Download Functional genomics analysis of foliar condensed tannin and

Tree Physiology 25, 1475–1486
© 2005 Heron Publishing—Victoria, Canada
Functional genomics analysis of foliar condensed tannin and phenolic
glycoside regulation in natural cottonwood hybrids†
SCOTT A. HARDING,1 HONGYING JIANG,1,2 MIJEONG LEE JEONG,1,3
FANNY L. CASADO,1,4 HAN-WEI LIN1 and CHUNG-JUI TSAI1,5
1
Biotechnology Research Center, School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931,
USA
2
Present address: Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health,
Rockville, MD 20850, USA
3
Present address: Unigen Pharmaceuticals Inc., Lacey, WA 98516, USA
4
Present address: Department of Chemical Engineering, University of Rochester, Rochester, NY 14627, USA
5
Corresponding author ([email protected])
Received October 29, 2004; accepted January 28, 2005; published online September 1, 2005
Summary Regulation of leaf condensed tannins (CT) and
salicylate-derived phenolic glycosides (PG) in fast- and slowgrowing cottonwood backcrosses was analyzed by metabolic
profiling and cDNA microarray hybridization. Seven hybrid
lines of Populus fremontii L. and P. angustifolia James exhibiting growth/CT-PG phenotypes ranging from fast/low (Lines
18 and 1979) to slow/high (Lines 1012 and RL2) and intermediate (Lines NUL, 3200 and RM5) were investigated. Methanol-extractable leaf metabolites were analyzed by gas chromatography–mass spectrometry, and the results evaluated by
principal component analysis. The hybrid lines formed separate clusters based on their primary metabolite profiles, with
cluster arrangement also reflecting differences in CT-PG phenotype. Nitrogen (N) supply was manipulated to alter CT-PG
partitioning and to obtain molecular insights into how primary
metabolism interfaces with CT-PG accumulation. Three backcross lines (RM5, 1012, 18) exhibiting differential CT-PG responses to a 10-day hydroponic N-deprivation treatment were
chosen for metabolite and gene expression analyses. The fastgrowing Line 18 showed a minimal CT-PG response to N deprivation, and a reduction in photosynthetic gene expression.
Line 1012 exhibited a strong phenylpropanoid response to N
deprivation, including a doubling in phenylalanine ammonia-lyase (PAL) gene expression, and a shift from CT accumulation in the absence of stress toward PG accumulation under
N-deprivation conditions. Amino acid concentrations were depressed in Lines 18 and 1012, as was expression of nitrate-sensitive genes coding for transketolase (TK), and malate dehydrogenase (MDH). Genes associated with protein synthesis
and fate were down-regulated in Line 1012 but not in Line 18.
Line RM5 exhibited a comparatively large increase in CT in response to N deprivation, but did not sustain decreases in amino
acid concentrations, or changes in PAL, TK or MDH gene ex-
pression. Molecular characterization of the variable CT-PG responses shows promise for the identification and future testing
of candidate genes for CT-PG trait selection or manipulation.
Keywords: growth and fitness, microarray, metabolic profiling, nitrogen stress, Populus.
Introduction
Fast-growing tree species of the genus Populus typically maintain constitutive and potentially growth-compromising foliar
sinks of flavonoid-derived condensed tannins (CT) and salicylate-derived phenolic glycosides (PG). Both CT and PG
contribute to plant fitness by protecting the photosynthetic
light-harvesting apparatus from harmful UV-B radiation (Lavola 1998, Hoch et al. 2000) and by deterring insect defoliators
(Bryant et al. 1993, Lindroth and Hwang 1996). In ecosystems
dominated by Populus, these phenolics condition insect diversity, litter decomposition and site productivity (Bradley et al.
2000, Driebe and Whitham 2000). These important compounds routinely comprise 10 to 35% of Populus leaf dry mass
(Lindroth and Hwang 1996), representing a large commitment
of metabolic resources for protection and defense, one that has
evoked considerable discussion over the costs of plant defense
(e.g., Bryant et al. 1983, Tuomi et al. 1984, Herms and Mattson
1992). Differences in steady-state CT-PG concentrations in
leaves correlate negatively with stem growth among aspen
(Populus tremuloides Michx.) clones maintained for several
years at one site (Lindroth and Hwang 1996). The implications
are that constitutively high CT-PG concentrations, or the added cost of stress-induced accumulations of phenolics (Kaitaniemi et al. 1998, Ruuhola et al. 2001), may compromise tree
growth. Whether such a commitment is necessary for defense,
† This paper was among those presented at the 18th North American Forest Biology Workshop, which was sponsored by the Society of American
Foresters and the USDA Forest Service, and hosted by the Michigan Technological University, Houghton, MI, July 11–15, 2004.
