 133, 1677–1690.
doi: 10.1104/pp.103.032169
Bonser AM, Lynch J, Snapp S (1996) Effect of phosphorus deficiency on
growth angle of basal roots in Phaseolus vulgaris. New Phytologist 132,
281–288. doi: 10.1111/j.1469-8137.1996.tb01847.x
Boonsirichai K, Guan C, Chen R, Masson PH (2002) Root
gravitropism: an experimental tool to investigate basic cellular
and molecular processes underlying mechanosensing and signal
transmission in plants. Annual Review of Plant Biology 53, 421–447.
doi: 10.1146/annurev.arplant.53.100301.135158
Borch K, Bouma TJ, Lynch JP, Brown KM (1999) Ethylene: a regulator
of root architectural responses to soil phosphorus availability. Plant,
Cell and Environment 22, 425–431. doi: 10.1046/j.1365-3040.
1999.00405.x
Brown DE, Rashotte AM, Murphy AS, Normanly J, Tague BW, Peer WA,
Taiz L, Muday GK (2001) Flavonoids act as negative regulators of
auxin transport in vivo in Arabidopsis. Plant Physiology 126, 524–535.
doi: 10.1104/pp.126.2.524
Buer CS, Sukumar P, Muday GK (2006) Ethylene modulates flavonoid
accumulation and gravitropic responses in roots of Arabidopsis. Plant
Physiology 140, 1384–1396. doi: 10.1104/pp.105.075671
Campbell RB, Moreau RA (1979) Ethylene in a compacted field soil and its
effect on growth, tuber quality and yield of potatoes. American Potato
Journal 56, 199–210.
De Paepe A, Vuylsteke M, Van Hummelen P, Zabeau M, Van Der Straeten D
(2004) Transcriptional profiling by cDNA-AFLP and microarray
analysis reveals novel insights into the early response to ethylene in
Arabidopsis. The Plant Journal 39, 537–559. doi: 10.1111/j.1365313X.2004.02156.x
Edelmann HG (2002) Ethylene perception generates gravicompetence in
gravi-incompetent leaves of rye seedlings. Journal of Experimental
Botany 53, 1825–1828. doi: 10.1093/jxb/erf025
Edelmann HG, Gudi G, Kuhnemann F (2002) The gravitropic setpoint
angle of dark-grown rye seedlings and the role of ethylene.
Journal of Experimental Botany 53, 1627–1634. doi: 10.1093/
jxb/erf007
Fan MS, Zhu JM, Richards C, Brown KM, Lynch JP (2003) Physiological
roles for aerenchyma in phosphorus-stressed roots. Functional Plant
Biology 30, 493–506. doi: 10.1071/FP03046
Frahm MA, Rosas JC, Mayek-Perez N, Lopez-Salinas E, AcostaGallegos JA, Kelly JD (2004) Breeding beans for resistance to
terminal drought in the lowland tropics. Euphytica 136, 223–232.
doi: 10.1023/B:euph.0000030671.03694.bb
Ge Z, Rubio G, Lynch JP (2000) The importance of root gravitropism for
inter-root competition and phosphorus acquisition efficiency: results
from a geometric simulation model. Plant and Soil 218, 159–171.
doi: 10.1023/A:1014987710937
Ethylene modulation of root plagiogravitropism
Functional Plant Biology
Guisinger M, Kiss J (1999) The influence of microgravity and spaceflight on
columella cell ultrastructure in starch-deficient mutants of Arabidopsis.
American Journal of Botany 86, 1357–1366. doi: 10.2307/2656918
Harper RM, Stowe-Evans EL, Luesse DR, Muto H, Tatematsu K,
Watahiki MK, Yamamoto K, Liscum E (2000) The NPH4 locus encodes
the auxin response factor ARF7, a conditional regulator of differential
growth in aerial Arabidopsis tissue. The Plant Cell 12, 757–770.
doi: 10.1105/tpc.12.5.757
He CJ, Morgan PW, Drew MC (1992) Enhanced sensitivity to ethylene
in nitrogen-starved or phosphate-starved roots of Zea mays L. during
aerenchyma formation. Plant Physiology 98, 137–142.
Ho MD, McCannon BC, Lynch JP (2004) Optimization modeling of
plant architecture for water and phosphorus acquisition. Journal of
Theoretical Biology 226, 331–340. doi: 10.1016/j.jtbi.2003.09.011
Ho MD, Rosas JC, Brown KM, Lynch JP (2005) Root architectural tradeoffs
for water and phosphorus acquisition. Functional Plant Biology 32,
737–748. doi: 10.1071/FP05043
Jacobs M, Rubery PH (1988) Naturally occurring auxin transport regulators.
Science 241, 346–349. doi: 10.1126/science.241.4863.346
LaMotte CE, Pickard BG (2004) Control of gravitropic orientation. II. Dual
receptor model for gravitropism. Functional Plant Biology 31, 109–120.
doi: 10.1071/FP03089
Lee JS, Chang W, Evans ML (1990) Effects of ethylene on the kinetics of
curvature and auxin redistribution in gravistimulated roots of Zea mays.
Plant Physiology 94, 1770–1775.
Liao H, Rubio G, Yan XL, Cao AQ, Brown KM, Lynch JP (2001) Effect
of phosphorus availability on basal root shallowness in common bean.
Plant and Soil 232, 69–79. doi: 10.1023/A:1010381919003
Liao H, Yan XL, Rubio G, Beebe SE, Blair MW, Lynch JP (2004)
Genetic mapping of basal root gravitropism and phosphorus acquisition
efficiency in common bean. Functional Plant Biology 31, 959–970.
doi: 10.1071/FP03255
Lynch J (2006) Roots of the second green revolution. Australian Journal of
Botany (In press).
Lynch J, Brown KM (1997) Ethylene and plant responses to nutritional
stress. Physiologia Plantarum 100, 613–619. doi: 10.1111/j.13993054.1997.tb03067.x
Lynch J, van Beem JJ (1993) Growth and architecture of seedling roots of
common bean genotypes. Crop Science 33, 1253–1257.
Lynch JP (1995) Root architecture and plant productivity. Plant Physiology
109, 7–13.
Lynch JP, Brown KM (2001) Topsoil foraging – an architectural adaptation
of plants to low phosphorus availability. Plant and Soil 237, 225–237.
doi: 10.1023/A:1013324727040
Ma Z, Baskin TI, Brown KM, Lynch JP (2003) Regulation of root elongation
under phosphorus stress involves changes in ethylene responsiveness.
Plant Physiology 131, 1381–1390. doi: 10.1104/pp.012161
Madlung A, Behringer FJ, Lomax TL (1999) Ethylene plays multiple
nonprimary roles in modulating the gravitropic response in tomato.
Plant Physiology 120, 897–906. doi: 10.1104/pp.120.3.897
Murphy A, Peer WA, Taiz L (2000) Regulation of auxin transport by
aminopeptidases and endogenous flavonoids. Planta 211, 315–324.
doi: 10.1007/s004250000300
51
Murphy J, Riley JP (1962) A modified single solution method for the
determination of phosphorus in natural waters. Analytica Chimica Acta
27, 31–36. doi: 10.1016/S0003-2670(00)88444-5
Perrin RM, Young L-S, Narayana Murthy UM, Harrison BR, Wang YAN,
Will JL, Masson PH (2005) Gravity signal transduction in primary
roots. Annals of Botany 96, 737–743. doi: 10.1093/aob/mci227
Philosoph-Hadas S, Meir S, Rosenberger I, Halevy AH (1996) Regulation
of the gravitropic response and ethylene biosynthesis in gravistimulated
snapdragon spikes by calcium chelators and ethylene inhibitors. Plant
Physiology 110, 301–310.
Ponce G, Barlow PW, Feldman LJ, Cassab GI (2005) Auxin and ethylene
interactions control mitotic activity of the quiescent centre, root cap
size, and pattern of cap cell differentiation in maize. Plant, Cell and
Environment 28, 719–732. doi: 10.1111/j.1365-3040.2005.01318.x
Pothuluri J, Kissel D, Whitney D, Thien S (1986) Phosphorus uptake
from soil layers having different soil test phosphorus levels. Agronomy
Journal 78, 991–994.
Rubio G, Liao H, Yan XL, Lynch JP (2003) Topsoil foraging and its role in
plant competitiveness for phosphorus in common bean. Crop Science
43, 598–607.
Rubio G, Walk T, Ge ZY, Yan XL, Liao H, Lynch JP (2001) Root gravitropism
and below-ground competition among neighbouring plants: a modelling
approach. Annals of Botany 88, 929–940. doi: 10.1006/anbo.2001.1530
Sanyal D, Bangerth F (1998) Stress induced ethylene evolution and its
possible relationship to auxin-transport, cytokinin levels, and flower bud
induction in shoots of apple seedlings and bearing apple trees. Plant
Growth Regulation 24, 127–134. doi: 10.1023/A:1005948918382
Singh SP (1982) A key for identification of different growth habits
of Phaseolus vulgaris L. Annual Report of the Bean Improvement
Cooperative 25, 92–95.
Singh SP, Gepts P, Debouck DG (1991) Races of common bean (Phaseolus
vulgaris, Fabaceae). Economic Botany 45, 379–396.
Suttle JC (1988) Effect of ethylene treatment on polar IAA transport, net
IAA uptake and specific binding of N-1-naphthylphthalamic acid in
tissues and microsomes isolated from etiolated pea epicotyls. Plant
Physiology 88, 795–799.
Yan X, Beebe SE, Lynch JP (1995) Genetic variation for phosphorus
efficiency of common bean in contrasting soil types: II. Yield response.
Crop Science 35, 1094–1099.
Zhang YJ (2002) Ethylene and phosphorus responses in plants. PhD thesis,
Pennsylvania State University, PA.
Zhang YJ, Lynch JP, Brown KM (2003) Ethylene and phosphorus availability
have interacting yet distinct effects on root hair development. Journal
of Experimental Botany 54, 2351–2361. doi: 10.1093/jxb/erg250
Zhu JM, Kaeppler SM, Lynch JP (2005) Topsoil foraging and phosphorus
acquisition efficiency in maize (Zea mays). Functional Plant Biology
32, 749–762. doi: 10.1071/FP05005
Zobel RW (1986) Rhizogenetics (root genetics) of vegetable crops.
HortScience 21, 956–959.
Manuscript received 30 August 2006, accepted 17 November 2006
http://www.publish.csiro.au/journals/fpb
Breakthrough Technologies
A Novel Image-Analysis Technique for Kinematic Study
of Growth and Curvature1[W][OA]
Paramita Basu2, Anupam Pal, Jonathan P. Lynch, and Kathleen M. Brown*
Intercollege Program in Plant Biology, Pennsylvania State University, University Park, Pennsylvania 16802
(P.B., J.P.L., K.M.B.); and Department of Biological Sciences and Bioengineering, Indian Institute of
Technology, Kanpur 208016, India (A.P.)
Kinematic analysis has provided important insights into the biology of growth by revealing the distribution of expansion within
growing organs. Modern methods of kinematic analysis have made use of new image-tracking algorithms and computer-assisted
evaluation, but these methods have yet to be adapted for examination of growth in a variety of plant species or for analysis of
graviresponse. Therefore, a new image-analysis program, KineRoot, was developed to study spatio-temporal patterns of growth
and curvature of roots. Graphite particles sprinkled on the roots create random patterns that can be followed by image analysis.
KineRoot tracks the displacement of patterns created by the graphite particles over space and time using three search algorithms.
Following pattern tracking, the edges of the roots are identified automatically by an edge detection algorithm that provides root
diameter and root midline. Local growth rate of the root is measured by projecting the tracked points on the midline. From the
shape of the root midline, root curvature is calculated. By combining curvature measurement with root diameter, the differential
growth ratio between the greater and lesser curvature edges of a bending root is calculated. KineRoot is capable of analyzing a
large number of images to generate local root growth and root curvature data over several hours, permitting kinematic analysis of
growth and gravitropic responses for a variety of root types.
Detailed analysis of plant growth requires measurements that capture the large spatial and temporal heterogeneity of the expansion and differentiation of plant
organs. While measurement of the aggregate growth of
a plant organ provides important information, such as
overall growth rate and velocity, the spatial distribution of growth is not described by these measurements. A number of researchers have characterized
growth zones by employing kinematic analysis—an
aspect of study of dynamics of physical motion (e.g.
acceleration, velocity, etc.) without reference to the
forces resulting in the movement (Gandar, 1983). As
applied to plant growth, kinematics requires observation of the motion of discrete elements of an organ over
time, from which the velocity and acceleration of those
elements within a specified spatial context may be
quantified.
Kinematic analysis has been widely used to determine the growth profiles (Silk and Erickson, 1979) of
1
This work was supported by U.S. Agency for International
Development Bean/Cowpea Collaborative Research Support Program.
2
Present address: Department of Biological Sciences and Bioengineering, Indian Institute of Technology, Kanpur 208016, India.
* Corresponding author; e-mail [email protected].
The author responsible for distribution of materials integral to
the findings presented in this article in accordance with the policy
described in the Instructions for Authors (www.plantphysiol.org) is:
Kathleen M. Brown ([email protected]).
[W]
The online version of this article contains Web-only data.
[OA]
Open Access articles can be viewed online without a subscription.
www.plantphysiol.org/cgi/doi/10.1104/pp.107.103226
elongating plant organs, such as roots, stems, and
leaves, in which the spatial distribution of growth may
or may not be time dependent. More than six decades
ago, using a compound microscope, Goodwin and
Stepka (1945) measured cell division and the displacement of epidermal cells in Phleum roots at 30-s intervals in order to describe the processes of growth and
maturation. Later studies have combined measurement of incremental organ growth and increase in cell
length and cell number to define components of
growth and analyze the spatial distribution of elongation (Erickson and Sax, 1956; Goodwin and Avers,
1956; Bertaud et al., 1986; Ben-Haj-Salah and Tardieu,
1995; Beemster et al., 1996; Sacks et al., 1997; Beemster
and Baskin, 1998). In addition, relative elemental
growth rate, describing the instantaneous displacement
of points across a growing organ, has been analyzed
for the two-dimensional growth of leaves (Erickson,
1966). Kinematic analysis has been used to study the
influence of environmental factors on spatial and temporal growth patterns, e.g. effect of water stress (Sharp
et al., 1988; Fraser et al., 1990; Liang et al., 1997; Sacks
et al., 1997), shoot irradiance (Muller et al., 1998), and
temperature (Pahlavanian and Silk, 1988; Walter et al.,
2002) on maize (Zea mays) primary root elongation,
and influence of nitrogen supply (Gastal and Nelson,
1994) and water stress (Durand et al., 1995) on fescue
(Festuca spp.) leaf growth. Kinematic analysis has also
been employed to describe the influence of biotic
stress, such as aphid infestation, on elongation rate
of alfalfa (Medicago sativa) shoot (Girousse et al., 2005).
Recently, kinematic analysis has been used to analyze
the effect of phosphorus deficiency on the elongation
Plant Physiology, October 2007, Vol. 145, pp. 305–316, www.plantphysiol.org Ó 2007 American Society of Plant Biologists
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rate of the primary root of Arabidopsis (Arabidopsis
thaliana; Ma et al., 2003) and grass leaf growth (Kavanova
et al., 2006). Application of the kinematic approach in
such diverse studies shows the utility of this technique in
understanding the details of plant growth.
Various methods have been employed to visualize
the spatial patterns of expansion for distinct physical
elements of an organ (Erickson and Sax, 1956; Gandar,
1983). Many approaches involve marking the expanding regions of plant organs with ink, graphite particles,
charcoal particles, carbon-water slurries, and needle
holes, then measuring the displacement of the markers
over time (Selker and Sievers, 1987; Sharp et al., 1988;
Gould and Lord, 1989; Ben-Haj-Salah and Tardieu,
1995; Sacks et al., 1997; Beemster and Baskin, 1998;
Granier and Tardieu, 1998, 1999; Muller et al., 1998;
Hu et al., 2000). The displacement of these identifiable
markers on the surfaces of the growing organs can be
measured manually with a ruler or with a binocular
microscope, or by taking time-lapse photographs using still or video cameras (Sharp et al., 1988; Gould and
Lord, 1989; Bernstein et al., 1993; Ben-Haj-Salah and
Tardieu, 1995; Sacks et al., 1997; Beemster and Baskin,
1998; Granier and Tardieu, 1998, 1999; Muller et al.,
1998; Hu et al., 2000). More recently, instead of marking the growing organ, researchers have measured
spatio-temporal displacements of natural landmarks
such as vein structures on leaves (Schmundt et al.,
1998) or computationally discernible patterns on the
roots (van der Weele et al., 2003), and then applied
various methods of image analysis for quantification
of growth. Schmundt et al. (1998) used image sequence
analysis, which they termed optical growth analysis,
for measurement of growth in leaves of Ricinus communis and Nicotiana tabacum. They visualized leaf vein
structures using infrared light and then employed
computer-assisted image-analysis software based on a
structure-tensor approach (Jahne, 1997) to obtain highresolution growth maps of leaves. Their study resulted
in quantification of the actual growth rates and
changes in growth rates over time of the actively
expanding leaves. Later, this method was modified by
Walter et al. (2002), who applied automated image
sequence analysis for detailed study of relative elemental growth rate distribution of growing maize
primary roots influenced by variation in root temperature. Recently, van der Weele et al. (2003) introduced
a new computer-assisted technique that involved the
combination of two methods, the structure-tensor
(Jahne, 1997) and robust matching algorithms (Black
and Anandan, 1996), to measure the expansion profile
of a growing root at high spatio-temporal resolution.
They captured digital images of an Arabidopsis root at
5- or 10-s intervals, and nine consecutive images were
analyzed using the structure-tensor method to find a
line of minimum variation in pixel intensity and to
define the moving and static portions of the root. van
der Weele et al. (2003) used the robust matching
algorithm to improve the initial, structure-tensorbased estimates of velocity.
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In most of the studies discussed above, the primary
objective was to characterize the growth of a plant
organ. However, we wanted to characterize both root
growth and gravitropic curvature of the basal roots of
common bean (Phaseolus vulgaris) in response to gravity. Whereas one-dimensional kinematic study in the
direction of growth is sufficient for identifying and
characterizing the growth zones of the roots, at least
two-dimensional kinematic analysis is essential for
our purposes. It is necessary to examine root growth
and bending over a relatively long period (4–6 h) to
accommodate the time scales associated with changes
in growth angle of basal roots. The structure-tensor
method used by a number of researchers (Schmundt
et al., 1998; van der Weele et al., 2003) calculates local
root or leaf growth velocity with a high degree of confidence only if there are many high-contrast patterns,
which are lacking at the magnification required to
follow the growth of larger plant organs such as the
roots of most crop plants. In the absence of such patterns, the structure-tensor method can only produce a
very sparse velocity field with low confidence. Therefore, we developed a novel semiautomated imageprocessing system to analyze the gravitropic growth of
roots that takes advantage of patterns not only at a
pixel site but also in its neighborhood. As a result, the
new approach can generate reliable root growth data
even in regions where there are very low contrast patterns or no patterns as long as the neighborhood is
large enough to include identifiable patterns. This
approach is also particularly suitable for measuring the
two-dimensional growth velocity of the root for relatively longer times. Furthermore, this program automatically detects root edges, generating the root midline for
calculation of root curvature, diameter, and differential
growth ratio between two sides of a bending root.
RESULTS
Here, we briefly describe the image-analysis program
KineRoot for kinematic study of growth and gravitropism
of roots. The mathematical details of the algorithm are
provided in Supplemental Appendix S1. Although we
use the new technique primarily to analyze gravitropic
growth of basal roots of common bean, the approach
can also be applied to study kinematics of other root
systems. KineRoot was developed using Matlab 7.0
(The MathWorks). It features an easy-to-use graphical
user interface, shown in Figure 1. KineRoot allows
loading of a sequence of images (the number is limited
only by the computer’s memory), and then playing of
the images as a movie at desired speeds and moving
from one frame to another with the click of a mouse
button. Furthermore, by measuring the millimeter
marks on the ruler, KineRoot also allows easy spatial
calibration of the images from pixels to millimeters.
Image analysis by KineRoot is divided into two basic
steps.
Plant Physiol. Vol. 145, 2007
Kinematic Analysis of Root Growth and Curvature
Figure 1. Screen shot of the graphical user interface of the image-analysis software KineRoot.
Step 1: Tracking of Marker Points on the Root Images
From all the time sequence images loaded into KineRoot, the user selects an initial reference image that
shows the root tip and elongation zone most clearly. In
the reference image, the user selects a number of points
(generally 10–15) along the root with one point lying on
the root tip. The choice of points is arbitrary and
unrelated to natural features or added graphite, with
the only requirement being that they are chosen sequentially along the root. Then the user identifies the
point lying on the root tip. The user can either choose all
the points to be tracked by clicking the mouse on the
image, or select a few points and then use cubic spline
interpolation (Press et al., 1992) to generate the desired
number of marker points to be tracked by the software.
The marker points are then tracked in all other images
sequentially such that the patterns around a point have
the greatest similarity between two consecutive images. For tracking the points, a new highest correlation
coefficient search algorithm and its variations are used.
Highest Correlation Coefficient Search
This algorithm matches boxes of pixels between a
reference image and the current image irrespective
of whether the pixels are on the root or on the background. The image in which the points have been
tracked before the current image is used as the referPlant Physiol. Vol. 145, 2007
ence. For example, if the user selected the points in the
ith image, then, for tracking the interpolated points in
frames i 1 1 and i 2 1, the ith image is used as the
reference. Similarly, for tracking points in image i 1 2,
image i 1 1 is used as a reference, and, for tracking
points in image i 2 2, image i 2 1 is used as the
reference. Figure 2 schematically shows the patternmatching algorithm using the highest correlation
search method. The black circle in Figure 2A shows a
point (x0, y0) in the reference image that is being
searched for in the current image (Fig. 2B) based on
patterns within the gray square in Figure 2A. As the
root grows, the patterns separate from each other.
However, images captured at frequent intervals ensure that a high degree of similarity is maintained between consecutive images. KineRoot calculates the
correlation coefficient between the color intensities
of pixels in the gray square in Figure 2A and color
intensities of pixels from similar gray squares around a
predicted point in Figure 2B, such as the white circles.
This process of calculating the correlation coefficients
between color intensities of the pixels in the reference
image and the predicted image is repeated until the
correlation coefficient reaches its highest magnitude.
In Figure 2B, the white circle marked (x*, y*) shows the
most likely location of point (x0, y0) in Figure 2A. The
process ensures identification of the new locations of
the points based on highest similarity between the
patterns in two consecutive images, even if the points
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less space in the gray shaded box in Figure 2B, and,
therefore, the program will match patterns on the
germination paper rather than the root, causing inaccurate tracking. Since N 3 N pixels from each image
are correlated, minimizing N improves the speed of
tracking due to reduction of computational load.
Therefore, optimum choices of R and N are important
for both computational efficiency and accuracy of the
method.
To make the algorithm efficient, the operator can use
the velocity of the marker points to provide a better
prediction to the search algorithm and reduce the
search box size R. In Figure 2B, the dashed square of
size R 3 R pixels is centered on the point (x0, y0). But if
the velocity of the point (x0, y0) in Figure 2A is already
known, then one can predict the new location of this
point in Figure 2B, and, therefore, the dashed square
R 3 R can be drawn around the predicted location of
(x0, y0). This use of velocity of the individual points to
provide a better initial guess to the search algorithm
eliminates need for large R and reduces computational
load, making the tracking algorithm more efficient.
Use of estimated velocity for tracking can be toggled
on or off in the software.
Figure 2. Schematic showing the pattern-matching algorithm. The
white tubular shapes with black borders on the gray background show
the growing root. The black spots show patterns on the root. A shows
the reference image and B shows the current image. The black circle
with a white outline in A is the marker point (x0, y0), which is being
tracked in B. We chose all pixels within the gray square of N 3 N in A
and correlate those with the gray boxes in B. The search for the new
location of the marker point in B is restricted within the larger dotted
square R 3 R. When the N 3 N box is centered on the (x*, y *) in B, the
correlation with A is highest. But when the gray box is placed
elsewhere, the correlation coefficient between the N 3 N boxes in A
and B drops. Note that there is no requirement for the points to be on a
graphite particle for tracking.
are not located on a graphite particle or other surface
marker. The small arrow pointing from the black circle
to the white circle in Figure 2B shows the local root
growth velocity with respect to the fixed germination
paper background.
The user specifies the size of the square N within
which pixels are correlated between two images (Fig.
2A) and the search box size R within which KineRoot
searches for the new location of the points (Fig. 2B).
The amount of computation necessary to track a point
depends on the search box size R and pixel box size N.
Since search for the new location of a tracked point is
limited by the size of R, it is necessary that R is larger
than the displacement distance of any marker point
between two consecutive images. However, selecting
an overly large value of R unnecessarily increases the
computation without any benefit. Larger values of N
match patterns over a larger area, increasing the
accuracy of tracking to a certain extent. However, at
very high values of N the root will occupy relatively
308
Highest Color-Weighted Correlation Coefficient
Search Algorithm
Although the highest correlation search method
worked in more than 70% of our experiments, if the
root grew into an area where the background (in this
case the germination paper texture) was very different
from the reference image, the algorithm had more
difficulty tracking the points accurately.
To overcome this problem, we introduced a weighing factor w, based on the color of the pixel, into the
calculation of correlation coefficient. The user selects a
small area of the image covering only the root and then
another area covering only the background. Color
intensities of red, green, and blue channels from each
of these areas are averaged and stored as root color
(Rr, Gr, Br) and background color (Rb, Gb, Bb), where
R, G, and B are the intensities of red, green, and blue,
respectively, and range between 0 and 1. Figure 3
shows a schematic for calculation of the weighing
factor w. If the difference in intensity of any color
between the root and the background is less than 0.2,
the weighing factor w is assigned a value of 1 (e.g. the
dashed line in Fig. 3 labeled ‘‘Blue’’); otherwise, the
weighing factor is calculated by linear interpolation
for pixels with color intensity between that of the root
and the background. If the color intensity is outside
the root and background color intensity range, w is
assigned a value of 1 or 0 depending on proximity to
the root color or background color, respectively. The
color-based weighing factors reduce the importance of
the pixels from the background in calculating the
correlation coefficients between two boxes of pixels.
As a result, even if the appearance of the background
changes drastically, the software is able to track points
Plant Physiol. Vol. 145, 2007
Kinematic Analysis of Root Growth and Curvature
Figure 3. Schematic showing the weights for calculating colorweighted correlation coefficients based on color of the pixel and sampled colors of the root (Rr , Gr , Br) and the background (Rb, Gb, Bb). The
red, green, and blue labeled lines show the weighting factors for the
corresponding colors. If the difference in color intensity between the root
and the background is less than 0.2, weighting factor is assigned a value
of 1; otherwise, weighting factor w is calculated by linear interpolation
for a pixel whose color intensity lies between that of the root and the
background. If the color intensity of a pixel is outside this range, a value
of 1 or 0 is assigned based on the proximity to the root color or background color, respectively.
on the root reliably. It should be noted that in case of
low contrast images, where the intensity difference between the root and background is less than 0.2 for all
three colors, the weighing factor becomes 1. As a result,
the color-weighted highest correlation search method
changes to the highest correlation search method described in the previous section.
Using Tracking History
In addition to the methods described above, we also
employed a variation where instead of using the previous image as the only reference, the user could include more images, including the one where the user
first selected the points as reference. In the absence of
history tracking, if there are 50 images and the user
chooses the 35th image to select the points, then the
35th image will be used as reference for locating the
points on the 34th image, the 34th image will be used
as a reference for the 33rd image, and so on. However,
with history tracking the user could also use other
images where points have already been tracked as a
reference also, e.g. for the 22nd image the reference
could include the 23rd, 24th, 25th, and the initial reference image (in this example, the 35th image). KineRoot calculates a weighted average of the correlation
coefficients, putting greater weight on images with
closer proximity in time to the current image and
progressively lesser weight on the images that are
Plant Physiol. Vol. 145, 2007
further away from the current image. Then this average correlation coefficient is used for finding the most
likely position of a marker point.
Apart from the maximal correlation search method,
KineRoot can also use a simpler approach for straight
roots by searching for the minimum pixel intensity
difference. Further details on this approach are provided in Supplemental Appendix S1.
The tracking methods described above have different computational loads. Since our objective is to track
marker points reliably with the minimum possible
computation, the methods are ranked and chosen according to decreasing computational efficiency in the
following order: minimum pixel intensity difference
search method, highest correlation coefficient search
method, highest color-weighted correlation coefficient
search method, combination of difference and correlation search methods, and correlation search with tracking history method. After tracking the marker points,
the algorithm for each method provides a confidence
measure of marker tracking, and, if the confidence
measure is too low, KineRoot suggests that the user
use the next tracking method with a higher computational load. For the correlation coefficient search
method, the minimum of the highest correlation coefficients for tracking all marker points in all frames
provides the confidence measure F 5 Cmin. A threshold
value of confidence F 5 0.8 was used before moving to
the next method.
Step 2: Automatic Edge Detection and Finding the
Midline of the Roots
Once the marker points are tracked along the root,
KineRoot finds the root centerline and projects these
points on the midline to estimate root growth. To
identify the root midline, the edges of the root are
identified in each image. An ‘‘edge’’ in an image is
defined as a line at which the gradient of color intensity has a local peak. However, quite often the edge
cannot be accurately identified by highest magnitude
of the derivative of the pixel intensities directly because of noise in the image or blurriness at the edge.
Many methods have been developed for automatic
detection of edges from digital images (Prewitt, 1970;
Sobel, 1978; Canny, 1986). Among these methods, one
of the most popular is the edge detection algorithm by
Canny (1986). The Canny algorithm has three steps, of
which we use two and replace the third step with a
simpler method by customizing for the specific characteristics of root images. The steps of edge detection
are shown in Figure 4.
Noise Smoothing and Image Gradient
Since an edge is identified by a sudden change in
color within a span of a few pixels, i.e. a strong color
gradient, it is important to ensure that the strongest
color gradients of the image do not reflect either noise
or the dark graphite particles on the image. Therefore,
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Basu et al.
Edge Finding
Although the Canny edge detection algorithm has
one more step in which the edge points are linked
together to generate the final edge, we apply an easier
approach knowing that the roots have tubular shape
and the edges can be found if we move perpendicular
to the lines joining the tracked points. However, there
could be another root near the edge that can be picked
erroneously by the computer. To prevent this error, the
user measures the approximate root diameter, which is
then used as the search radius for finding the root edge
from the non-maxima suppressed image gradient (Fig.
4E). Figure 4F shows the final edge-detected image,
where both upper and lower edges are outlined with
thin white lines.
Root Midline Identification
Figure 4. Steps of automatic edge detection: A, two-dimensional
Gaussian filter; B, close-up image of a basal root; C, basal root image
after noise smoothing by convolution with the Gaussian filter; D,
magnitude of the gradient of the smoothed image showing blurry edges;
E, edge enhanced by non-maxima suppression; F, detected upper and
lower edges of the root and the centerline shown by white lines.
before detecting the edge of the root, noise is smoothed
by convolving the image with a Gaussian filter (Fig.
4A). Figure 4B shows the image before convolution,
and Figure 4C shows the smoothed image after convolution with the Gaussian filter.
Edge Enhancement
In this step the magnitude of the color intensity
gradient of the image is calculated (Fig. 4D). To obtain
the best estimate of the root edge, it is important to
use the maximum available contrast between the root
and the background. For our experiments the background germination paper is blue, whereas the root
color is light gray. When we compared the individual
red, green, and blue colors between the root and the
background, we found that instead of averaging all
three colors, the red color produced the highest contrast, whereas the blue color had the least contrast.
Therefore, for edge detection in our experiments, best
results were obtained using the intensity of red color of
the pixels. However, KineRoot allows the user the flexibility of choosing how to calculate the color gradient.
Figure 4D shows that although the gradient identifies
the edges, the peak gradient corresponding to the edge
spreads over more than one pixel width, resulting in a
smudged edge. To identify the true edge in the image,
the Canny edge detector identifies the local maxima
along the edge and suppresses all other high gradient
values in the image (Fig. 4E), resulting in edges that
are one pixel wide.
310
By taking the average of both upper and lower
edges, we can also identify the root midline, which is
shown by the thick white line in Figure 4F. To get an
accurate estimate of the root midline by averaging the
root edges even for highly curved roots, the points are
selected through an iterative algorithm that ensures
that the radial lines connecting any pair of edge points
are locally perpendicular to the root midline. The
details of the root midline identification algorithm are
provided in the Supplemental Appendix S1.
Measurements
Once the root midline is found, we project the
tracked marker points on the midline (i.e. drop perpendicular on the midline) and measure the distance
Sp of the pth point from the root tip along the midline of
the root as shown in Figure 5A using the following
equation.
p qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
2
2
ð1Þ
Sp 5 + ðxi 2 xi 21 Þ 1 ðyi 2 yi 21 Þ
i52
For our subsequent measurements, we use Sp to
compute root growth velocity and relative elongation
rate. In addition, we also directly measure the root diameter D at any point along the root length. Figure 5B
shows the schematic of the space-time mapping of
marker points where distance of the marker points
from the root tip is along the vertical axis and time is
on the horizontal axis. Note here that since we use the
root tip as our spatial reference, it is held fixed. The
region where the distance between consecutive marker
points changes more rapidly over time than other areas
along the root identifies the growth zone (Fig. 5B).
Knowing the distance of the tracked points from
root tip allows us to calculate root growth velocity as a
function of distance from the root tip and time. If a
point p is located at Sp distance from the root tip at time
t and after dt time it moves to Sp 1 dSp distance from
the root tip, then the growth velocity of the point p is as
follows.
Plant Physiol. Vol. 145, 2007
Kinematic Analysis of Root Growth and Curvature
from the root tip, a line locally perpendicular to the
root midline is drawn. The distance between the two
points of intersection of the two edges with this perpendicular line is the root diameter at distance s from
the root tip. As a root bends toward gravity, one side of
the root grows more than the other side. Therefore, the
ratio of arc lengths along the two edges of the root can
be used to characterize graviresponse of a root. Following Silk and Erickson (1978), the differential growth
ratio of two arcs of length dsu and dsl on the upper and
lower edges of an element of a bending root is calculated by the following equation.
dsu
2 1 kd
5
2 2 kd
dsl
ð5Þ
Example Measurements
Figure 5. A, Schematic showing projection of tracked points on the
root centerline. Distance of the projected tracked points from the root
tip Sp is measured along the root centerline. From the detected root
edge, we also measure the root diameter D as a function of distance
from the root tip and time. B, Schematic showing the spatio-temporal
trajectory of the tracked points. The region where the gap between the
points increases rapidly with time identifies the growth zone.
In this section we present representative measurements from one bean basal root to demonstrate the
performance of KineRoot and the typical results obtainable from it. Figure 6 shows an example of marker
point tracking and automatic edge detection using a
montage of eight images of basal roots. The images
shown in Figure 6 are at 90-min intervals from a
dSp
ð2Þ
dt
The relative elongation rate describes the rate of relative growth of a small segment of the root over a short
time where a root segment of length l 5 Sp 2 Sp21 grows
to l 1 dl over time dt. Therefore, relative elongation rate
is as follows.
dl
ð3Þ
r5
ldt
Relative elongation rate r(s, t) can also be calculated
by taking the derivative of the root growth velocity
u(s, t) with respect to distance from the root tip s (Silk
and Erickson, 1978; Taiz and Zeiger, 1998).
Since we are also interested in bending of the roots,
one of the important parameters to calculate from
image analysis is the root curvature. Curvature is the
reciprocal of radius of curvature, i.e. the radius of a
circle that matches the curve at a point (x, y), and is
given by
Up 5 Up ðSp ; tÞ 5
2
dy
2
dx
k5 "
2 #3=2 ;
dy
11
dx
ð4Þ
where y 5 y(x) is the equation that describes the root
midline. To calculate the root diameter d at distance s
Plant Physiol. Vol. 145, 2007
Figure 6. Montage of eight images of a bean basal root. The images are
at 90-min intervals from a sequence of 72 images originally captured at
5-min intervals. The images on the left show the patterns on the root
generated by graphite particles, whereas the images on the right show
the tracked marker points and the root edges on the same images of the
left. The upper and lower edges of the growing root are detected by
KineRoot, and the bold white line shows the root midline. The black
dots show the tracked points.
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Basu et al.
sequence of 72 images originally captured at 5-min
intervals. The images on the left show the patterns on
the root generated by graphite particles, whereas the
images on the right show the tracked marker points
and the root edges on the same images as on the left.
The 2-d-old seedling with emerging basal roots was
grown in growth pouch in nutrient solution (see
‘‘Materials and Methods’’). The images were captured
beginning 36 h after the emergence of the basal roots.
The black dots are the marker points selected by the
user at 120 min and tracked in other frames by
KineRoot using highest correlation search method.
Note that after the user selected the marker points,
they were interpolated to generate a total of 25 points
that are tracked in all frames. To avoid crowding of the
points, here we only show 14 points selected by the
user. After the marker points were tracked, edges of
the root were identified by edge detection. The average
of the root edge lines generates the root midline, which
is shown by the bold white line. The root tip is
identified by the asterisk symbol. The marker points
were projected on the midline to calculate distance Sp
from the root tip along the midline.
As the root grows, the marker points move away
from each other (Fig. 6). The rate at which points move
away from each other defines the growth zones of the
root. In Figure 7, the top-most line (3.5 mm at time
0 min and 7 mm at 355 min) shows overall growth of
the selected root segment. The points located between
0.8 and 2.2 mm from the root tip at time 5 0 separated
more than points in other regions of the root; this is the
rapid elongation zone of the root.
Figure 8A shows the growth velocity of tracked
markers from a single root as a function of distance
from the root tip. The gray dots in Figure 8A show the
growth velocity of all marker points from 72 images
taken over a period of 6 h at 5-min intervals. The superimposed bold line is the mean growth velocity after
grouping the data in bins of 0.5 mm. The raw data
from KineRoot form a clustered group showing the
Figure 7. Root length map showing the growth of the root by plotting
distance of the marker points from the root tip along the root midline at
5-min time intervals.
312
Figure 8. A, Root growth velocity plotted as a function of distance from
the root tip. The gray dots show the growth velocity of 25 tracked points
in 72 frames. The bold line shows the average growth velocity after
grouping the data in bins of 0.5 mm. The vertical bars are 6 1 SD. B,
Mean relative elongation rate plotted against distance from the root tip
with SD error bars.
robustness of the algorithm. The velocity profile shows
the typical sigmoid shape and is comparable to results
of other kinematics techniques (e.g. Sharp et al., 1988;
Fraser et al., 1990; Sharp et al., 2004). The plot of mean
relative elongation rate as a function of distance from
the root tip (Fig. 8B) shows that the growth zone spans
up to 6 mm from the root tip.
To show the versatility of the software in handling
the images of different types of roots, we also analyzed
the growth velocity and relative elongation rate of Arabidopsis primary root. Gray-scale images of Arabidopsis primary root were collected by using a compound
microscope with infrared light and without marking.
Figure 9A shows the velocity profile of the primary
root measured as a function of distance from the root
tip. The image at the top of Figure 9A shows the
primary root of Arabidopsis from which the mean
velocity profile was calculated. The thin wiggly line in
Figure 9A shows the growth velocity obtained through
tracking of 500 marker points along the root. The solid
black line shows smoothed growth velocity plot obtained using the method of overlapping polynomials.
Plant Physiol. Vol. 145, 2007
Kinematic Analysis of Root Growth and Curvature
almost constant at 1 mm from the root tip, but the
distal end of the growth zone expands, lengthening
the growth zone. In addition, the rate of elongation
also increases with time as shown by the large red
region beyond 270 min compared to mostly green
elongation zone before that. The isocontour plot illustrates the dynamism of the developing growth zone.
Detection of root edges also allows us to measure root
diameter in space-time coordinates. Figure 11 shows the
time-averaged root diameter as a function of distance
from the root tip. The diameter of the root near the tip is
minimum and reaches a nearly constant magnitude at
about 1.5 mm from the root tip. The small error bars in
Figure 11 show that as the root grows by about 3.5 mm
in length over 6 h, the root diameter remains nearly
constant.
Root graviresponse or curvature can be described by
KineRoot as curvature of the root midline (Fig. 12A) or
as the differential growth ratio between two edges of
the root (Fig. 12B). Positive curvature and a differential
growth ratio greater than 1 indicate downward bending, and negative curvature indicates upward bending. In this case, we have presented the very small
change in growth direction of a plagiogravitropic bean
basal root in the absence of gravistimulation, i.e. these
data are for the small changes in direction accompanying normal plagiogravitropic growth. Although the
curvature and the differential growth ratio are very
small in this example (the upper edge of the root grew
2%–4% more than the lower edge in 6 h), KineRoot
was able to quantify this difference and detect two
regions of bending, the apical bending zone spanning
1 to 3.5 mm from the root tip and the distal bending
zone spanning 3.5 to 5.5 mm from the root tip.