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or costly enough to interfere with biomass productivity (i.e.,
growth), is unclear and not understood at the mechanistic
level, but it raises questions about efforts for improving plantation productivity through manipulation of tree carbon use.
Steps of the flavonoid pathway giving rise to CT have been
elucidated and promoter analysis of the genes involved has led
to the identification of several regulatory factors (e.g., Winkel-Shirley 2001, Mathews et al. 2003, Xie et al. 2003). In contrast, less is known about PG synthesis. Multiple pathways
have been implicated in the biosynthesis of salicylic acid (Ribnicky et al. 1998, Wildermuth et al. 2001), and although several PGs, PG precursors and PG conjugates have been identified, enzymatic steps for the elaboration of PGs from
salicylate remain undefined (Lindroth and Hwang 1996, Ruuhola and Julkunen-Tiitto 2003). Improved understanding of
CT-PG costs to tree growth depends on a better understanding
of their biosynthetic pathways and their molecular integration
with growth regulating networks. In the absence of molecular data, the carbon-nutrient-balance (CNB) hypothesis is a
tested, though limited, model for expressing growth and secondary metabolism trade-offs (Bryant et al. 1983, Haukioja et
al. 1998, Hamilton et al. 2001). According to the CNB hypothesis, the diversion of photosynthate (carbon) into phenolic
sinks is favored when light is plentiful, and growth is limited
by nutrients or other stresses. A supplemental view, the protein
competition model (PCM), was developed to accommodate
subsequent advances in the molecular regulation of phenylpropanoid metabolism, and posits that protein and phenylpropanoid biosynthetic pathways compete dynamically for
plant nitrogen (N) according to prevailing demands of development or stress response (Jones and Hartley 1999). Both
CNB and PCM thus offer a conceptual framework supported
by numerous field and controlled environment observations
(Haukioja et al. 1998) for molecular analysis of CT-PG metabolism in tree species. The importance of N availability and
C:N ratio to carbohydrate sensing and signaling networks that
regulate photosynthesis, development, carbon allocation and
partitioning for stress response and growth in herbaceous species is already being elucidated (e.g., Paul and Driscoll 1997,
Rolland et al. 2002, Sun et al. 2002, Paul and Pellny 2003, and
references therein). A limited body of physiological and enzymatic data has recently emerged, supporting the view that CT
accumulation depends on carbohydrate translocation and sink
strength in hybrid poplar (Arnold and Schultz 2002, Arnold et
al. 2004).
In the present work, we exploited the wide variation in
growth rates and phenolic reserves observed among Fremont
(P. fremontii L.) and narrowleaf (Populus angustifolia James)
cottonwoods and their natural hybrids (Keim et al. 1989, Martinson et al. 2001, Whitham et al. 2003), and began to analyze
the molecular regulation of CT-PG for normal and N-limited
growth in relation to the tenets of CNB, based on a functional
genomics approach. A difference between the hybrid lines analyzed was that the timing of carbon partitioning into CT-PG
varied relative to the onset of N-stress symptoms. This will be
discussed as it pertains to CNB predictions of growth consequences.
Materials and methods
Plant materials
Hybrids of Fremont and narrowleaf cottonwoods, originating
from a hybrid zone along the Weber River and maintained in a
common garden in Ogden, Utah, were provided by T.G. Whitham (Dept. Biological Sciences, Northern Arizona University,
Flagstaff, AZ). They have been characterized genetically as
pure, F1 hybrids or backcrosses based on 35 random fragment
length polymorphism (RFLP) markers (see Martinsen et al.
2001). Hybrid lines were selected for our study based on foliar
CT-PG concentrations in common-garden-grown trees, and
include F1 (Line 1979), backcross (Lines 18, 1012, NUL, RL2
and RM5) and narrowleaf (Line 3200) cottonwoods (T.G.