Figure 9. A, Root growth velocity of Arabidopsis primary root plotted
as a function of distance from the root tip. Thin wiggly lines represent
growth velocity data obtained from tracking of 500 marker points, and
solid black line is the smoothed root growth velocity profile. B, Relative
elongation rate calculated from the derivatives of smoothed velocity
profiles. The image of the root from which the velocity profile was
obtained is shown at the top.
Figure 9B shows the profile of relative elongation rate,
i.e. the derivative of the smoothed growth velocity
data in Figure 9A. The data represented in Figure 9
show average growth velocity and relative elongation
rate calculated from nine frames. Second-order finite
difference method was used for calculating derivatives
to estimate both growth velocity and relative elongation rate.
A color isocontour plot shows relative elongation
rate of bean root as a function of distance from the root
tip and time, i.e. spatio-temporal variation in relative
elongation rate (Fig. 10). The isocontour plot is generated using Matlab 7.0 through KineRoot’s interface.
The length of the growth zone increases with time
from approximately 1.5 mm (1–2.5 mm from root tip)
at 60 min to 4 mm (1–5 mm from root tip) at 350 min.
The apical boundary of the growth zone remains
Plant Physiol. Vol. 145, 2007
DISCUSSION
This study presents semiautomated image-analysis
software, KineRoot, for kinematic analysis of root
Figure 10. Colored isocontour plot of the rate of relative elongation
plotted as a function of distance from the root tip and time. Reds,
oranges, and yellows show high rate of elongation, whereas light and
dark blues show low/zero rate of elongation.
313
Basu et al.
Figure 11. Mean root diameter plotted as a function of distance from
the root tip. The vertical bars show 6 1 SE. Where bars are not visible,
the SE is less than the size of the symbol.
growth and graviresponse. This method is suitable for
larger-rooted species, such as crop plants, as well as
for small-rooted plants, and can monitor growth over
several hours. As an example, we present analysis of
common bean basal root growth and graviresponse.
Common bean basal roots were 0.4 to 1 mm in diameter and 10 to 20 mm long at the onset of the study, and
grew at rates of 0.8 to 1.2 mm/h. Since these roots are
devoid of patterns permitting spatio-temporal tracking at suitable magnification, we sprinkled graphite
particles to add patterns to the root for tracking by
KineRoot. Although use of ink or graphite particles as
markers has been used before (Erickson and Sax, 1956;
Sacks et al., 1997; Beemster and Baskin, 1998; Muller
et al., 1998), the process was tedious. Clearly visible
markers had to be added very carefully for tracking
because mechanical stimulation can damage roots and/
or alter root growth. However, in KineRoot the computer matches patterns within boxes of pixels surrounding a marker, so there is no need for any
particular type or placement of markers on the roots,
and any point on the root can be used as a marker even
if there is no graphite particle exactly at that point, as
long as there are some uniquely identifiable color
patterns around the roots. As a result, KineRoot is more
suitable for kinematic study of a large number of roots
with minimal user interventions. Furthermore, the
method of pattern matching allows us to track the
marker points on the roots for extended periods, even
if the roots deviate from a straight trajectory.
The existing algorithms based on the structuretensor method (Schmundt et al., 1998; van der Weele
et al., 2003) search for a path of minimum pixel intensity difference in a stack of seven to nine images to
generate the velocity field of the plant organ. Therefore, in any portion of the plant organ where there are
very few patterns, this method cannot generate velocity with sufficient confidence, and as a result produces
a velocity field that is very sparse. In a growing root,
314
it is the zone of interest (the growth zone) that becomes less populated with patterns with time, and
the structure-tensor method generates very few highconfidence velocity measurements there. Since KineRoot not only matches patterns at a pixel site but also
from its neighboring sites, even when the patterns
expand within the growth zone, KineRoot can track
marker points with high confidence based on patterns
in the neighboring pixels.
Our analysis of growth velocity and relative elongation rate shows that KineRoot can also be used to
analyze the images of different types of roots, such as
relatively large roots of common bean and small roots
of Arabidopsis. KineRoot automatically tracks the
marker points and detects edges of the roots, generating reliable growth data. The growth velocity data
generated by KineRoot (Figs. 8 and 9) match the description of root growth found in the literature (Taiz
and Zeiger, 1998). The growth zone of roots can be
divided into two main regions, the meristem (zone of
cell division) and zone of rapid elongation. As the cells
divide, they successively pass through the elongation
zone and to the maturation zone, where growth ceases
as cells become mature with differentiated characteristics (Dolan et al., 1993; Taiz and Zeiger, 1998). The
Figure 12. A and B, Mean root curvature (A) and differential growth
ratio (B) between the upper and lower sides of the root plotted as a
function of distance from the root tip. Positive curvature and differential
growth ratio greater than 1 indicate downward bending and vice versa.
The vertical bars indicate SE.
Plant Physiol. Vol. 145, 2007
Kinematic Analysis of Root Growth and Curvature
rate of root elongation is regulated by the combined
effects of cell production in the meristem and cumulative cell expansion in both meristem and growth
zone (Beemster and Baskin, 1998). Since individual
cells are not visible in images collected for KineRoot
analysis of common bean, it is not possible to directly
measure the cell production in the meristem. Our
analysis of a bean root shows that the relative elongation rate is not quite zero close to the root tip (Fig. 8B),
reflecting the expansion of meristem cells. The plot of
relative elongation rate of an Arabidopsis root, which
is to a finer scale, shows a small (,300 mm) zone at
the site of the apical meristem with nearly flat relative
elongation rate (Fig. 9B). The relative elongation rate
and velocity profile of an Arabidopsis primary root
obtained using KineRoot matches closely with the
output from a structure-tensor method, RootFlowRT
(T. Baskin, personal communication; RootFlowRT described in van der Weele et al., 2003).
Color isocontour plotting shows the variation in
relative elongation rate as a function of both space
and time (Fig. 10). This type of representation of
bivariate data allows easy identification of spatiotemporal patterns of growth of the basal roots. The
spatio-temporal isocontour plot of relative elongation
rate (Fig. 10) also explains the large SDs in Figure 8B.
Since the length of the growth zone as well as rate of
elongation change with time, grouping data from the
entire duration of the experiment introduces variability, resulting in large SD in mean relative elongation
rate (Fig. 8B).
Identification of the root edge allows us to not only
locate the root midline but also measure the root
diameter. In this example, the root diameter remained
nearly constant during the nearly 6-h test period,
whereas root length grew by 3.5 mm (Fig. 11). The
diameter function would be useful under situations
such as drought, when root radial expansion is reduced throughout the growth zone (Sharp et al., 1988).
KineRoot measures the distribution and extent of
root curvature as well as root elongation, permitting
detailed analysis of gravitropism and other responses
resulting in changes in the direction of growth. The
root midline was used to estimate the curvature of the
root as it grew (Fig. 12A). When combined with root
diameter, root curvature can also be used to calculate
differential growth ratio (Fig. 12B) between two sides
of a bending root because a root can only bend if one
side grows more than the other side. In this case, since
the bending of the root was minimal, the differential
growth ratio was also minimal with the upper edge
growing 2% to 4% more than the lower edge of the
root. The program was able to quantify even very
small and temporary growth differentials.
Our approach of nearly automatic image analysis
and measurement using colored images provides a
new tool for application of kinematic techniques to the
analysis of spatio-temporal growth of plant organs
over long time spans as long as there are discernible
patterns in the images for tracking on the organ.
Plant Physiol. Vol. 145, 2007
MATERIALS AND METHODS
Experimental Method
Common bean (Phaseolus vulgaris) genotype TLP19 developed at the
International Center for Tropical Agriculture (Cali, Colombia) was employed
for this study. Seeds were surface sterilized with 6% sodium hypochlorite for
5 min, rinsed thoroughly with distilled water, and scarified with a razor blade.
Seeds were germinated at 28°C in darkness for 2 d in rolled germination paper
(25.5 3 37.5 cm; Anchor Paper Co.) moistened with nutrient solution, which
was composed of (in mM) 3,000 KNO3, 2,000 Ca(NO3)2, 1,000 NH4H2PO4,
250 MgSO4, 25 KCl, 12.5 H3BO3, 1 MnSO4, 1 ZnSO4, 0.25 CuSO4, 0.25
(NH4)6Mo7O24, and 25 Fe-Na-EDTA. Germinated seeds with radicles approximately 2 to 3 cm long were transferred to a sheet of 30- 3 24-cm blue
germination paper (Anchor Paper Co.) stiffened by attaching perforated
plexiglass sheets to stabilize the root system. The bottom of the blue paper
with plexiglass was placed to allow direct contact with the nutrient solution.
The germination paper containing a seedling was suspended in nutrient
solution and covered with aluminum foil to prevent illumination of the roots.
Graphite particles sprinkled on the roots created patterns on the otherwise
uniformly colored root that could be followed in image analysis. A small
amount of graphite powder was drawn into a dropper fitted with a pipette tip
and then blown on the roots from close proximity. During this procedure care
was taken to not touch the roots or change the orientation of the seedling with
respect to the gravity. A pouch containing one seedling was placed in a watersealed plexiglass box maintained at 25°C to 26°C. Seedlings were photographed from outside the plexiglass box. Images of root systems were
captured for 4 to 6 h at fixed intervals (5 min) using a high-resolution (6
Megapixel) digital single-lens reflex camera (Nikon D70s) fitted with 105-mm
Nikkor micro lens, beginning 1 d after emergence of basal roots in pouches.
The camera was triggered at fixed intervals by a laptop computer through a
universal serial bus cable using the software Nikon Capture 3.5. The resolution of the captured images was 10 to 20 mm pixel21. Except for the use of the
camera’s flash for image capture, plants were grown in complete darkness to
minimize light exposure of the roots. To avoid shadows from direct flash,
which interferes with image analysis, light from two flashes was bounced off a
sheet of white paper placed on top of the plexiglass box. The flashes were
wirelessly triggered by the built-in flash of the Nikon D70s camera. A ruler
was attached to the supporting plexiglass sheet for calibrating pixel dimensions into millimeters.
Arabidopsis (Arabidopsis thaliana) images were obtained from Dr. Tobias
Baskin, University of Massachusetts, Amherst, MA.
The KineRoot program is available for downloading from Dr. Anupam Pal
([email protected]). Since the software is built using Matlab 7, the user must have
Matlab to use the software. KineRoot is compatible with Windows, Linux, and
Unix versions of Matlab.
Supplemental Data
The following materials are available in the online version of this article.
Supplemental Figure S1. Illustration of the algorithm used for finding the
midline of the root.
Supplemental Appendix S1. Mathematical details of the new imageanalysis program KineRoot.
ACKNOWLEDGMENT
We gratefully acknowledge Dr. Tobias Baskin for providing the image of
the primary root of Arabidopsis shown in Figure 9.
Received June 1, 2007; accepted August 13, 2007; published August 24, 2007.
LITERATURE CITED
Beemster G, Baskin T (1998) Analysis of cell division and elongation
underlying the developmental acceleration of root growth in Arabidopsis
thaliana. Plant Physiol 116: 1515–1526
Beemster GTS, Masle J, Williamson RE, Farquhar GD (1996) Effects of soil
resistance to root penetration on leaf expansion in wheat (Triticum
315
Basu et al.
aestivum L.): kinematic analysis of leaf elongation. J Exp Bot 47:
1663–1678
Ben-Haj-Salah H, Tardieu F (1995) Temperature affects expansion rate of
maize leaves without change in spatial distribution of cell length
(analysis of the coordination between cell division and cell expansion).
Plant Physiol 109: 861–870
Bernstein N, Lauchli A, Silk WK (1993) Kinematics and dynamics of
sorghum (Sorghum bicolor L.) leaf development at various Na/Ca
salinities (I. Elongation growth). Plant Physiol 103: 1107–1114
Bertaud DS, Gandar PW, Erickson RO, Ollivier AM (1986) A simulation
model for cell proliferation in root apices. I. Structure of model and
comparison with observed data. Ann Bot (Lond) 58: 285–301
Black MJ, Anandan P (1996) The robust estimation of multiple motions:
parametric and piecewise-smooth flow fields. Comput Vis Image Underst
63: 75–104
Canny J (1986) A computational approach to edge detection. IEEE Trans
Pattern Anal Mach Intell 8: 679–698
Dolan L, Janmaat K, Willemsen V, Linstead P, Poethig S, Roberts K (1993)
Cellular organisation of the Arabidopsis thaliana root. Development 119:
71–84
Durand J-L, Onillon B, Schnyder H, Rademacher I (1995) Drought effects
on cellular and spatial parameters of leaf growth in tall fescue. J Exp Bot
46: 1147–1155
Erickson RO (1966) Relative elemental rates and anisotropy of growth in
area: a computer programme. J Exp Bot 17: 390–403
Erickson RO, Sax KB (1956) Rates of cell division and cell elongation in the
growth of the primary root of Zea mays. Proc Am Philos Soc 100: 499–514
Fraser TE, Silk WK, Rost TL (1990) Effects of low water potential on cortical
cell length in growing regions of maize roots. Plant Physiol 93: 648–651
Gandar PW (1983) Growth in root apices. I. The kinematic description of
growth. Bot Gaz 144: 1–10
Gastal F, Nelson CJ (1994) Nitrogen use within the growing leaf blade of
tall fescue. Plant Physiol 105: 191–197
Girousse C, Moulia B, Silk W, Bonnemain JL (2005) Aphid infestation
causes different changes in carbon and nitrogen allocation in alfalfa
stems as well as different inhibitions of longitudinal and radial expansion. Plant Physiol 137: 1474–1484
Goodwin RH, Avers W (1956) Studies on roots. III. An analysis of root
growth in Phleum pratense using photomicrographic records. Am J Bot
43: 479–487
Goodwin RH, Stepka W (1945) Growth and differentiation in the root tip of
Phleum pratense. Am J Bot 32: 36–46
Gould KS, Lord EM (1989) A kinematic analysis of tepal growth in Lilium
longiflorum. Planta 177: 66–73
Granier C, Tardieu F (1998) Spatial and temporal analyses of expansion
and cell cycle in sunflower leaves. A common pattern of development
for all zones of a leaf and different leaves of a plant. Plant Physiol 116:
991–1001
Granier C, Tardieu F (1999) Water deficit and spatial pattern of leaf
development. Variability in responses can be simulated using a simple
model of leaf development. Plant Physiol 119: 609–620
Hu Y, Camp KH, Schmidhalter U (2000) Kinetics and spatial distribution
of leaf elongation of wheat (Triticum aestivum L.) under saline soil
conditions. Int J Plant Sci 161: 575–582
Jahne B (1997) Digital Image Processing: Concepts, Algorithms, and
Scientific Applications, Ed 4. Springer, Berlin
316
Kavanova M, Grimoldi AA, Lattanzi FA, Schnyder H (2006) Phosphorus
nutrition and mycorrhiza effects on grass leaf growth. P status- and sizemediated effects on growth zone kinematics. Plant Cell Environ 29:
511–520
Liang BM, Sharp RE, Baskin TI (1997) Regulation of growth anisotropy in
well-watered and water-stressed maize roots. 1. Spatial distribution of
longitudinal, radial, and tangential expansion rates. Plant Physiol 115:
101–111
Ma Z, Baskin TI, Brown KM, Lynch JP (2003) Regulation of root elongation
under phosphorus stress involves changes in ethylene responsiveness.
Plant Physiol 131: 1381–1390
Muller B, Strosser M, Tardieu F (1998) Spatial distributions of tissue
expansion and cell division rates are related to sugar content in the
growing zone of maize roots. Plant Cell Environ 21: 149–158
Pahlavanian AM, Silk WK (1988) Effect of temperature on spatial and
temporal aspects of growth in the primary maize root. Plant Physiol 87:
529–532
Press WH, Teukolsky SA, Vetterling WT, Flannerty BP (1992) Numerical
Recipes in C: The Art of Scientific Computing, Ed 2. Cambridge University
Press, New York
Prewitt JMS (1970) Object enhancement and extraction. In EBS Lipkin, A
Rosenfield, eds, Picture Processing and Psychopictorics. Academic
Press, New York, pp 75–149
Sacks MM, Silk WK, Burman P (1997) Effect of water stress on cortical cell
division rates within the apical meristem of primary roots of maize.
Plant Physiol 114: 519–527
Schmundt D, Stitt M, Jahne B, Schurr U (1998) Quantitative analysis of the
local rates of growth of dicot leaves at a high temporal and spatial
resolution, using image sequence analysis. Plant J 16: 505–514
Selker JML, Sievers A (1987) Analysis of extension and curvature during
the graviresponse in Lepidium roots. Am J Bot 74: 1863–1871
Sharp R, Silk W, Hsaio T (1988) Growth of the maize primary root at low
water potentials. I. Spatial distribution of expansive growth. Plant
Physiol 87: 50–57
Sharp RE, Poroyko V, Hejlek LG, Spollen WG, Springer GK, Bohnert HJ,
Nguyen HT (2004) Root growth maintenance during water deficits:
physiology to functional genomics. J Exp Bot 55: 2343–2351
Silk WK, Erickson RO (1978) Kinematics of hypocotyl curvature. Am J Bot
65: 310–319
Silk WK, Erickson RO (1979) Kinematics of plant growth. J Theor Biol 76:
481–501
Sobel I (1978) Neighborhood coding of binary images for fast contour
following and general binary array processing. Computer Graphics and
Image Processing 8: 127–135
Taiz L, Zeiger E (1998) Plant Physiology, Ed 2. Sinauer Associates, Sunderland, MA
van der Weele CM, Jiang HS, Palaniappan KK, Ivanov VB, Palaniappan
K, Baskin TI (2003) A new algorithm for computational image analysis
of deformable motion at high spatial and temporal resolution applied
to root growth. Roughly uniform elongation in the meristem and also,
after an abrupt acceleration, in the elongation zone. Plant Physiol 132:
1138–1148
Walter A, Spies H, Terjung S, Kusters R, Kirchgessner N, Schurr U (2002)
Spatio-temporal dynamics of expansion growth in roots: automatic quantification of diurnal course and temperature response by digital image sequence processing. J Exp Bot 53: 689–698
Plant Physiol. Vol. 145, 2007
Detailed Quantitative Analysis of Architectural Traits
of Basal Roots of Young Seedlings of Bean in Response
to Auxin and Ethylene1[W]
Paramita Basu, Kathleen M. Brown, and Anupam Pal*
Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur
208016, India (P.B., A.P.); and Intercollege Program in Plant Biology (P.B., K.M.B.) and Department of
Horticulture (K.M.B.), Pennsylvania State University, University Park, Pennsylvania 16802
Vertical placement of roots within the soil determines their efficiency of acquisition of heterogeneous belowground
resources. This study quantifies the architectural traits of seedling basal roots of bean (Phaseolus vulgaris), and shows that the
distribution of root tips at different depths results from a combined effect of both basal root growth angle (BRGA) and root
length. Based on emergence locations, the basal roots are classified in three zones, upper, middle, and lower, with each zone
having distinct architectural traits. The genotypes characterized as shallow on BRGA alone produced basal roots with higher
BRGA, greater length, and more vertically distributed roots than deep genotypes, thereby establishing root depth as a robust
measure of root architecture. Although endogenous indole-3-acetic acid (IAA) levels were similar in all genotypes, IAA and
1-N-naphthylphthalamic acid treatments showed different root growth responses to auxin because shallow and deep
genotypes tended to have optimal and supraoptimal auxin levels, respectively, for root growth in controls. While IAA
increased ethylene production, ethylene also increased IAA content. Although differences in acropetal IAA transport to
roots of different zones can account for some of the differences in auxin responsiveness among roots of different emergence
positions, this study shows that mutually dependent ethylene-auxin interplay regulates BRGA and root growth differently
in different genotypes. Root length inhibition by auxin was reversed by an ethylene synthesis inhibitor. However, IAA
caused smaller BRGA in deep genotypes, but not in shallow genotypes, which only responded to IAA in the presence of an
ethylene inhibitor.
Root architecture (i.e. the three-dimensional configuration of the root system) is an important factor for
the acquisition of underground resources (Lynch,
1995). In common bean (Phaseolus vulgaris), the root
system consists of the primary, basal, lateral, and
adventitious roots. The basal roots (BRs), which are
specialized secondary roots emerging from the hypocotyl (Zobel, 1996; Basu et al., 2007), together with the
primary root constitute a relatively larger portion of
the scaffolding for the mature root system (Liao et al.,
2001). The length and growth angle of the BRs are two
of the most important factors determining root distribution and the consequent acquisition efficiency of
belowground resources like nutrients and water, the
1
This work was supported by the U.S. Agency for International
Development Bean/Cowpea Collaborative Research Support Program
(grant to K.M.B.) and by the Department of Science and Technology,
Government of India (grant no. SR/FT/LS–085/2007 to P.B.).
* Corresponding author; e-mail [email protected].
The author responsible for distribution of materials integral to the
findings presented in this article in accordance with the policy
described in the Instructions for Authors (www.plantphysiol.org) is:
Anupam Pal ([email protected]).
[W]
The online version of this article contains Web-only data.
www.plantphysiol.org/cgi/doi/10.1104/pp.110.168229
2056
distribution of which varies with depth (Lynch and
Brown, 2001; Ho et al., 2005).
Basal root growth angle (BRGA) and length are
regulated by genotype, phosphorus availability, and
ethylene (Bonser et al., 1996; Liao et al., 2001; Lynch
and Brown, 2001; Basu et al., 2007). BRs in the topsoil
are better adapted to low phosphorus availability than
BRs in the subsoil, because of higher availability of
phosphorus near the soil horizon (Bonser et al., 1996;
Liao et al., 2001; Lynch and Brown, 2001; Basu et al.,
2007). On the other hand, BR growth into the subsoil is
desirable for drought avoidance (Ho et al., 2005).
Phytohormones such as auxin and ethylene play
vital roles in modulating root growth and gravitropism, although there are few studies of plagiogravitropic growth responses. Various models have been
proposed for ethylene-auxin interplay in root gravitropism (Lee et al., 1990; Alonso et al., 2003; Buer
et al., 2006) and root growth (Rahman et al., 2001;
Swarup et al., 2002; Stepanova et al., 2007). While
some studies have reported that ethylene inhibits
polar and lateral auxin transport in tissues of shoots
(Morgan and Gausman, 1966; Suttle, 1988) and roots
(Lee et al., 1990; Prayitno et al., 2006), other studies
have reported no effect in hypocotyls and petioles
(Abeles, 1966) or a stimulatory effect of ethylene on
auxin transport in root tissues (Negi et al., 2008, 2010).
Plant PhysiologyÒ, April 2011, Vol. 155, pp. 2056–2065, www.plantphysiol.org Ó 2011 American Society of Plant Biologists
Hormonal Cross Talk in Root Architecture
According to Stepanova et al. (2005), ethylene triggers
auxin synthesis in Arabidopsis (Arabidopsis thaliana)
root tip by transcriptional activation of genes coding
for both subunits of anthranilate synthase, a phenomenon partially responsible for the inhibition of
root growth by ethylene. An indirect assessment of
auxin distribution using the auxin-sensitive reporter
DR5-GUS by Růžička et al. (2007) also showed that
the stimulation of biosynthesis and transport of auxin
by ethylene is responsible for inhibition of root elongation. Physiological studies have shown that application of gaseous ethylene inhibits root growth while
increasing BRGA in common bean (Basu et al., 2007).
Ethylene-auxin interaction also depends on cell type,
developmental stage of the organ, and environmental
conditions (Stepanova et al., 2007). All of this evidence suggests that auxin and auxin-ethylene interplay could be important for regulating architectural
traits of the BRs.
Therefore, the objectives of this study were to (1)
quantify the patterns of BRGA, root growth, and root
tip depth in different genotypes, (2) analyze the effects
of changing levels of auxin on architectural traits of
BRs, and (3) describe the role of auxin-ethylene interplay in regulating these traits in common bean during
seedling development.
RESULTS
Root Architectural Traits
BRs emerged from a narrow axial region of 0.4 6
0.1 cm along the lower hypocotyl above the root-shoot
interface (Fig. 1). In both deep and shallow genotypes,
all of the BRs emerged as protrusions in vertical files
(Fig. 1, A and B) within a time window of 4 to 6 h.
However, after 2 d of growth, the BRs had very
different architectural traits depending on genotype
and position of emergence (Fig. 1, C and D). Typically,
three axial locations of BR emergence were observed
(Fig. 1, A and B). Frequency distribution of the emergence locations of the BRs also showed a trimodal
distribution (Fig. 2). Using the nadir values of frequencies between the modes, the emergence locations
were clustered in three emergence zones: lower, middle, and upper. Classifications of the BRs in these
zones were compared with careful manual classification of the roots based on “whorls” (Basu et al., 2007)
by k statistics. For deep and shallow genotypes, k
values were 0.97 and 0.87 (P , 0.001), respectively.
Measurements of BRGA, root length, and tip depth
after 2 d of growth quantified the observed differences
in architectural traits of BRs (Fig. 3). As expected, deep
genotypes produced roots with significantly smaller
Figure 1. Examples of bean seedlings at 0 (A and B) and 48 (C and D) h after transfer to the growth pouch. Images of the same plants,
deep genotype B98311 (A and C) and shallow genotype TLP19 (B and D), are shown at the two developmental stages. IAA and NPA
were applied in a piece of germination paper within the plastic ring at the hypocotyl (B and D). All BRs emerge simultaneously as
protrusions along the hypocotyl (A and B). The BRs are labeled according to upper (u, U), middle (m, M), and lower (l, L) zones and
numbered for matching between 0-h and 48-h seedlings (no other physiological implication in labeling). The thick black line (D) is
the plant midline on which the emergence points of the BRs (white outlined black circles) are projected perpendicularly. The
projection of the emergence point of the lowest originating BR (e.g. L5) is the reference point identified by the white star. The
emergence location of each BR is recorded with respect to the reference point along the plant midline. Tip depth is measured with
respect to the reference point along the gravity vector. BR length is measured along the midline (dotted black line) from the
emergence point to the root tip. BRGA is the angle made by the straight line joining the root tip to the emergence point with gravity.
Plant Physiol. Vol. 155, 2011
2057
Basu et al.
Figure 2. Frequency distributions of emergence
locations of BRs along the hypocotyl of deep
genotypes B98311, RIL7, and RIL76 (A) and shallow genotypes TLP19, RIL15, and RIL57 (B). Data
represent six to seven plants per genotype. The
dotted lines indicate the demarcation of lower,
middle, and upper emergence zones of BRs for
each genotype. The range of emergence zones is
indicated as follows: deep genotypes (A), lower =
0 to 0.128 cm, middle = 0.128 to 0.23 cm, and
upper = greater than 0.23 cm; shallow genotypes
(B), lower = 0 to 0.154 cm, middle = 0.154 to
0.282 cm, and upper = greater than 0.282 cm.
BRGA compared with shallow genotypes from the
upper and lower zones, but the middle zones for both
genotypes produced BRs of similar BRGA (Fig. 3A).
The BRs originating from the higher emergence zones
grew with higher BRGA (one-way ANOVA, P ,
0.001), a result that confirmed similar results from a
previous report (Basu et al., 2007). On the other hand,
the length of the roots decreased with higher emergence locations (Fig. 3B; one-way ANOVA, P , 0.001).
The shallow genotypes produced significantly longer
BRs than the deep genotypes. Both BRGA and root
length contributed to depth distribution of the root
tips (Fig. 3C). For straight-growing roots, the relationship among BRGA, root length, and tip depth is
expressed as
tip depth ¼ emergence location
þ root length 3 cosðBRGAÞ
ð1Þ
For curved roots, in Equation 1 root length is replaced with the straight line distance from the root base
to the tip (Fig. 1D). With higher emergence locations,
cos(BRGA) increased as the BRGA decreased (Fig. 3A).
Simultaneously, the root length increased (Fig. 3B),
which resulted in increased tip depth with lower emergence locations (Fig. 3C; one-way ANOVA, P , 0.001).
For the lower roots of the shallow genotypes, larger
BRGA led to smaller cos(BRGA) compared with the
deep genotypes (Fig. 3A). However, roots of the shallow genotypes grew longer than the deep genotypes
(Fig. 3B). As a result, the tips of the lower roots from the
shallow genotypes reached 32.5% deeper than those
from the deep genotypes (Fig. 3C). On the other hand,
both genotypes produced BRs of similar tip depth from
the upper and middle emergence zones.
Endogenous Free Indole-3-Acetic Acid Content and
Ethylene Production
To determine if endogenous indole-3-acetic acid
(IAA) and ethylene in the BR tissue act as regulators
of BRGA and root growth, we measured endogenous
IAA as well as ethylene production from the BRs
emerging from the upper, middle, and lower zones
(Table I). Analysis of IAA content per BR as well as per
2058
gram fresh weight of tissue showed that endogenous
IAA content did not differ between shallow and deep
genotypes or among roots of different emergence
locations (Supplemental Table S1). No significant genotypic effect was observed on endogenous ethylene
production either. However, endogenous ethylene
production was slightly lower (P = 0.04) in the lower
roots compared with the upper ones.
Since wounding (excision) can potentially cause
excess ethylene production and hence bias the results,
we also compared ethylene evolution from the intact
tissues and tissues divided into upper, middle, and
lower zones of the emergence region containing the
BRs. We observed no significant effect of wounding
(excision) on ethylene production, as dividing the root
tissues into separate emergence zones did not significantly affect ethylene evolution compared with that of
the intact tissue (data not shown).
Responsiveness of BR Architectural Traits to Auxin
The absence of a difference in endogenous IAA
among the genotypes with different architectural traits
indicates that if auxin has to play any role in regulating
these traits, there must be differences in auxin responsiveness among the roots and the genotypes. Therefore, the seedlings were treated with IAA (0–30 nmol)
and 1-N-naphthylphthalamic acid (NPA; 0–20 nmol) to
examine the responsiveness of root architectural traits
to exogenous auxin and an inhibitor of auxin transport, respectively (Fig. 4). Both genotypes showed
smaller BRGA at lower emergence zones and vice
versa (Fig. 4, A and B; two-way ANOVA, P , 0.001),
similar to controls (Fig. 3A). In the deep genotypes,
hormone treatment also significantly affected BRGA,
but not in shallow genotypes (two-way ANOVA, P ,
0.001 in deep, P = 0.156 in shallow). There was no
significant interaction between the main effects, emergence zone, and hormone treatment in either genotype. The BRs in the deep genotypes had significantly
smaller BRGA following both IAA and NPA treatments compared with the controls (Fig. 4A; Dunnett’s
two-sided test, P , 0.001). In the shallow genotypes,
however, there was no effect of application of IAA or
NPA on BRGA (Fig. 4B).
Plant Physiol. Vol. 155, 2011
Hormonal Cross Talk in Root Architecture
types produced the longest BRs, as treatment with
either IAA or NPA reduced root length significantly
(Dunnett’s two-sided test, P , 0.001; Fig. 4D). In the
deep genotypes, while treatment with higher IAA
doses inhibited root growth (Fig. 4C; P , 0.001 for 20
and 30 nmol), treatment with NPA marginally promoted root growth in the lower and middle roots
and slightly inhibited root growth in the upper roots
(Fig. 4C).
Variations in BRGA and root length are reflected in
tip depth of the IAA- and NPA-treated plants. With
the application of IAA, as BRGA reduced in the deep
genotypes (Fig. 4A), cos(BRGA) in Equation 1 increased. But at the same time, root length also reduced
with IAA (Fig. 4F). As a result, tip depth varied little in
deep genotypes following IAA treatment (Fig. 4C). But
since BRGA is not significantly affected by IAA treatment in the shallow genotypes, the effect of reduced
root length due to IAA treatment (Fig. 4D) is directly
reflected in the reduction of tip depth (Fig. 4F; P , 0.05
for 20 and 30 nmol). On the other hand, NPA treatment
increased tip depth in the deep genotypes (Fig. 4E; P ,
0.05), a result of lower BRGA due to NPA (Fig. 4A). But
NPA reduced tip depth (Fig. 4F; P = 0.09 for 10 nmol
and P = 0.03 for 20 nmol) in the shallow genotypes, a
reflection of the inhibition of root growth under NPA.
Auxin Transport
Figure 3. Growth parameters of BRs from the upper, middle, and lower
emergence zones of six contrasting genotypes of common bean (three
deep and three shallow). Data are means 6 SE of six to seven plants per
genotype. Differences in BRGA, root length, and tip depth between
deep and shallow genotypes for each emergence zone were determined by t test (P , 0.05). n.s., Nonsignificant difference of means. For
each genotype, BRGA, root length, and tip depth varied significantly
with emergence zones as determined by one-way ANOVA (P , 0.001).
Two-way ANOVA indicated that both emergence
zone and treatments affected root length in both the
deep and shallow genotypes (P , 0.001). The upper
BRs in both deep and shallow genotypes were shorter
than the lower BRs (Fig. 4, C and D), similar to controls
(Fig. 3B). However, the responses of root length to
NPA and IAA treatments were different in the deep
and shallow genotypes. Controls in the shallow genoPlant Physiol. Vol. 155, 2011
The results of IAA and NPA treatment experiments
indicated that differences in responsiveness of the BRs
to exogenous IAA depended on emergence location.
Since exogenous IAA was applied closer to the upper
roots, this could be partly explained by differences in
auxin transport to roots from different emergence
locations. Auxin transport was analyzed using radioactive auxin, [5-3H]IAA, applied in the plastic ring at
the hypocotyl, similar to application of IAA or NPA
(Fig. 1). There was no difference in radiolabel detection
between deep and shallow genotypes. But, as expected, because of the proximity of the upper emergence zone to the site of application (from lower zone,
0.74 6 0.22 cm; from upper zone, 0.45 6 0.26 cm), more
radiolabel was found in the upper roots than the lower
roots (upper, 10,789 6 967 cpm; lower, 4,978 6 370
cpm; P , 0.001, pooled for both genotypes). This
nearly doubling of radiolabel in the upper roots compared with the lower roots may contribute to some
extent to the differences in auxin dose response in root
architectural traits. In the shallow genotypes, application of 30 nmol of IAA led to 60% and 33% reductions
in lengths of upper and lower roots, respectively,
whereas the BRs in the deep genotypes shortened by
26% and 22% for the upper and lower emergence
zones, respectively, following application of the same
amount of IAA.
To assess the effect of ethylene on IAA transport,
movement of [3H]IAA from the hypocotyl to the
primary roots and BRs in ethylene-treated seedlings of
both genotype classes was measured. Although an
2059
Basu et al.
Table I. Comparison of endogenous IAA content (per g fresh weight of tissue and per BR) and endogenous
ethylene production (per g fresh weight of tissue)
IAA content was measured from the BRs (12–22 BRs yielding approximately 150–200 mg of root tissue
per sample) emerging from the upper, middle, and lower zones. Endogenous ethylene production was
measured by tissue segments of three emergence zones bearing BRs emerging from the upper, middle, and
lower zones (9–10 samples). Two contrasting genotypes (shallow RIL57 and deep RIL7) were used for
determining IAA content and ethylene production. Values shown are means 6 SE. Differences in
endogenous IAA measures and ethylene production due to genotypes and emergence zones of the BRs
were determined by two-way ANOVA (Supplemental Table S1).
Genotype
Emergence Zone
Deep
Deep
Deep
Shallow
Shallow
Shallow
Upper
Middle
Lower
Upper
Middle
Lower
Endogenous IAA
Measure
ng g21 fresh wt
26.16
28.90
28.04
25.78
28.61
27.91
6
6
6
6
6
6
increasing trend compared with the controls was
observed (10,823 6 57 cpm in upper and 5,037 6 250
cpm in lower, both genotypes pooled), the effect was
not significant (P . 0.6).
6.0
0.9
0.5
3.1
4.2
5.4
Endogenous IAA
Measure
Endogenous Ethylene
Production
ng BR21
nL h21 g21 fresh wt
0.23
0.28
0.28
0.21
0.27
0.28
6
6
6
6
6
6
0.066
0.006
0.024
0.006
0.062
0.003
46.71
47.30
36.66
55.40
49.79
41.05
6
6
6
6
6
6
3.9
2.2
3.2
5.9
8.1
4.6
the shortening of roots due to IAA alone (Fig. 5D).
Whereas IAA alone reduced tip depths of lower roots,
addition of AVG did not alter IAA effects on tip depths
of either deep or shallow genotypes (Fig. 5, E and F).
Ethylene-Auxin Interplay
Auxin could regulate BR architectural traits via
alteration of ethylene synthesis or response. To examine if the application of exogenous IAA had any effect
on ethylene evolution, endogenous ethylene production rates were measured from the deep and shallow
genotypes. Ethylene production from the BRs was
significantly higher with external IAA treatment (Table II). The effect of ethylene on free IAA content was
quantified by gas chromatography-mass spectrometry
(Engelberth et al., 2003; Schmelz et al., 2003). There
was an approximately 30% increase (P , 0.02) in IAA
content per BR following ethylene treatment.
Influence of Ethylene Inhibitors on Root
Architectural Traits
To examine the changes in architectural traits of the
BRs, the seedlings were treated with both IAA and the
ethylene synthesis inhibitor aminoethoxyvinylglycine
(AVG). A preliminary experiment showed that 1.2
nmol (60 mM) of AVG inhibited ethylene production
from the BRs by 80%. Therefore, BRGA, root length,
and tip depth were measured after treatment with 1.2
nmol of AVG + 30 nmol of IAA (Fig. 5). In the deep
genotypes, application of 30 nmol of IAA reduced
BRGA, but application of AVG + IAA had no additional effects (Fig. 5A). In shallow genotypes, opposite
effects were observed: 30 nmol of IAA alone had very
little effect on BRGA, but addition of AVG + IAA
reduced BRGA (Fig. 5B). AVG completely reversed the
IAA-induced effect on root length in deep genotypes
when compared with the control (Fig. 5C). However,
in shallow genotypes, AVG + IAA partially reversed
2060
DISCUSSION
This study presents detailed quantitative assessment of architectural traits (e.g. growth angle, root
length, and tip depth) of BRs of common bean and the
effects of auxin-ethylene cross talk on these traits.
These architectural traits play key roles in conformation of the root system and the consequent efficiency of
acquisition of belowground resources. BRs emerge on
day 3 after imbibition when the primary root is about 2
to 3 cm long. When unimpeded, the BRs exhibit
plagiogravitropic growth and tend to maintain their
growth trajectory for the next 24 h; eventually, they
reorient their direction of growth by exhibiting higher
or lower BRGA (Bonser et al., 1996; Basu et al., 2007).
Quantitative Measure of BR Emergence Zones
BRs have been observed to emerge from two to three
distinct whorls along the lower hypocotyl (Basu et al.,
2007), but the emergence zones are not perfectly
aligned (e.g. roots l1, l2, and l3 in Figure 1A emerge
from the lower whorl, but their emergence occurs at
different positions along the hypocotyl). Quantitative
assessment of the emergence location revealed three
emergence zones that matched very well with the
manual classification. These zones allow objective
classification of the roots that might be difficult and
ambiguous to classify by visual examination. For
example, in Figure 1, C and D, it is difficult to objectively identify the originating zone of each BR by
visual observation alone, especially when the roots are
more than 1 cm long, although in the emerging seedlings they are identifiable (Fig. 1, A and B). But the
Plant Physiol. Vol. 155, 2011
Hormonal Cross Talk in Root Architecture
more direct estimation of the vertical root position and
hence allows better categorization of the root system.
As explained using Equation 1, the depth of the BRs is
affected by both BRGA and root length. As a result, the
lower roots from the shallow genotypes actually had
deeper tips than those of the deep genotypes, but roots
from the upper zone had similar tip depths in both
genotype classes. In addition, root length and angle
also determine the horizontal placement of the BRs,
with greater length and larger BRGAs spreading the
roots away from the primary root and vice versa.
Therefore, these results indicate that compared with
the deep genotypes, the shallow genotypes have a
more “spread out” root system, vertically and horizontally, which not only helps the shallow genotypes
adapt better to vertically heterogeneous distribution of
soil resources but also reduces intraplant competition
(Lynch and Brown, 2008).
Auxin-Mediated Changes in Root Architectural Traits
Figure 4. The effects of IAA and NPA applications on BR architectural
traits BRGA (A and B), root length (C and D), and tip depth (E and F) for
the upper, middle, and lower emergence zones. A, C, and E represent
deep genotypes, and B, D, and F represent shallow genotypes. Data
show means 6 SE (n = 6–7 plants per treatment). The vertical dotted line
in each plot indicates the control. Asterisks indicate significant differences compared with the control group (two-way ANOVA followed by
Dunnett’s two-sided test, P , 0.05).
quantitative demarcation of each emergence zone
makes it easy and objective to identify the zone of
emergence of each BR. Because of distinct BRGA for
each zone, the roots tend to occupy specific soil depths
during growth. Consequently, for the nutrients that are
nonuniformly distributed in the soil, one can anticipate specific roles of BRs emerging from the specific
zones. These zones can be used to characterize the
physiology and function of each group of BRs.
Characterization of Root Systems by Depth
The previous classification of genotypes as shallow
or deep was based on BRGA alone (Bonser et al., 1996;
Liao et al., 2001). Calculation of tip depth provides a
Plant Physiol. Vol. 155, 2011
The presence of a variety of root growth angles in
various bean genotypes (Bonser et al., 1996; Liao et al.,
2001; Basu et al., 2007) and the known role of auxin in
regulating gravitropic responses (Luschnig et al., 1998;
Marchant et al., 1999; Friml et al., 2002) prompted us to
examine whether endogenous IAA in the BRs could be
responsible for differences in their architectural traits.
However neither endogenous IAA concentration (ng
g21 fresh weight) nor content (ng BR21) was found to
be different between the shallow and deep genotypes,
indicating that total endogenous IAA is insufficient to
account for the variation in BRGA or growth rates.
Therefore, auxin responsiveness is likely to regulate
the architectural traits of the BRs.
Our experiments with IAA and NPA treatments
showed that auxin responsiveness indeed varied with
genotypes as well as specific architectural traits. Shallow genotypes appeared to have optimal auxin for
root growth, since growth was reduced by either IAA
or NPA treatment, but not so in deep genotypes.
Instead, auxin content in the roots emerging from the
middle and lower zones of deep genotypes appeared
to be supraoptimal for growth, since treatment with
NPA slightly increased root length, while treatment
with IAA reduced it significantly. The roots from the
upper emergence zone in deep genotypes showed
similar responsiveness to that of shallow genotypes,
although the effects were not significant. Acropetal
auxin transport has been shown to influence root
growth in Arabidopsis (Reed et al., 1998), while basipetal transport controls root gravitropism (Rashotte
et al., 2000). We applied IAA and NPA at the hypocotyl
above the basal rooting zone. Therefore, it appears that
exogenous IAA and NPA had stronger influence on
acropetal than basipetal transport of auxin, leading to
greater auxin responsiveness of root length than
BRGA in shallow genotypes. Comparatively, deep
genotypes appeared to have optimal auxin concentration for BRGA, as BRGA was reduced by either IAA or
2061
Basu et al.
Table II. Effect of exogenous IAA (30 nmol) on ethylene production by tissue segments of emergence zone-bearing young BRs
Values shown are means 6 se of four to seven plants for each group. Differences in ethylene production from control and IAA-treated roots for
each emergence zone and each genotype as determined by t test are indicated as follows: a P , 0.05, b P , 0.01.
Ethylene Production
Treatment
Deep Genotype (RIL7)
Upper
Middle
Shallow Genotype (RIL57)
Lower
Upper
-1
Control
IAA
46.71 6 3.9a
63.01 6 4.4a
47.30 6 2.2
55.68 6 3.6
nL h g
36.66 6 3.2b
52.23 6 2.1b
NPA (Fig. 4A). However, BRGA in shallow genotypes
was relatively insensitive to IAA or NPA treatment,
indicating that basipetal auxin transport may have
been affected by IAA or NPA treatment in deep
genotypes but not in shallow genotypes.
Transport of auxin studied with [5-3H]IAA showed
that the proximity of the application point leads to
nearly double auxin content in the roots of the upper
zone relative to the lower zone, but there is no difference in auxin transport between deep and shallow
genotypes. Therefore, transport of exogenous auxin
may account for the differences in response to application of IAA (and possibly NPA as well) between
roots of different zones, but it does not explain the
variation in responses between deep and shallow
genotypes; rather, it points to differences in responsiveness of the genotypes to auxin.
21
fresh wt
55.40 6 5.9a
73.02 6 3.2a
Middle
Lower
49.78 6 8.1b
66.21 6 1.8b
41.05 6 4.6b
55.68 6 1.7b
similar effects in both deep and shallow genotypes.
The inhibition by IAA was reversed completely in
deep genotypes and partially in shallow genotypes
due to the inhibition of ethylene biosynthesis by AVG
treatment along with IAA. It has been reported that
auxin signaling is downstream of the ethylene signal
transduction pathway (Roman et al., 1995; Stepanova
Ethylene-Auxin Cross Talk
Ethylene has been repeatedly invoked as an important modulator of gravity responses (Chadwick and
Burg, 1967; Wheeler and Salisbury, 1981; Lee et al., 1990;
Madlung et al., 1999) in addition to auxin. Here, we
show that IAA stimulated ethylene production (Table
II), as expected from earlier observations (Abeles et al.,
1992; Abel et al., 1995; Woeste et al., 1999). Similarly,
application of gaseous ethylene also increased endogenous IAA content, a result consistent with earlier
reports showing that ethylene application enhanced
IAA synthesis (Stepanova et al., 2007; Swarup et al.,
2007) as well as IAA transport (Negi et al., 2008). It has
also been shown that, similar to IAA, gaseous ethylene
inhibited root growth of the same genotypes (Basu
et al., 2007). In addition, ethylene had a strong effect on
BRGA as well (Basu et al., 2007). Here, we show that
IAA reduces BRGA, but only in the deep genotypes.
Ethylene synthesis inhibition with AVG neither reversed nor enhanced the auxin effect on BRGA in the
deep genotypes. Therefore, it seems that the effect of
AVG was insufficient to alter the auxin effect on BRGA
in deep genotypes. However, AVG reduced BRGA of
shallow genotypes, indicating that BRGAs of shallow
genotypes are more sensitive to ethylene, confirming
previous observations (Basu et al., 2007).
Comparison of root lengths following treatments
with IAA and IAA + AVG with controls showed
2062
Figure 5. Effect of exogenous application of IAA (30 nmol) alone and
AVG (1.2 nmol) + IAA (30 nmol) on BRGA (A and B), root length (C and
D), and tip depth (E and F) of BRs of upper, middle, and lower zones
from deep (B98311 and RIL7) and shallow (TLP19 and RIL57) genotypes. Bars indicate means 6 SE (n = 5–6 plants per genotype per
treatment). Differences in BRGA, root length, and tip depth between
control and IAA /AVG + IAA treatments as well as between IAA and
AVG + IAA treatments for each emergence zone, as determined by t
test, are as follows: a P , 0.05, b P , 0.01, c P , 0.001.
Plant Physiol. Vol. 155, 2011
Hormonal Cross Talk in Root Architecture
et al., 2005), suggesting that ethylene regulates root
growth via auxin. These observations, therefore, led to
the following hypothetical model explaining ethyleneauxin interplay in regulating root growth.
A Hypothetical Model for Ethylene-Auxin Cross Talk in
Regulating BR Architecture
Based on the results from our experiments together
with the observations reported in the literature, we
propose a hypothetical model to explain ethyleneauxin interplay in regulating BR growth in common
bean (Fig. 6). Since the shallow genotypes have optimal auxin for root growth, addition of IAA or NPA
makes auxin content supraoptimal or suboptimal,
respectively, inhibiting root growth. However, the
deep genotypes naturally have supraoptimal auxin
for root growth. Exogenous IAA inhibits root growth,
but NPA may reduce auxin content to optimal or
suboptimal levels, depending on the dose of NPA.
Since NPA was applied at the hypocotyl, the NPA
effect may have been stronger in the upper BRs than in
the middle and lower BRs. As a result, auxin content is
likely driven to suboptimal levels, slightly inhibiting
root growth in the upper roots of the deep genotypes.
In the middle and lower roots of the deep genotypes,
NPA tends to reduce auxin transport toward optimal
levels, marginally promoting root growth. Therefore,
root growth response to auxin is dependent on the
endogenous concentration of auxin relative to the
auxin-versus-root growth curve (i.e. whether auxin
concentration is optimal, suboptimal, or supraoptimal
for root growth in controls).
Auxin stimulates ethylene synthesis (Abeles et al.,
1992; Abel et al., 1995; Woeste et al., 1999; this work),
while ethylene promotes auxin synthesis (Stepanova
et al., 2007; Swarup et al., 2007; this work) and transport (Negi et al., 2008, 2010). Ethylene is also known to
have stimulatory effects on both acropetal and basipetal auxin transport (Buer et al., 2006; Růžička et al.,
2007; Negi et al., 2008), regulating root growth and
gravitropism, respectively. Our experiments with
[5-3H]IAA indicated that application of IAA at the
hypocotyl above the basal rooting zone directly influenced acropetal auxin transport, which affected root
growth. In addition, IAA treatment also increased
ethylene production, which in turn can affect basipetal
transport of auxin and thereby potentially influence
the graviresponse of the BRs. Therefore, this mutually
dependent ethylene-auxin interaction may be the key
mechanism of variations in BRGA due to exogenous
IAA and IAA + AVG. However, the difference in
response of BRGA to IAA and IAA + AVG between
deep and shallow genotypes indicates a more complex
genotype-dependent interaction between auxin and
ethylene in regulating graviresponse of the BRs, which
this study does not completely resolve.
Although our study was designed initially to test
the effect of different phosphorus treatments on BR
architectural traits, in both this and the previous work
Plant Physiol. Vol. 155, 2011
Figure 6. A model for ethylene-auxin cross talk in regulating BR growth
and architecture in deep (right) and shallow (left) genotypes. Acropetal
auxin transport (black arrows) regulates root growth, whereas basipetal
transport (white arrows) regulates gravitropism. Application of IAA
(solid arrow at the top) or NPA (broken arrow at the top) at the
hypocotyl alters acropetal transport and consequently affects root
growth. Larger black arrows indicate higher amounts of IAA, and larger
broken lines indicate higher activities of NPA. Application of IAA also
promotes endogenous ethylene production (gray circles with Et), which
can affect both acropetal and basipetal auxin transport and, hence, root
growth and root angle as secondary effects. The effects of IAA, NPA,
ethylene, and AVG treatment are shown on the root growth-versusauxin schematic curves. The white stars on the curves indicate the
relationship of root growth to auxin in controls. U, M, and L represent
upper, middle, and lower emergence zones, respectively.
(Basu et al., 2007), there were no significant effects of
phosphorus on root architecture. Previous work with
older seedlings showed that genotypes vary in their
response to phosphorus (Bonser et al., 1996; Liao et al.,
2001). Therefore, a more detailed study with older
plants or different genotypes is necessary to explore
BR architectural plasticity in response to nutrient
availability. Furthermore, although the BRs from all
zones tend to emerge within a time span of 4 to 6 h, the
small temporal difference in emergence time of each
root may contribute to the differences in root length
after 48 h. As the development of the BRs is a continuous process, it is very difficult to pinpoint the exact
timing of emergence of the BRs. Therefore, this study
does not quantify the timing of the emergence of BRs,
2063
Basu et al.
and a detailed kinematic study is planned to establish
the effect of temporal difference in variations in root
growth on root length and consequent root architecture. Since the study focuses on early seedlings alone
in the two-dimensional growth pouch, further investigations are necessary to explore how these early
developmental features of BRs translate to mature root
systems in the native three-dimensional environment.
In conclusion, this study provides a quantitative
description of architectural traits of BRs of common
bean seedlings and contrasts these between two genotype classes. The hormonal cross talk regulating the
architectural traits of roots is complex. This study
explores this complexity of ethylene-auxin interaction
in regulating root growth and builds a framework for
future molecular studies.
MATERIALS AND METHODS
Plant Material and Growth Conditions
Six genotypes (parents B98311 and TLP19 and recombinant inbred lines
[RILs] 15, 57, 7, and 76) of bean (Phaseolus vulgaris) were selected from the L88
population developed by Dr. Jim Kelly at Michigan State University (Frahm
et al., 2004). B98311 and RIL7 and RIL76 have deep root systems (BRGA of
41.7° 6 14°), and TLP19 and RIL15 and RIL57 have shallow root systems
(BRGA of 56.4° 6 18°; Basu et al., 2007).
Seeds were surface sterilized with 6% sodium hypochlorite, rinsed with
distilled water, and scarified. Seeds were placed in darkness at 28°C in a
germination chamber for 2 d in rolled germination paper (25.5 3 37.5 cm;
Anchor Paper) moistened with nutrient solution composed of (in mM) 3,000
KNO3, 2,000 Ca(NO3)2, 250 MgSO4, 25 KCl, 12.5 H3BO3, 1 MnSO4, 1 ZnSO4,
0.25 CuSO4, 0.25 (NH4)6Mo7O24, and 25 Fe-Na-EDTA. Two types of nutrient
solution were used, low phosphorus [500 mM (NH4)2SO4] and high phosphorus (1,000 mM NH4H2PO4). Germinated seeds with radicles (2–3 cm) were
transferred to growth pouches consisting of a germination paper inside a
polyethylene bag supported by a plexiglass sheet. Pouches were open at the
bottom to allow direct contact with the nutrient solution. The time of transfer
to the growth pouch was identified as 0 h.
Imaging and Image Analysis
The roots were photographed 48 h after transfer to the pouch with the
camera placed at the level of the BR emergence region. Figure 1 shows
seedlings of deep and shallow genotypes at 0 h (A and B) and 48 h (C and D).
The plant midline (Fig. 1A) was drawn manually through the basal rooting
zone on which the emergence point of each BR was projected. Using the
lowest emerging BR as a reference, the emergence location of each BR was
measured along the plant midline. Root length, tip depth, and BRGA were
measured from the images. Downward measurements of tip depth from the
reference location along the gravity vector were positive values. Larger
BRGAs indicate shallower BRs.
Quantification of Endogenous Auxin
To quantify the amount of endogenous IAA present in the BRs of two
contrasting genotypes (shallow, RIL57; and deep, RIL7), the BRs were
harvested 48 h after transplanting to the growth pouch. Endogenous free
IAA was measured by gas chromatography-tandem mass spectrometry with
methanol chemical ionization (Trace GC 2000 attached to a GCQ mass
spectrometer; Thermo Finningan) and compared with [2H5]IAA as internal
standard (Engelberth et al., 2003; Schmelz et al., 2003). Because of their small
size, BRs from 10 to 12 plants (150–200 mg of root tissue and 12–22 BRs per
vial) were analyzed together. Since root tip is reported to be a major site of
production of auxin (Ljung et al., 2005), the IAA content was determined per
BR in addition to per gram fresh weight of tissue.
2064
In a separate experiment, BRs of RIL57 (shallow) and RIL7 (deep) were
exposed to 0 or 0.6 mL L21 ethylene (Basu et al., 2007) to determine the effect of
ethylene on endogenous IAA.
Treatment with IAA and NPA
To study the effect of changes in auxin level on architectural traits of BRs,
solutions of IAA (10, 20, and 30 nmol in 20 mL) or NPA (10 and 20 nmol in
20 mL) were applied at 0 and 24 h in a small piece of germination paper within
a plastic ring at the hypocotyl (Fig. 1). Root images were captured 48 h after
transfer to the pouch. There were six to seven plants per genotype per
treatment.
Measurement of Ethylene Production
Endogenous ethylene production was measured from the tissue segments
of different emergence zones bearing BRs of the control and auxin-treated (30
nmol) seedlings at 48 h. The tissues were excised and immediately enclosed in
9-mL air-tight vials at 25°C. A 1-mL volume of the head space was taken from
the vials 2 h later and then injected into a gas chromatograph (HP6890;
Hewlett-Packard). To assess the effect of wounding (excision) on ethylene
production, ethylene was measured from intact tissue (whole segment of the
basal rooting zone) with BRs. These results were compared with the ethylene
evolution from the wounded tissues that had been cut into three separate
zones of emergence containing BRs and analyzed together.
Treatment with Ethylene Inhibitors
Seedlings were exposed to an inhibitor of ethylene biosynthesis, AVG,
together with IAA. Concentrations of 60 mM (1.2 nmol) AVG and 30 nmol of
IAA were added in the ring at the hypocotyl (Fig. 1) at 0 and 24 h.
Auxin Transport Analysis
Auxin transport was assessed using radioactive auxin [5-3H]IAA (25 Ci
mmol21; American Radiolabeled Chemicals). Twenty microliters of the stock
solution (by diluting 40 mM [5-3H]IAA with 1.5 mM cold IAA, which is
equivalent to 30 nmol, to make a total volume of 3 mL) was placed in the
plastic ring at the hypocotyl (Fig. 1). Seedling segments were harvested to
evaluate the transport of labeled IAA to the BR segments. Tissue samples were
transferred to separate vials containing 10 mL of Biosafe II, a biodegradable
and nonflammable scintillation fluid. Counts of radioactivity were measured
for 2 min using a scintillation counter (1500 Tricarb; Packard). To examine the
effect of ethylene on IAA transport, the radioactive seedlings in the pouch
were treated with ethylene inside an air-tight plexiglass chamber after
application of [3H]IAA before harvesting to determine radioactivity.
Data Analysis
The BRs emerged from up to three zones along the hypocotyl (Fig. 1, C and
D) previously referred to as whorls (Basu et al., 2007). These zones were
identified quantitatively from frequency distributions of emergence locations
measured relative to the lowest emerging BR. To compare how the emergence
zones match with the whorls, an experienced researcher manually identified
the whorls of emergence of the BRs from closeup views. Each experiment
consisted of two to six contrasting genotypes in two classes (shallow and
deep). Although two contrasting nutrient solutions containing low and high
phosphorus were used, there was no statistically significant effect of phosphorus treatment on root architectural traits. Therefore, in this entire study,
data were pooled over both high- and low-phosphorus treatments.
Statistical Analysis
The k statistic was used as a measure of concordance between quantitative
classification of BRs in emergence zones and manual identification of originating whorls. Student’s t test was used to detect significant differences in
architectural traits of BRs between deep and shallow genotypes, whereas oneway ANOVA was used to identify differences between roots of different
zones. Two-way ANOVA followed by Dunnett’s two-tailed t test were used to
detect significant differences between control and treatments associated with
genotypes and emergence zones. Effects of genotype, emergence location, and
Plant Physiol. Vol. 155, 2011
Hormonal Cross Talk in Root Architecture
dose of hormone and inhibitors on growth angle, root length, and tip depth
were tested at the 95% confidence level. Statistical analysis for this study was
carried out with SPSS 13.0 (SPSS).
Supplemental Data
The following materials are available in the online version of this article.
Supplemental Table S1. Two-way ANOVA for endogenous IAA content
and ethylene production from BRs of deep and shallow genotypes.
ACKNOWLEDGMENTS
We thank Jurgen Engelberth for helping in the quantification of free IAA
and Amelia Henry for critical comments on the manuscript.
Received November 7, 2010; accepted February 2, 2011; published February
10, 2011.
LITERATURE CITED
Abel S, Nguyen MD, Chow W, Theologis A (1995) ASC4, a primary indoleacetic acid-responsive gene encoding 1-aminocyclopropane-1-carboxylate
synthase in Arabidopsis thaliana. J Biol Chem 270: 19093–19099
Abeles FB (1966) Effect of ethylene on auxin transport. Plant Physiol 41:
946–948
Abeles FB, Morgan PW, Saltveit ME (1992) Ethylene in Plant Biology, Ed 2.
Academic Press, San Diego
Alonso JM, Stepanova AN, Solano R, Wisman E, Ferrari S, Ausubel FM,
Ecker JR (2003) Five components of the ethylene-response pathway
identified in a screen for weak ethylene-insensitive mutants in Arabidopsis. Proc Natl Acad Sci USA 100: 2992–2997
Basu P, Zhang YJ, Lynch JP, Brown KM (2007) Ethylene modulates genetic,
positional, and nutritional regulation of root plagiogravitropism. Funct
Plant Biol 34: 41–51
Bonser AM, Lynch J, Snapp S (1996) Effect of phosphorus deficiency
on growth angle of basal roots in Phaseolus vulgaris. New Phytol 132:
281–288
Buer CS, Sukumar P, Muday GK (2006) Ethylene modulates flavonoid
accumulation and gravitropic responses in roots of Arabidopsis. Plant
Physiol 140: 1384–1396
Chadwick AV, Burg SP (1967) An explanation of the inhibition of root
growth caused by indole-3-acetic acid. Plant Physiol 42: 415–420
Engelberth J, Schmelz EA, Alborn HT, Cardoza YJ, Huang J, Tumlinson
JH (2003) Simultaneous quantification of jasmonic acid and salicylic
acid in plants by vapor-phase extraction and gas chromatographychemical ionization-mass spectrometry. Anal Biochem 312: 242–250
Frahm MA, Rosas JC, Mayek-Perez N, Lopez-Salinas E, Acosta-Gallegos
JA, Kelly JD (2004) Breeding beans for resistance to terminal drought in
the lowland tropics. Euphytica 136: 223–232
Friml J, Wiśniewska J, Benková E, Mendgen K, Palme K (2002) Lateral
relocation of auxin efflux regulator PIN3 mediates tropism in Arabidopsis. Nature 415: 806–809
Ho MD, Rosas JC, Brown KM, Lynch JP (2005) Root architectural tradeoffs
for water and phosphorus acquisition. Funct Plant Biol 32: 737–748
Lee JS, Chang W-K, Evans ML (1990) Effects of ethylene on the kinetics of
curvature and auxin redistribution in gravistimulated roots of Zea mays.
Plant Physiol 94: 1770–1775
Liao H, Rubio G, Yan XL, Cao AQ, Brown KM, Lynch JP (2001) Effect of
phosphorus availability on basal root shallowness in common bean.
Plant Soil 232: 69–79
Ljung K, Hull AK, Celenza J, Yamada M, Estelle M, Normanly J,
Sandberg G (2005) Sites and regulation of auxin biosynthesis in
Arabidopsis roots. Plant Cell 17: 1090–1104
Luschnig C, Gaxiola RA, Grisafi P, Fink GR (1998) EIR1, a root-specific
protein involved in auxin transport, is required for gravitropism in
Arabidopsis thaliana. Genes Dev 12: 2175–2187
Plant Physiol. Vol. 155, 2011
Lynch JP (1995) Root architecture and plant productivity. Plant Physiol
109: 7–13
Lynch JP, Brown KM (2001) Topsoil foraging: an architectural adaptation of
plants to low phosphorus availability. Plant Soil 237: 225–237
Lynch JP, Brown KM (2008) Root strategies for phosphorus acquisition. In
PJ White, JP Hammond, eds, The Ecophysiology of Plant-Phosphorus
Interactions. Springer, Dordrecht, The Netherlands, pp 83–116
Madlung A, Behringer FJ, Lomax TL (1999) Ethylene plays multiple
nonprimary roles in modulating the gravitropic response in tomato.
Plant Physiol 120: 897–906
Marchant A, Kargul J, May ST, Muller P, Delbarre A, Perrot-Rechenmann C,
Bennett MJ (1999) AUX1 regulates root gravitropism in Arabidopsis by
facilitating auxin uptake within root apical tissues. EMBO J 18: 2066–2073
Morgan PW, Gausman HW (1966) Effects of ethylene on auxin transport.
Plant Physiol 41: 45–52
Negi S, Ivanchenko MG, Muday GK (2008) Ethylene regulates lateral root
formation and auxin transport in Arabidopsis thaliana. Plant J 55: 175–187
Negi S, Sukumar P, Liu X, Cohen JD, Muday GK (2010) Genetic dissection
of the role of ethylene in regulating auxin-dependent lateral and
adventitious root formation in tomato. Plant J 61: 3–15
Prayitno J, Rolfe BG, Mathesius U (2006) The ethylene-insensitive sickle
mutant of Medicago truncatula shows altered auxin transport regulation
during nodulation. Plant Physiol 142: 168–180
Rahman A, Amakawa T, Goto N, Tsurumi S (2001) Auxin is a positive
regulator for ethylene-mediated response in the growth of Arabidopsis
roots. Plant Cell Physiol 42: 301–307
Rashotte AM, Brady SR, Reed RC, Ante SJ, Muday GK (2000) Basipetal
auxin transport is required for gravitropism in roots of Arabidopsis.
Plant Physiol 122: 481–490
Reed RC, Brady SR, Muday GK (1998) Inhibition of auxin movement from
the shoot into the root inhibits lateral root development in Arabidopsis.
Plant Physiol 118: 1369–1378
Roman G, Lubarsky B, Kieber JJ, Rothenberg M, Ecker JR (1995) Genetic
analysis of ethylene signal transduction in Arabidopsis thaliana: five
novel mutant loci integrated into a stress response pathway. Genetics
139: 1393–1409
Růžička K, Ljung K, Vanneste S, Podhorská R, Beeckman T, Friml J,
Benková E (2007) Ethylene regulates root growth through effects on
auxin biosynthesis and transport-dependent auxin distribution. Plant
Cell 19: 2197–2212
Schmelz EA, Engelberth J, Alborn HT, O’Donnell P, Sammons M,
Toshima H, Tumlinson JH III (2003) Simultaneous analysis of phytohormones, phytotoxins, and volatile organic compounds in plants. Proc
Natl Acad Sci USA 100: 10552–10557
Stepanova AN, Hoyt JM, Hamilton AA, Alonso JM (2005) A link between
ethylene and auxin uncovered by the characterization of two root-specific
ethylene-insensitive mutants in Arabidopsis. Plant Cell 17: 2230–2242
Stepanova AN, Yun J, Likhacheva AV, Alonso JM (2007) Multilevel
interactions between ethylene and auxin in Arabidopsis roots. Plant
Cell 19: 2169–2185
Suttle JC (1988) Effect of ethylene treatment on polar IAA transport, net
IAA uptake and specific binding of N-1-naphthylphthalamic acid in
tissues and microsomes isolated from etiolated pea epicotyls. Plant
Physiol 88: 795–799
Swarup R, Parry G, Graham N, Allen T, Bennett M (2002) Auxin crosstalk: integration of signalling pathways to control plant development.
Plant Mol Biol 49: 411–426
Swarup R, Perry P, Hagenbeek D, Van Der Straeten D, Beemster GTS,
Sandberg G, Bhalerao R, Ljung K, Bennett MJ (2007) Ethylene
upregulates auxin biosynthesis in Arabidopsis seedlings to enhance
inhibition of root cell elongation. Plant Cell 19: 2186–2196
Wheeler RM, Salisbury FB (1981) Gravitropism in higher plant shoots. I. A
role for ethylene. Plant Physiol 67: 686–690
Woeste KE, Ye C, Kieber JJ (1999) Two Arabidopsis mutants that overproduce
ethylene are affected in the posttranscriptional regulation of 1-aminocyclopropane-1-carboxylic acid synthase. Plant Physiol 119: 521–530
Zobel R (1996) Genetic control of root systems. In Y Waisel, A Eshel, U Kafkafi,
eds, Plant Roots: The Hidden Half. Marcel Dekker, New York, pp 21–30
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article addendum
This manuscript has been published online, prior to printing. Once the issue is complete and page numbers have been assigned, the citation will change accordingly.
Plant Signaling & Behavior 6:7, 1-4; July 2011; © 2011 Landes Bioscience
Spatio-temporal analysis of development of basal roots
of common bean (Phaseolus vulgaris L.)
Paramita Basu* and Anupam Pal*
Department of Biological Sciences and Bioengineering; Indian Institute of Technology Kanpur; Kanpur, Uttar Pradesh India
T
Key words: basal root, kinematics,
root architecture, root growth, spatiotemporal analysis, root imaging
Submitted: 03/11/11
Accepted: 03/11/11
DOI:
*Correspondence to: Paramita Basu and
Anupam Pal; Email: [email protected]
and [email protected]
Addendum to: Basu P, Brown KM, Pal A. Detailed
quantitative analysis of architectural traits of
basal roots of young seedlings of Phaseolus vulgaris L. in response to auxin and ethylene. Plant
Physiol 2011; 155:2056–65; PMID: 21311033; DOI:
10.1104/pp.110.168229.
www.landesbioscience.com
emporal development of roots is key
to the understanding of root system
architecture of plants which influences
nutrient uptake, anchorage and plant
competition. Using time lapse imaging
we analyzed developmental patterns of
length, growth angle, depth and curvature of Phaseolus basal roots from emergence till 48 h in two genotypes, B98311
and TLP19 with contrasting growth
angles. In both genotypes all basal
roots appeared almost simultaneously,
but their growth rates varied which
accounted for differences in root length.
The growth angles of the basal roots
fluctuated rapidly during initial development due to oscillatory root growth
causing local bends. Beyond 24 h, as
the root curvature stabilized, so did the
growth angle. Therefore growth angle
of basal roots is not a very reliable quantity for characterizing root architecture,
especially during early seedling development. Comparatively, tip depth is a more
robust measure of vertical distribution of
the basal roots even during early seedling
development.
Vertical and horizontal placements of
the roots in the soil influence plant performance through acquisition of below
ground resources like water and nutrients,
plant anchorage and intra- and inter-plant
competition.1-4 Therefore the architecture
of the root system plays important roles in
regulating plant growth and yield, especially under abiotic stresses.5 As a seedling grows to become a mature plant, the
root architecture develops continuously in
response to various cues e.g., genotypic,
environmental, hormonal, etc. Therefore
studies of root architecture of plants of
different ages are important for understanding the influence of these cues in
regulating plant growth.
The root scaffold of a plant is comprised of different types of roots with different functions. A mature common bean
(Phaseolus vulgaris L.) plant has root system consisting of primary, adventitious,
lateral and basal roots. Among these, the
basal roots are typically the earliest emerging secondary roots from the hypocotyl6
forming a major part of the mature root
system. We have recently demonstrated
important differences in architectural
traits of the basal roots of common bean in
the early seedling stage between two contrasting class of genotypes and how auxinethylene interplay regulates these traits.7
While this study of basal roots at a fixed
time allows assessment and comparison of
root development up to that point of time,
investigation of the temporal events of
emergence and growth of the basal roots
is important and complementary to the
understanding of their architectural traits.
Therefore in the present study, we examined the detailed developmental patterns
of basal roots through time lapse imaging
in two genotypes.
We chose two bean genotypes with
contrasting basal root growth angles
(BRGA) relative to the gravity—B98311
producing basal roots of smaller BRGA
(41.7° ± 14°) and TLP19 having roots of
larger BRGA (56.4° ± 18°).8 The germinated seedling with 2–3 cm radical was
transferred to the blue germination paper
(Anchor Paper Co., St. Paul, MN, USA),
which was suspended in nutrient solution7 inside a growth chamber (ACMAS
Plant Signaling & Behavior1
the method of overlapping polynomials.
Length of the midline is root length. The
angle between gravity and the line connecting the root tip to the base is BRGA.7
The vertical distance of the root tip from
the base of the lowest emerging root along
the gravity vector is tip depth. From the
midline, root curvature was also determined using the equation
(1)
where [x(x), y(s)] is coordinate of any
point along the root midline, s is normalized distance along the midline, and the
primes denote derivatives with respect to
s. Here positive curvature signifies bending upward and vice versa.
Spatio-Temporal Development
of Basal Roots
Figure 1. Temporal variations in growth parameters of basal roots from upper and lower emergence zones of two plants of contrasting genotypes (B98311 and TLP19) of common bean. Data
were collected at 30 min intervals from emergence till 48 h. Asterisks in (B) identify the roots, the
spatio-temporal variations in curvature of which are shown in Figure 2.
Technocracy Limited, Delhi, India)
maintained at 25 ± 1°C. Time lapse photography was carried out for 48 h at 30
min intervals using Nikon D200 digital
camera fitted with a macro lens to obtain
high resolution digital images of the roots.
Imaging started from the visibility of the
protrusions of emerging basal root along
the root-shoot interface. A computer
2
program was developed in Matlab® 7.8
(Mathworks, Natick, USA) to analyze the
images semi-automatically. From every
image the computer program identified
the basal roots using contrast of color
between the roots (mostly white) and the
germination paper (blue). Root midlines
were determined following the methodology of Miller et al.9 and smoothed using
Plant Signaling & Behavior
The temporal development of architectural trait of four basal roots each from a
B98311 plant and a TLP19 plant is shown
in Figure 1. For both plants, all basal
roots emerged together but they grew at
different growth rates which accounted
for their differences in length (Fig. 1A).
Similar observation was made for other
Phaseolus plants of same and different
genotypes as well. This result points to a
marked difference in emergence patterns
between basal roots compared with other
types of secondary roots. For example,
it has been reported that lateral roots of
Arabidopsis emerge with specific temporal rhythm.10 Sequential emergence of
seminal and adventitious roots have also
been reported in grass.11 But our results
show that the emergence of basal roots
in common bean is almost simultaneous
and therefore the heterogeneity of lengths
of basal roots due to genotypic differences
and position of origin reported in Basu et
al.7 is primarily dependent on variations
in growth rate. Initially the growth rate
was slower and after 12–18 h the growth
rate accelerated as indicated by the change
in slope of the root length vs. time lines.
Although in majority of the cases the
growth rate was nearly maintained, a few
roots also showed deceleration of growth
rates. Furthermore the basal roots of
Volume 6 Issue 7
Figure 2. Gray scale spatio-temporal map of midline curvature of example basal roots from two contrasting genotypes and two emergence zones.
The negative curvature values signify downward curvature and vice versa. Distance along root length is measured from root base (0 cm).
TLP19 had higher growth rate compared
to B98311, and lower basal roots of both
genotypes grew faster than the upper ones
resulting in corresponding variations in
root length.7
The growth angles of these eight basal
roots fluctuated by a greater extent initially and then tended to stabilize with
time (Fig. 1B). As a result, any comparison of BRGAs at a fixed time is likely to be
dominated by these highly transient fluctuations for the first 24 h. After the initial 24 h, although fluctuations of BRGAs
tend to subside, the BRGAs continue to
change as the basal roots show plagiogravitropic growth. It is at this time that
the influence of genotype and position of
origin tend to appear in the patterns of
BRGA. It is also interesting to note that
BRGA vs. time plots show both increasing and decreasing trends at 48 h which
is an outcome of curvature production
www.landesbioscience.com
in the basal roots during their growth as
illustrated later in Figure 2.
Combined effects of changing root
length, curvature and growth angle are
visible in tip depths (Fig. 1C). In spite of
relatively large variations in BRGA during
the initial 24 h, tip depths did not show
much variability. Therefore as mentioned
in Basu et al.7 tip depths represent a more
robust and direct measure of vertical
placement of the roots even during the
very early stage of seedling development
when the BRGAs fluctuate rapidly. The
tip depths also show that the TLP19 plant
produced more vertically spread out root
system compared to B98311 at any stage
of development.
Root Curvature
Figure 2 shows gray scale map of midline curvature of two example basal roots
Plant Signaling & Behavior
each from a B98311 and a TLP19 plant
as a function of time and distance along
the root midline. The temporal development of BRGAs of these four roots is
indicated by black and gray asterisks in
Figure 1B. The brighter shades indicate
upward curvature (positive values) and
darker shades show downward curvature
(negative values). In each of the roots,
gray shades change rapidly both in time
as well as along the root midline during
the initial 18–24 h indicating rapid oscillatory growth patterns of basal roots during early development. But after that, the
fluctuations in gray shades tend to subside.
During 24–48 h, the upper roots in both
plants (Figs. 2A and B) tended to grow
nearly straight as the shades are almost
mid-gray albeit with very gentle changes
along the root length. But the lower root
of B98311 showed downward curvature
(darker shade) near the root tip and slight
3
upward curvature near root base (brighter
shade) during 30–48 h (Fig. 2C). The
lower root of TLP19 showed the opposite
curvature patterns (Fig. 2D).
A comparison of Figure 1B with 2C
shows that the BRGA of the lower root of
B98311 (marked with gray asterisk in Fig.
1B) started to drop around 36 h and the
darker gray shade (i.e., downward curvature) near the root tip also began to arise
at the same time. On the other hand,
Figures 1B and 2D show that the BRGA
of the lower root of TLP19 (marked by
black asterisk in Fig. 1B) reduced till 30
h, but became nearly constant beyond
that. The dark gray shade (i.e., downward
curvature) between 0.2–0.8 cm lightened
starting from 18 h, while the lighter shade
(i.e., upward curvature) near the root tip
started to appear around 30 h. As a result,
between 24–42 h the lower root of TLP19
had smaller BRGA compared with lower
root of B98311, but beyond 42 h the lower
root of B98311 started to have smaller
BRGA. Therefore these results indicate
that instead of unidirectional bending, balance of both upward and downward bends
along the root length underlies gravitropic
response of the basal roots. Initially the
4
basal roots bend rapidly both along root
length and time resulting in fluctuations
in BRGA. Later on as the curvatures of the
roots stabilize, the BRGAs also stabilize.
Conclusions
This paper presents temporal analysis of
developmental patterns of basal roots of
common bean of two contrasting varieties and shows that the differences in root
growth presented in Basu et al.7 arise primarily from variations in growth rates
rather than temporal differences in basal
root emergence. Furthermore we also
show that due to rapidly changing curvature of the basal roots, there are greater
fluctuations in BRGAs during the initial
development and hence any comparison
of BRGA among roots during this period
may produce unreliable results. However
tip depth remains a robust measure of
vertical distribution of basal roots in common bean even during the initial development. Finally, we show that the growth of
basal roots is oscillatory in nature, and the
balance between upward and downward
bends determines growth angles of the
basal roots.
Plant Signaling & Behavior
References
1. Bailey PHJ, Currey JD, Fitter AH. The role of root
system architecture and root hairs in promoting
anchorage against uprooting forces in Allium cepa and
root mutants of Arabidopsis thaliana. J Exp Bot 2002;
53:333-40.
2. Maina GG, Brown JS, Gersani M. Intra-plant versus
inter-plant root competition in beans: avoidance,
resource matching or tragedy of the commons. Plant
Ecol 2002; 160:235-47.
3. Wang H, Inukai Y, Yamauchi A. Root development and nutrient uptake. Crit Rev Plant Sci 2006;
25:279-301.
4. Osmont KS, Sibout R, Hardtke CS. Hidden branches: developments in root system architecture. Annu
Rev Plant Biol 2007; 58:93-113.
5. Kashiwagi J, Krishnamurthy L, Crouch JH, Serraj
R. Variability of root length density and its contributions to seed yield in chickpea (Cicer arietinum L.)
under terminal drought stress. Field Crops Res 2006;
95:171-81.
6. Zobel R. Genetic control of root systems. In: Waisel
Y, Eshel A, Kafkafi U, Eds. Plant Roots: The Hidden
Half. New York: Marcel Dekker, Inc. 1996:21-30.
7. Basu P, Brown KM, Pal A. Detailed quantitative
analysis of architectural traits of basal roots of young
seedlings of Phaseolus vulgaris L. in response to auxin
and ethylene. Plant Physiol 2011; 155:2056-65; DOI:
10.1104/pp.110.168229.
8. Basu P, Zhang YJ, Lynch JP, Brown KM. Ethylene
modulates genetic, positional and nutritional regulation of root plagiogravitropism. Funct Plant Biol
2007; 34:41-51.
9. Miller ND, Parks BM, Spalding EP. Computer-vision
analysis of seedling responses to light and gravity.
Plant J 2007; 52:374-81.
10.Lucas M, Guedon Y, Jay-Allemand C, Godin C,
Laplaze L. An auxin transport-based model of root
branching in Arabidopsis thaliana. PLoS One 2008;
3:3673.
11. Aguirre L, Johnson DA. Root morphological development in relation to shoot growth in seedlings of
four range grasses. J Range Manag 1991; 44:341-6.
Volume 6 Issue 7
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A new tool for analysis of root growth in spatio-temporal continuum
2
Paramita Basu and Anupam Pal
3
Department of Biological Sciences and Bioengineering,
4
5
Indian Institute of Technology Kanpur, Kanpur 208016, India.
6
7
8
9
10
11
12
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Corresponding authors:
Anupam Pal, Ph.D.
Department of Biological Sciences and Bioengineering
Indian Institute of Technology Kanpur
Kanpur, Uttar Pradesh-208016, India
Tel: +91-9007003940
E-mail: [email protected]
Paramita Basu, Ph.D.
Department of Biological Sciences and Bioengineering
Indian Institute of Technology Kanpur
Kanpur, Uttar Pradesh-208016, India
Tel: +91-33-2355-1755
E-mail: [email protected]
Running title: Root growth analysis in spatio-temporal continuum
Word count:
Introduction
Materials and methods
Results
Discussion
Acknowledgements
Total
Number of Figures
Number of Tables
Number of supporting information (Figures)
Number of supporting information (Video)
539
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1334
1418
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5325
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SUMMARY
•
Quantification of overall growth and local growth zones of root system development is key
27
to understanding the biology of plant growth which helps explore the effects of
28
environmental, genotypic and mutational variations on plant’s development and
29
productivity.
30
•
We introduce a methodology to analyze growth patterns of plant roots from two-
31
dimensional time series images treating them as spatio-temporal three-dimensional (3D)
32
image volume. The roots are segmented from the images followed by two types of
33
analyses—3D spatio-temporal reconstruction analysis for simultaneous assessment of
34
initiation and growth of multiple roots, and spatio-temporal pixel intensity analysis along
35
root midlines for quantification of the growth zones.
36
•
The test measurements show simultaneous emergence of basal roots but sequential
37
emergence of lateral roots in Phaseolus vulgaris L., while lateral roots of Cicer arietinum
38
L. emerge in a rhythmic pattern. Local growth analysis reveals multimodal transient growth
39
zone in basal roots. At the initial stages after emergence, the roots oscillate rapidly which
40
slows down with time.
41
•
The methodology presented here allows detailed characterization of phenomenology of
42
roots providing valuable information of spatio-temporal development with application in a
43
wide range of growing plant organs.
44
45
Key words : root kinematics, image analysis, lateral root, basal root, Cicer arietinum,
46
Phaseolus vulgaris, root growth
2
47
INTRODUCTION
48
The root system architecture is central to a plant’s ability to survive, grow, and produce
49
yield as it impacts key physiological processes such as nutrient and water uptake, anchorage, and
50
inter- and intra-plant competition (Osmont et al., 2007). As a seedling grows to become a mature
51
plant, root architecture develops continuously to form the final root system. Although the spatial
52
distribution of roots of a mature plant may seem to be most important for a plant’s productivity,
53
the developmental pattern of the roots at each stage is also equally important as it controls the
54
development of the whole plant in the subsequent stages. Therefore spatio-temporal description
55
of root development is critical for understanding plant growth. Since the overall growth and
56
development of roots is a result of cumulative effects of local growth, it is also important to
57
study the growth patterns of the roots at multiple scales in space and time for exploring both
58
overall development and local growth zones. Such multi-scale spatio-temporal descriptions of
59
root development provide deeper insights into the biology of plant roots and their interactions
60
with the environment (Walter et al., 2009).
61
In recent years, time lapse imaging coupled with semi-automated analysis tools have been
62
developed to assess various kinematic parameters of root growth. Among these, thresholding
63
(Miller et al., 2007; Yazdanbakhsh & Fisahn, 2010), skeletonization (Armengaud et al., 2009)
64
and computer tracking (French et al., 2009) based image analysis methodologies provide
65
assessment of overall root growth. However such measurements are not suited for analysis of
66
local growth zones of the growing roots. Optical-flow based methods (van der Weele et al.,
67
2003; Basu et al., 2007; Chavarria-Krauser et al., 2008), on the other hand, allow assessment of
68
local growth zones of the roots. But the limitation of both approaches is that they remain
69
exclusive as the former requires uniformly colored roots while the latter requires inherent or
3
70
externally-added patterns on the roots. In addition, the prior approaches are also unable to
71
analyze root initiation and branching, leaving the temporal description of the root system
72
development incomplete.
73
We, therefore, designed a new methodology to explore the development of roots starting
74
from emergence till later stages, with or without patterns on the root images. Thus the new
75
methodology presents a unified approach with additional features for exploring finer details of
76
root development and growth which are unavailable in any previous method (van der Weele et
77
al., 2003; Basu et al., 2007; Miller et al., 2007; Chavarria-Krauser et al., 2008; Armengaud et
78
al., 2009; French et al., 2009; Yazdanbakhsh & Fisahn, 2010). The new methodology treats stack
79
of two-dimensional (2D) time-lapse images as three-dimensional (3D) volume of image data
80
allowing 3D image analysis and reconstruction tools to be used for exploration of growth
81
patterns of the roots in space and time, albeit with specific customizations and newer
82
interpretations. The proof-of-principle of the technique was tested by analyzing initiation and
83
growth of basal roots in rajmash bean (an Indian cultivar of common bean, Phaseolus vulgaris
84
L.), and lateral roots in both chickpea (Cicer arietinum L.) and rajmash bean. The comparison of
85
growth patterns of basal and lateral roots, and lateral roots of two different plants provide
86
intriguing insights into the development of root systems of these two legumes.
87
MATERIALS AND METHODS
88
Plant growth
89
The new methodology uses 2D images of growing roots obtained from plants grown in
90
germination paper sandwiched between a glass sheet in the front and a plastic sheet at the back
91
(Fig. 1a). We used seeds of chickpea (Cicer arietinum L.) cultivated variety DCP 92-3 developed
92
by Indian Institute of Pulses Research (IIPR), India and rajmash bean (Phaseolus vulgaris L.)
4
93
germplasm EC541702 collected from IIPR. Seeds were surface sterilized with 6% sodium
94
hypochlorite solution for 5 min and rinsed thoroughly with distilled water.
95
germinated at 25°C in darkness inside the growth chamber (Acm-78094 S, ACMAS technology,
96
New Delhi India) for 48 h in brown germination paper (Anchor Paper Co., MN, USA) moistened
97
with nutrient solution composed of (in µM) 3000 KNO3, 2000 Ca(NO3)2, 1000 NH4H2PO4, 250
98
MgSO4, 25 KCl, 12.5 H3BO3, 1 MnSO4, 1 ZnSO4, 0.25 CuSO4, 0.25 (NH4)6Mo7O24 and 25 Fe-
99
NA-EDTA. Germinated seeds of Cicer with 5-6 cm long primary roots and those of Phaseolus
100
with 2-3 cm long primary roots were transferred to a sheet of blue germination paper (Anchor
101
paper Co., MN, USA) stiffened by attaching glass sheet to stabilize the root system (Fig. 1a).
102
The bottom 2-3 cm of the blue germination paper containing the seedling was immersed in
103
nutrient solution and the entire set-up was placed inside the growth chamber with temperature of
104
22 ±1 °C for Cicer and 25 ±1 °C for Phaseolus seedlings. To ensure that the roots grow in dark,
105
the setup is placed in a growth chamber, divided into two compartments, with the shoot growing
106
in upper-bright (12h/12h day/night cycle) and the roots in lower-dark environments (Fig. 1b).
107
Image acquisition
Seeds were
108
Roots were imaged with Nikon D200 digital camera. The camera was connected to a
109
computer through which time lapse imaging was performed. We used two external flashes
110
(Nikon SB-800 and Nikon SB-600) for capturing the images to minimize exposure of the roots to
111
light (Fig. 1b). The external flashes were triggered wirelessly by the built-in flash of the camera.
112
Light from the flashes was directed away from the roots to avoid specular reflection on the roots
113
which can cause unwanted patterns.
114
One day after transferring the seedlings to the growth pouch, the roots were photographed for
115
up to 5 d at 30 min intervals to assess total growth of the basal and lateral roots. For uniformly
5
116
colored roots of legumes, graphite particles were sprinkled to add patterns for analysis of local
117
growth zones (Fig. 1c). Roots were photographed at 5-10 min intervals for up to 8 h (Fig. 1c).
118
While the setup allows continuous imaging of the roots for relatively long periods of time up to a
119
week or more, the assessment of local growth zones using added patterns of graphite particles is
120
limited by time, because growth of the roots makes the graphite particles too sparse to analyze,
121
limiting the study to a maximum of 8 h.
122
Image Analysis
123
The steps for the image analysis are described below with additional mathematical details
124
presented in the Notes S1 together with the flowchart (Fig. S1).
125
Image preprocessing and stabilization
126
Images captured with Nikon D200 digital camera have high resolution (10 Megapixels)
127
making it difficult to analyze a number of these images simultaneously. Therefore before
128
proceeding with the analysis, the program allows cropping the images to isolate specific group of
129
roots for analysis. In addition, to reduce the resolution of the images so that large number of
130
images can be analyzed together, the program also allows down-sampling of the images.
131
Images captured in the growth chamber are susceptible to small amount of vibrational motion
132
due to the ventilating fans in the growth chamber which gets amplified in close up images (Video
133
S1). If uncorrected, these vibrations lead to erroneous results of root trajectory. Therefore before
134
beginning processing of the images for extraction of dynamical data of root growth, identifiable
135
marker points on the background or on the non-growing parts of the roots, e.g. ink marks on the
136
germination paper, are tracked among all the images using the block matching technique
137
(Scarano, 2002; Basu et al., 2007). The points for tracking are chosen by the user on one
6
138
representative
139
Δx iAB = xiA − xiB ; (i = 1," , n) are calculated where xiA and xiB are coordinates of the i th point in the
140
reference
141
ΔX AB = median(ΔxiAB ) is the shift of the image B due to vibrations which is subtracted from the
142
pixel coordinates to eliminate vibration and ‘stabilize’ the images (Fig. S2, Video S2). Further
143
details of the vibration stabilization procedure are provided in the Notes S1.
144
Spatio-temporal image slicing
image
reference
A
and
image
current
A.
image
From
B.
n
The
such
median
points,
value
of
displacements
displacements
145
After stabilization of the images the first step in the analysis is segmentation of the roots.
146
Uniformly colored roots with good contrast relative to the background can be segmented by
147
choosing a threshold value of image intensity (Miller et al., 2007). But in roots with marker
148
points, thresholding cannot segment them as the patterns also get separated. Although many
149
semi-automated image segmentation methodologies exist (Canny, 1986; Kass et al., 1987;
150
Barrett & Mortensen, 1997), applying these on a large set of images individually is highly time-
151
consuming. Therefore we developed a methodology where the entire stack of images is first
152
sliced with a number of user specified spatio-temporal planes (P1-P5 in Fig. 1d) such that these
153
planes intersect the roots in almost all images. Image data are interpolated by trilinear
154
interpolation to generate spatio-temporal images of the roots on these slicing planes. Figure 1(e)
155
shows the sliced and segmented image of plane P3.
156
Segmentation by livewire
157
From the interpolated spatio-temporal images on the slicing planes, the roots are segmented
158
semi-automatically using the ‘livewire’ algorithm (Barrett & Mortensen, 1997; Hamarneh et al.,
7
159
2005). The points of intersection of the segmented contours on the sliced planes and the original
160
image planes are treated as the seed points. The livewire algorithm finds minimum cost path
161
between two seed points (Fig. S3). The cost of the path is determined from image features such
162
as intensity gradient magnitude, intensity gradient direction, laplacian zero crossing, and Canny
163
edge. After the seed points are established the minimum cumulative cost between two seed
164
points is calculated using dynamic programming following the optimal graph search method
165
(Dijkstra, 1959). The segmentation of roots by livewire algorithm is completely automated and
166
does not require any user intervention after the seed points have been established. Edges
167
obtained are smoothed by applying a Gaussian filter. The user can choose the amount of
168
smoothing by choosing the size of the filter kernel. Following segmentation of the roots in the
169
interpolated images, edge contours are generated (cyan lines in Fig. 1d) which intersect the
170
original images. Treating these points of intersection as ‘seeds’ (red dots in Fig. 1d, f) we
171
segment the roots automatically using livewire, resulting in edge contours of the roots on all
172
original 2D images (green contours in Fig. 1f).
173
Identification of tip and midlines of the roots
174
From the segmented and smoothed edge contour of a root, the user identifies a point near the
175
root tip on one of the time series images as an initial guess for the root tip. The location of the
176
highest curvature point on the edge contour in the neighborhood of the selected point is finalized
177
as the root tip (blue dots in Fig. S4). The neighborhood used in the method is ±10% of the total
178
length of the root edge. Then the point on the edge contour of the subsequent image which is
179
nearest to the current root tip is identified as the initial guess for the root tip in that image. This
180
step is followed by the same algorithm to relocate the tip to the highest curvature position. This
181
way the root tip is automatically identified in all the images. In case the algorithm fails to
8
182
identify the root tip accurately, the user can manually reposition it. After the root tip is identified
183
the root base is identified at the point of emergence (yellow dots in Fig. S4). Since typically the
184
base point is static, it is copied in all the images. In case the base point of the root moves, the
185
user has to manually reposition it.
186
For every pixel within the edge contour of the root, Euclidean distance transform (EDT) is
187
calculated (Fig. S4a). EDT is the distance of a pixel from the nearest edge point. The trajectory
188
of the maximum values of EDT is the root midline (Fig. S4b). This line is obtained by initiating a
189
search at the root tip with a direction vector pointing to the root base and using the procedure
190
described by Miller et al (2007). Since the EDT is calculated at pixel resolution, the detected
191
midline is also at pixel resolution.
192
Construction of spatio-temporal 3D structures
193
Following segmentation, two types of analyses are performed for studying overall root
194
growth (Fig. 1g) and local growth zones (Fig. 1h). Firstly from the segmented edge contours an
195
optimal field function φ ( x, y, t ) is calculated such that on the surface of the spatio-temporal 3D
196
structure φ ( x, y, t ) = 0 (Cong & Parvin, 1999). Here ( x, y ) are image plane coordinates and t is
197
time. Isosurface of this function at φ ( x, y, t ) = 0 is the spatio-temporal 3D structure (Fig. 1g).
198
The second type of analysis uses spatio-temporal variations in pixel intensities along the root
199
midline (Fig. 1h). For that pixel intensities along the root midline are interpolated from the
200
images using bi-linear interpolation and expressed as J ( s, t ) where s ( x, y ) is distance of point
201
( x, y ) from the root base along the midline. Since small changes in root midline can cause
202
substantial changes in the interpolated intensity function J ( s, t ) , for every spatio-temporal point,
9
203
it is averaged within a neighborhood of 3 pixels to obtain an average value of J ( s, t ) . When
204
J ( s, t ) is displayed as J ( x, y, t ) it provides the spatio-temporal view of the changes in the pixel
205
intensities along the midline of the roots and illustrates local growth zones (Fig. 1h) (Erickson &
206
Sax, 1956).
207
Analysis of root growth
208
The length of the root midline is the root length. The striped patterns on the spatio-temporal
209
map of the pixel intensities along the root midline show the local growth zones (Fig. 1h).
210
Treating these patterns similar to fluid flow, the spatio-temporal displacement vectors
211
u = (us , ut ) are obtained using the block matching technique (Scarano, 2002; Basu et al., 2007)
212
(Fig. S5a-c). Here us and ut are the components of the displacement vector along space and
213
time respectively. The temporal component of the displacement vector ut is the time gap
214
between two consecutive images so that the spatial component us is the displacement of a
215
marker position between those two images. Therefore
216
local growth velocity with respect to the root base. From the displacement vectors we calculate
217
the streak lines. For any point x0 = ( s0 , t0 ) on the spatio-temporal midline intensity map, the next
218
location can be calculated as x1 = x0 + u (i.e. s1 = s0 + us and t1 = t0 + ut ). Thus by joining n such
219
points x0 , x1 ," , x n the trajectory of a marker point is obtained which is the streak line (Fig. S5d-
220
f). Root growth velocities are interpolated along the streak lines and smoothed using overlapping
221
polynomials prior to calculation of relative elemental growth rate, REGR=
222
Validation
us
quantifies the local tissue velocity, i.e.
ut
∂ (us ut )
.
∂s
10
223
To assess the accuracy of the image analysis methodology we compared results obtained
224
from artificially generated sequences of 41 root images (resolution 300 x 600 pixels). The
225
artificial image sequences were generated from pre-designed theoretical growth zones with
226
specific REGR distributions—unimodal and bifurcating. In both cases the peak REGR values
227
were 0.05 per time point. Examples of these artificial image sequences are presented in the
228
Videos S3, S4. In the first example, the artificial root has a unimodal growth zone defined by a
229
Gaussian REGR which results in uniform elongation of the root. In the second example, the
230
unimodal elongation zone bifurcates into double-peaked elongation zones defined by Gaussian
231
distributions of REGR. The artificial root images were marked with 35 black dots so that the
232
local growth zones can be estimated. Root-mean-square-deviations (RMSD) of the measured
233
growth parameters from the theoretical values were calculated to quantify the accuracy of the
234
image analysis system.
235
236
RESULTS
237
Analysis of spatio-temporal growth of the root system
238
A spatio-temporal 3D structure was constructed from the edge contours as shown in Fig.
239
1(g). As the roots grow, the spatio-temporal 3D structure widens along the time axis. Therefore
240
its slope along the tip (i.e. the rate of widening with time) of each root shows variations in
241
growth rate with time. Furthermore, the geometry of the spatio-temporal 3D structure illustrates
242
the overall growth patterns, e.g. a wavy structure (basal root B2 in Fig. 1g) indicates oscillatory
243
root growth, whereas a flat structure (basal root B6 in Fig. 1g) shows unidirectional root growth.
244
This spatio-temporal 3D structure based analysis was used to explore the growth of basal (Fig.
245
2a; Video S5) and lateral roots of a bean seedling (Fig. 2b; Video S6), and lateral roots of a
11
246
chickpea seedling (Fig. 2c; Video S7). All the basal roots emerged around 12 h after transfer of
247
the seedling to the growth system indicated by the emerging ridges. However, at 48 h the basal
248
roots had different lengths. This analysis points to variations in growth rates as the solely
249
responsible factor contributing to the differences in root length (Fig. 2d). These observations
250
were confirmed in other plants of common bean as well (Fig. S6a, b). Although the lateral root
251
initiation began at 30 h in the same plant (hidden under the basal roots in Fig. 2a), only three
252
lateral roots emerged initially. But at 48 h a large number of lateral roots began to emerge
253
sequentially with the lower ones emerging later (Fig. 2b, e). Other plants of common bean also
254
showed similar behavior (Fig. S6c, Video S8). This initiation behavior was preserved even in
255
secondary lateral roots when they grew from the basal roots (Fig. S6d, Video S8). Similar spatio-
256
temporal pattern of lateral root initiation was also observed in chickpea with a slight difference
257
(Fig. 2c). The emergence of lateral roots in chickpea tended to have a rhythmic pattern. The
258
upper-most lateral roots emerged within 0-16 h followed by a delay of 8-16 h after which a large
259
number of lateral roots initiated within 32-48 h (Fig. 2c, f). Then again there was a pause of
260
approximately 12 h followed by initiation of next batch of lateral roots. The rhythmic growth
261
patterns of chickpea lateral roots are typical of the plants of this variety (e.g. Fig. S6e, f).
262
Analysis of local growth zone of roots
263
A second analysis targets local growth zones of individual roots. We identified the root tips
264
from the edge contours, and determined the root midlines by passing a line through the
265
maximum Euclidean distance transform (EDT) from the contours of the root edges (Fig. S4)
266
(Miller et al., 2007). Pixel intensities along the root midline at each time-step are presented as a
267
function of space and time in Fig. 1(h). The striped patterns on each spatio-temporal intensity
12
268
distribution indicate the movement of surface tissue. Region of the root where the stripes diverge
269
with time shows the growth zone and the slopes of the stripes provide estimates of local growth
270
velocities.
271
The root midline based analysis was tested in a basal (Fig. 3a, Video S2) and a lateral (Fig.
272
3b, Video S9) root of bean seedlings, and a lateral root of a chickpea seedling (Fig. 3c, Video
273
S10). In all three cases the patterns diverged with time. Treating the spatio-temporal patterns as
274
streak lines of a 2D fluid flow, we calculated the spatio-temporal displacement vectors using the
275
block matching technique of optical flow (Scarano, 2002; Basu et al., 2007) (Fig. S5a-c). From
276
the displacement vectors the streak lines were obtained (Fig. S5d-f). Slope of the displacement
277
vectors relative to the time axis is root growth velocity, the spatial derivative of which is the
278
REGR. Since calculation of derivative is highly affected by small variations, we interpolated the
279
growth velocities along each streak line (to ensure that the velocities of the same tissue are used
280
for interpolation) and smoothed before calculation of REGR. The calculations of REGR showed
281
that in bean basal roots the unimodal growth zone bifurcated into a multimodal growth zone with
282
two maxima as the peak split into two ridges (Fig. 4a). In the lateral root of bean, after the 1 h
283
mark, the growth zone shifted toward the apex (Fig. 4b) resulting in changes in slopes of the
284
patterns of graphite particles (black and white arrow heads in Fig. 3b). In chickpea lateral root,
285
the overall growth rate increased at the 2 h mark which is indicated by difference in slopes of the
286
black lines along the right edge of the REGR surface in Fig. 4(c). At this time the growth zone of
287
the root also widened implying that the increase in growth rate of the root was associated with
288
expansion of the growth zone rather than the local growth rate.
13
289
The consequence of multimodal growth zones in the basal roots was observed in variations of
290
growth rate. As the basal root of bean in Figs. 2(a), 3(a) was monitored for longer duration, we
291
observed that these multiple local maxima of the growth zone grew and diminished transiently
292
(Fig. 5a) which contributed to the rise and fall in growth rates of the basal roots (Fig. 5b). Such
293
behavior of root growth was consistent in all the basal roots of bean (e.g. Fig. 5c, d).
294
Root tip angle
295
The time lapse movie (Video S2) showed that the basal roots had wavy motion as they grew.
296
Fig. 6(a) shows superimposition of all root outlines of the bean seedling used in Fig. 1 (i.e. top
297
view of Fig. 1(g) without the spatio-temporal surface). The root tips shown by the colored dots
298
illustrate an oscillatory pattern indicating wavy root growth—a reflection of 2D projection of
299
nutation of the roots (Video S2). By joining the root tips with the root bases (open circles in Fig.
300
6a), we calculated the tip angle θ of the root tips relative to the gravity. The tip angle changed
301
with time as the roots followed a wavy growth pattern (Fig. 6b). Interestingly the fluctuations in
302
the root tip angle were more in basal roots B1, B3 and B4 which were relatively shorter in length
303
than the other three basal roots. In addition, the roots tended to initially oscillate with higher
304
frequency (for example, root B2) and with time the tip angles became relatively stable. Similar
305
behavior was observed in all other bean seedlings as well.
306
Assessment of accuracy of the image analysis system
307
Analyses of the artificial root images for testing the accuracy of the image analysis
308
system are presented in Figs. 7 and 8. Root edges were detected by livewire from which spatio-
309
temporal 3D structures were constructed. In Fig. 7(a), as the root elongated uniformly, the spatio-
14
310
temporal 3D structure had a uniform slope at the root tip with time. The slope of the spatio-
311
temporal 3D structure along the root tip in Fig. 7(b) changed, signifying changes in growth rate.
312
Following identification of the root tips and the root midlines, root lengths were calculated and
313
compared with the theoretical root lengths used in generating the artificial root images (Fig. 7c,
314
d). The measured root lengths almost matched with the theoretical root lengths (RMSD = 0.4
315
pixels).
316
The spatio-temporal maps of midline intensities are shown in Fig. 8(a, b). The spatio-
317
temporal traces of the marker dots (black stripes in the background) along the midline coincide
318
with the theoretical spatio-temporal positions of the marker dots (overlaying yellow lines). The
319
theoretical spatio-temporal variations of REGR used in generating the sequences of images with
320
single and bifurcating growth zones are shown in Fig. 8(c, d) respectively and the corresponding
321
REGR distributions calculated from the images are shown in Fig. 8(e, f). The RMSD values are
322
0.0034 per time point (between Fig. 8c and e) and 0.0062 per time point (between Fig. 8d and f).
323
With respect to the peak REGR value (0.05 per time point), differences between the theoretical
324
REGR and the calculated REGR are 6.7% and 12.5% for single and bifurcating growth zones
325
respectively.
326
DISCUSSION
327
We present here a new semiautomatic methodology to visualize and quantify initiation and
328
growth of roots in both space and time. The methodology addresses both overall growth and
329
local growth zones of roots providing interesting insights into the biology of root system
330
development. The methodology is tested in basal and lateral roots of bean seedlings and lateral
331
roots of chickpea seedlings. The test results demonstrate that both overall growth and local
15
332
growth zones are analyzable using our methodology which has the potential to reveal hitherto
333
unexplored patterns of root development.
334
The new methodology begins with the segmentation of the roots from the background which
335
is typically an arduous task. Therefore we developed a novel technique where the user input is
336
required in only a few sets of images to generate interpolated images on arbitrary slicing planes
337
followed by livewire assisted semiautomatic segmentation of the roots on these spatio-temporal
338
slicing planes. Once this step is completed, the rest of the process is nearly automated. The
339
number of slicing planes used for generating spatio-temporal interpolated images is not
340
dependent on the number of original images. Therefore even for a large number of images, the
341
human input does not need to increase, although the computational burden increases
342
proportionately. Once the root edge contours are detected, further analyses are performed to
343
obtain information relevant for exploring the development of root system architecture. For a set
344
of 30 images of 10 megapixel resolution it takes approximately 1 hour to do a complete analysis
345
on a 2 GHz Intel® Core 2TM Duo computer with 2GB RAM. This includes time for both
346
computer processing and user input.
347
The formation of spatio-temporal 3D structures from root edge contours is a new step that
348
this technology introduces for exploring initiation of secondary roots such as basal and lateral
349
roots. As evident in the test cases (Fig. 2), it is very difficult to exactly pin point when a basal or
350
a lateral root emerges as the process is continuous. In addition, measurement of structural details
351
e.g. length and angle of a barely emerging root can be erroneous as both the root base and the tip
352
are not uniquely identifiable even from the high resolution images. Therefore instead of direct
353
quantitative assessment of root emergence, a somewhat qualitative description is preferred. The
354
spatio-temporal 3D structures provide such information as can be seen in the emergence of the
16
355
ridges in Fig. 2(a)-(c). This 3D structure can help identify the spatio-temporal window when the
356
secondary roots begin to emerge, rather than being over-specific. Thus the spatio-temporal 3D
357
structure allows visualization and assessment of root initiation, branching and spatio-temporal
358
changes in length, diameter and angle. Furthermore such analysis shows development of multiple
359
roots simultaneously allowing comparative studies of the architectural behaviors. In the test
360
cases here, we demonstrated that the heterogeneity of lengths of bean basal roots arose from
361
differences in growth rates only as the emergence of basal roots was nearly simultaneous.
362
However in case of lateral roots of both bean and chickpea, root lengths depended on both
363
emergence time as well as growth rate. We also found that chickpea lateral roots grew with a
364
specific rhythm, similar to a phenomena reported in Arabidopsis (Lucas et al., 2008). At each
365
time window a group of lateral roots emerged followed by a pause and then the next set emerged.
366
But bean lateral roots emerged sequentially without any such pause or rhythm. Therefore the
367
current technology paves way for detailed investigations of how such growth patterns help these
368
different plants adapt to specific environmental conditions.
369
The local growth analysis using spatio-temporal patterns of pixel intensities along the root
370
midline allows visual demonstration of the spatio-temporal growth zones of the roots and,
371
consequently, helps understand the dynamics of root development in both space and time at finer
372
details. The measurements from such approach show that there are variations of REGR in bean
373
lateral and chickpea lateral roots indicating the transient nature of the growth zones which could
374
have been missed without the REGR distribution in space-time continuum. Interestingly bean
375
basal roots show clearly bifurcating multimodal transient growth zone which grows and
376
diminishes. Similar transient growth zones were also observed in maize (Walter et al., 2002).
17
377
The lateral movement of the root tip was visualized when the root outlines and tips from all
378
images were superimposed. We found wavy motion of the root tip which was higher in smaller
379
roots. The root tip angle was calculated by θ = tan−1(a b ) where a and b are horizontal and
380
vertical distances of the root tip from the base respectively. For very small roots, even a small
381
growth can change the ratio a b very rapidly, resulting in rapid fluctuations in root tip angle.
382
Therefore although root initiation angle has been identified as an important determinant of
383
spatial localization of roots (Clark et al., 2011), one has to be careful in quantifying initiation
384
angle because of rapid temporal variation at the initial stages of seedling development.
385
The methodology presented here was tested by analyzing artificial root images for which the
386
growth parameters were known. The results showed highly accurate assessment of the root
387
lengths indicating precise edge detection by livewire. The diverging patterns of spatio-temporal
388
maps of root midline intensities although indicate approximate growth zones, without further
389
quantitative analysis it is impossible to estimate the nature of the growth zones. For example,
390
only visual examination of Fig. 8(a), (b) do not reveal that in one case (Fig. 8a) there is a
391
unimodal growth zone, whereas in the other (Fig. 8b) the growth zone bifurcates. Thus the new
392
technology provides not only visual demonstration of the growth zones through spatio-temporal
393
maps of root midline intensities but also provide tools to quantify the local growth zones. The
394
comparison of REGR between theoretical and calculated values although indicated qualitative
395
similarity, the %RMSD values were slightly higher. Further analysis indicated that the relatively
396
higher values of %RMSD were contributed by three sources. Firstly the coordinates of the
397
marker dots calculated for each image were typically decimal point numbers. But the images
398
required these coordinates to be integers resulting in rounding errors. With higher resolution
399
images the rounding errors can be reduced, but at the cost of computing efficiency. Secondly
18
400
calculation of REGR required calculation of derivative of root growth velocity which, in turn,
401
required smoothing of the root growth velocity. Higher amount of smoothing caused relatively
402
smoother REGR distributions, but also underestimated the peak REGR values as smoothing
403
reduced slopes of root growth velocity, and vice versa. Since the growth velocities were
404
smoothed along the streak lines, REGR distributions along the streak lines appeared smoother
405
and sharper (e.g. the bifurcated REGR peak toward the tip in Fig. 8f). Finally, the calculation of
406
root growth velocity and REGR depended on pattern matching, and therefore, on the patterns
407
available. Higher number of marker dots of distinct patterns helped estimation of growth velocity
408
and REGR.
409
Although we used the methodology to analyze root growth, it can be used for analyzing
410
growth of any tissue. There is also no specific requirement of image source either for using the
411
technique. However, similar to any other image analysis technique, the quality and reliability of
412
assessments from the images depend on clarity of the images. The images used to test the system
413
were of high quality which required little human intervention. However on rare occasions when
414
the images had poorer quality due to condensation on the glass in front of the roots or uneven
415
illumination, the automated segmentation failed. In those cases the user had to manually segment
416
the roots. Since the methodology uses 2D images, it requires that the growth be uniplanar. In the
417
current study this was ensured by the transparent growth system which might not be the case for
418
other tissues. It has been shown earlier that the root length and angle are directly correlated
419
between 2D culture and sand or soil culture (Liao et al., 2001). The optical flow based analyses
420
for assessment of velocity are sensitive to image quality and parameters governing the
421
calculations. But in this method, the visual demonstration of pattern movement prior to optical
422
flow analysis provides an opportunity to verify the calculated velocities and streak lines against
19
423
the patterns, and correspondingly adjust the optical flow parameters for accurate results.
424
Therefore the new methodology offers possibility of unraveling newer and greater details of
425
developmental and growth patterns and, hence, may prove to be very useful in investigations of
426
tissue growth in biological systems in general.
427
428
The
image
analysis
software
is
available
from
the
website
http://home.iitk.ac.in/~apal/growthexplorer.html.
429
430
431
ACKNOWLEDGEMENTS
432
We thank Dr. Partha S. Basu at the Indian Institute of Pulses Research, Kanpur, India for
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providing the legume seeds. This work was financially supported by a grant from the Fast Track
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scheme by Department of Science and Technology, Government of India (no. SR/FT/LS-
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085/2007).
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REFERENCES
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Armengaud P, Zambaux K, Hills A, Sulpice R, Pattison RJ, Blatt MR, Amtmann A. 2009.
439
Ez-rhizo: Integrated software for the fast and accurate measurement of root system architecture.
440
The Plant Journal 57(5): 945-956.
441
Barrett WA, Mortensen EN. 1997. Interactive live-wire boundary extraction. Medical Image
442
Analysis 1(4): 331-341.
443
Basu P, Pal A, Lynch JP, Brown KM. 2007. A novel image-analysis technique for kinematic
444
study of growth and curvature. Plant Physiology 145(2): 305-316.
445
Canny J. 1986. A computational approach to edge detection. IEEE Transaction Pattern Analysis
446
Machine Intelligence 8(6): 679-698.
447
Chavarria-Krauser A, Nagel KA, Palme K, Schurr U, Walter A, Scharr H. 2008. Spatio-
448
temporal quantification of differential growth processes in root growth zones based on a novel
449
combination of image sequence processing and refined concepts describing curvature
450
production. New Phytologist 177(3): 811-821.
451
Clark RT, MacCurdy RB, Jung JK, Shaff JE, McCouch SR, Aneshansley DJ, Kochian LV.
452
2011. Three-dimensional root phenotyping with a novel imaging and software platform. Plant
453
Physiology 156(2): 455-465.
21
454
Cong G, Parvin B. 1999. An algebraic solution to surface recovery from cross-sectional
455
contours. Graphical Models and Image Processing 61: 222–243
456
Dijkstra EW. 1959. A note on two problems in connexion with graphs. Numerische Mathematik
457
1(1): 269-271.
458
Erickson RO, Sax KB. 1956. Elemental growth rate of the primary root of zea mays.
459
Proceedings of American Philosophical Society 100: 487-498.
460
French A, Ubeda-Tomas S, Holman TJ, Bennett MJ, Pridmore T. 2009. High-throughput
461
quantification of root growth using a novel image-analysis tool. Plant Physiology 150(4): 1784-
462
1795.
463
Hamarneh G, Yang J, McIntosh C, Langille M. 2005. 3d live-wire-based semi-automatic
464
segmentation of medical images. SPIE Medical Imaging 5747: 1597-1603.
465
Kass M, Witkin A, Terzopoulos D. 1987. Snakes: Active contour models. International
466
Journal of Computer Vision: 321-331.
467
Liao H, Rubio G, Yan XL, Cao AQ, Brown KM, Lynch JP. 2001. Effect of phosphorus
468
availability on basal root shallowness in common bean. Plant and Soil 232(1-2): 69-79.
469
Lucas M, Guedon Y, Jay-Allemand C, Godin C, Laplaze L. 2008. An auxin transport-based
470
model of root branching in arabidopsis thaliana. PLoS One 3(11): e3673.
22
471
Miller ND, Parks BM, Spalding EP. 2007. Computer-vision analysis of seedling responses to
472
light and gravity. The Plant Journal 52(2): 374-381.
473
Osmont KS, Sibout R, Hardtke CS. 2007. Hidden branches: Developments in root system
474
architecture. Annual Review of Plant Biology 58: 93–113.
475
Scarano F. 2002. Iterative image deformation methods in piv. Measurement Science and
476
Technology 13(1): R1-R19.
477
van der Weele CM, Jiang HS, Palaniappan KK, Ivanov VB, Palaniappan K, Baskin TI.
478
2003. A new algorithm for computational image analysis of deformable motion at high spatial
479
and temporal resolution applied to root growth. Roughly uniform elongation in the meristem and
480
also, after an abrupt acceleration, in the elongation zone. Plant Physiology 132(3): 1138-1148.
481
Walter A, Silk WK, Schurr U. 2009. Environmental effects on spatial and temporal patterns of
482
leaf and root growth. Annual Review of Plant Biology 60: 279-304.
483
Walter A, Spies H, Terjung S, Kusters R, Kirchgessner N, Schurr U. 2002. Spatio-temporal
484
dynamics of expansion growth in roots: Automatic quantification of diurnal course and
485
temperature response by digital image sequence processing. Journal of Experimental Botany
486
53(369): 689-698.
487
Yazdanbakhsh N, Fisahn J. 2010. Analysis of arabidopsis thaliana root growth kinetics with
488
high temporal and spatial resolution. Ann Bot 105(5): 783-791.
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FIGURE LEGENDS
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Fig. 1. Experimental setup for in vivo root imaging and analysis methodology. (a) Schematic of
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the transparent growth system where the seedling roots grow on a germination paper placed
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between a clear glass sheet in the front and a plastic sheet at the back enforcing nearly planar
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root architecture. (b) The transparent growth system is placed in the plant growth chamber
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divided in two compartments. The lower compartment contains a camera on a tripod that is
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tethered to a computer for time lapse imaging, and flashes. It is kept dark by covering with a
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thick black cloth. The shoot appears in the upper chamber through the cloth and is exposed to
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light (12 h/12 h day/night cycle). (c) A sample image of a growing bean seedling with basal roots
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B1-B6 and lateral roots L1-L2 labeled (no physiological implication in labeling). The black spots
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on the root were added by sprinkling graphite particles to provide patterns for local growth
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estimation. (d) A stack of 9 time series images are shown from a sequence of 81 images. Slicing
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planes P1-P5 are passed through the stack to obtain spatio-temporal image sections on which the
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roots are segmented (cyan contours). Red dots show the intersection of the contours with the
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images and are used as seed points. (e) An example sliced image from plane P3. (f) Using the
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seed points, root images are segmented (green contours). (g) From the edge contours of the root
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images spatio-temporal 3D structure is constructed. The thin green lines enveloping the spatio-
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temporal 3D structure show edge contours of all 81 images. Labels show the structures
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corresponding to roots in (c). (h) Spatio-temporal patterns of graphite particles along the root
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midlines.
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Fig. 2. Spatio-temporal structures constructed from segmented edge contours of (a) basal roots of
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a bean seedling. (b) lateral roots of the same bean seedling and (c) lateral roots of a chickpea
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seedling. At the top of each spatio-temporal 3D structure the last image of the sequence is shown
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to indicate the correspondence between the roots and the spatio-temporal 3D structures.
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Variations in length of selected roots from (a)-(c) are shown in (d)-(f) respectively illustrating
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initiation and temporal changes in growth rate. The roots for which length vs. time data are
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plotted in (d)-(f) are labeled in (a)-(c).
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Fig. 3. Analysis of growth zone of the roots from the spatio-temporal patterns of graphite
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particles along the midlines of (a) a bean basal root, (b) a bean lateral root and (c) a chickpea
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lateral root. The insets at right show the root system and the white arrow points to the specific
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root, the data of which are presented in (a)-(c). The insets at the top show close up of the
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corresponding roots with the midlines marked with white dashed lines. Therefore the gray stripes
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in (a)-(c) are spatio-temporal patterns along these lines. White arrowhead in (b) shows the spatio-
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temporal location where the initial basal growth zone ended and the black arrowhead shows the
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location from where a more apical growth zone evolved. The dark graphite stripes have a change
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in slope at these locations.
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Fig. 4. (a)-(c) Relative elemental growth rate (REGR) along the root midline is shown in space
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and time for the roots shown in Fig. 3(a)-(c) respectively. Both the height and colors of the
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spatio-temporal 3D plots show magnitudes of REGR.
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Fig. 5. (a) Relative elemental growth rate (REGR) of the bean basal root of Fig. 3(a) is shown at
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a later stage. (b) Variations in growth rate of the bean basal root of (a) is shown with time. (c)
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Example of another bean basal root is shown with similar transient growth zones. (d) Growth
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rate of the bean basal root of (c) is shown vs. time.
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Fig. 6. (a) Superimposition of root outlines obtained from a time series of bean basal root
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images. The color dots show root tips and the open circles show root bases. (b) Root tip angles
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measured between gravity vector and the lines joining root tip and base are shown as a function
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of time.
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Fig. 7. (a)-(b) Spatio-temporal structures constructed from segmented edge contours of
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artificially generated root images. The red dots are the seed points using which the edge contours
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(green lines) of the roots were generated. The blue spheres indicate the root tips and the magenta
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lines are root midlines. (c)-(d) Comparison of root lengths between theoretical values used in
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generating the images and calculated values from the edge contours of the image sequences of
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(a) and (b) respectively.
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Fig. 8. (a)-(b) Spatio-temporal maps of midline intensities of the artificial root images. The
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black stripes in the background show the calculated spatio-temporal intensities of the marker
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dots and the overlaying yellow lines show the theoretical spatio-temporal maps of the marker
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dots. (c)-(d) Theoretical REGR distributions of the artificial root images used in generating the
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image sequences of (a) and (b) respectively. (e)-(f) Calculated REGR distributions corresponding
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to (c) and (d) respectively.
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26
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SHORT LEGENDS FOR SUPPORTING INFORMATION
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Fig. S1. Flowchart of the image analysis system.
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Fig. S2. Illustration of vibration stabilization process.
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Fig. S3. Edge detection by livewire algorithm.
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Fig. S4. Identification of root midline using Euclidean distance transform.
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Fig. S5. Root growth velocity and growth trajectories.
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Fig. S6. Examples of spatio-temporal 3D reconstructions of basal and lateral roots.
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Video S1. Time-lapse movie of a growing bean seedling before stabilization showing the effect
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of vibration due to the ventilation fans in the growth chamber. After stabilization, this movie is
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shown as Video S2.
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Video S2. Stabilized time-lapse movie of a growing bean seedling showing the development of
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basal roots during a period of 5 h. Dark spots are graphite particles sprinkled on the roots for
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analysis of local growth.
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Video S3. Movie showing growth of artificially generated root with a unimodal growth zone.
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The black dots were used as marker dots to assess local growth zones.
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Video S4. Movie showing growth of artificially generates root with bifurcating growth zones.
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The black dots were used as marker dots to assess local growth zones.
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Video S5. Time-lapse movie of a bean seedling showing emergence and development of basal
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roots for 48 h.
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Video S6. Time-lapse movie of the same bean seedling of Video Clip 2 for 48 h to 70 h showing
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emergence and growth of lateral roots.
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Video S7. Time-lapse movie showing Initiation and development of lateral roots of a chickpea
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seedling during 72 h.
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Video S8. Time-lapse movie a bean seedling for 124 h showing emergence and growth of basal
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and lateral roots. The faint white rectangles show the segments from where spatio-temporal
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growth patterns of lateral roots were studied in Fig. S6(c) and (d).
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Video S9. Time-lapse movie showing local growth of bean lateral roots during a period of 5 h
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following sprinkling of graphite particles.
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Video S10. Time-lapse movie showing local growth of chickpea lateral roots during a period of 5
580
h following sprinkling of graphite particles.
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