Whitham, personal communication). Backcrosses occur unidirectionally by F1 hybrid introgression into narrowleaf cottonwoods (Keim et al. 1989). Cuttings from the common garden were collected in spring 2003 and rooted in a greenhouse
at Michigan Technological University. In vitro micropropagated (Tsai et al. 1994) plantlets were transferred to 7.5-cm
pots filled with a peat:perlite mix (1:1, v/v), and acclimated in
a mist chamber for 2 weeks before being planted in a greenhouse. Acclimated plantlets were repotted in 2-l containers
filled with a 2:1:1 (v/v) mix of topsoil:peat moss:perlite, watered daily, and fertilized every 2 weeks with Miracle-Gro
all-purpose plant food (N,P,K 15,30,15, Scotts, OH) and iron
chelate micronutrient (Sprint 330, Becker Underwood, IA).
Leaves (80–90% expanded, and exhibiting no signs of senescence) were harvested from 1.5- to 2-m-tall pot-grown plants
during midsummer, snap-frozen in liquid N and stored at
–80 °C. Leaf tissues were ground to a fine powder with liquid
N, aliquoted and stored at –80 °C for RNA and chemical extraction.
Hydroponic nitrogen treatments
Micropropagated plants were acclimated in a mist chamber
before transfer to 10-cm perlite pots for hydroponic growth.
Plants were maintained in aerated nutrient solution formulated
for temperate tree species (Murray et al. 1996), and modified
for Populus (Wait et al. 1996). Nutrient solution was replaced
every 3–4 days. Hydroponically adapted plants, about 1 m in
height, were either maintained in full strength N (2.5 mM, referred to as N+, or control) or transferred to one-tenth strength
N (0.25 mM, referred to as N–, or stressed) for 10 days. Newly
expanded leaves, having completed 80–90% of their expansion during N treatment, and showing no signs of senescence,
were harvested, ground to powder in liquid N, aliquoted and
stored at –80 °C until analyzed.
Analysis of foliar total phenolics, condensed tannins and
phenolic glycosides
Methanolic extracts of frozen leaf powder were analyzed for
CT and PG. Briefly, about 30 mg frozen tissue powder was extracted in 600 µl of methanol for 15 min in an ultrasonic bath
and centrifuged at 15,000 g for 10 min. The methanol extract
was used for PG and soluble CT analyses, and the pellet was
resuspended in 250 µl of methanol for measurements of insol-
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FUNCTIONAL GENOMICS OF PHENOLICS IN POPULUS
uble (bound) CT. Methanol-soluble (100 µl extracts) and
bound CTs were quantified based on A550 (Porter 1986, Tiarks
et al. 1992) using aspen leaf CT standards purified according
to Hussein et al. (1990). Foliar PG concentrations were estimated based on alkaline hydrolyzable salicin in the leaf methanolic extracts by high-performance thin-layer chromatography (HPTLC). Briefly, 200 µl of methanolic extract was
saponified with 20 µl of sodium carbonate (0.5 M) at 60 °C for
10 min, clarified by centrifugation, and 10 µl was applied to
HPTLC Merck silica gel 60 F254 plates (Fisher, Hanover Park,
IL). Plates were developed in ethyl acetate:methanol:water
(77:13:10, v/v) as described by Meier et al. (1987), and documented under UV 254 nm with a ChromaDoc-It TLC imaging
system (UVP, Upland, CA). Salicin concentrations were estimated from standard curves of salicin obtained from Sigma
(St. Louis, MO).
GC–MS metabolite profiling
Frozen leaf powder (~20 mg) was suspended in 1.5 ml of
methanol:chloroform:H2O (12:5:3, v/v), briefly sonicated,
shaken vigorously for 2 h at 4 °C on an orbital shaker, extracted twice with 0.5 ml of hexane at room temperature in an
ultrasonic bath to remove pigments and dried under vacuum.
Methoximated and N-methyl-N-(trimethylsilyl)-trifluoroacetamide (MSTFA)-derivatized extracts (1 µl) were injected in
splitless mode using split/splitless inlet liners packed with deactivated glass wool (Supelco, Bellefonte, PA) according to
our established protocol (Jeong et al. 2004). Gas chromatography (GC) was performed on a 6890 GC/5973N system (Agilent Technologies, Wilmington, DE) equipped with an AT5MS column (30-m × 0.25-mm ID with 0.25-µm film thickness) with a deactivated guard column (Alltech, Deerfield, IL).
Conditions for GC, temperature program, mass spectra (MS)
recording, instrument maintenance and mass spectral data
analysis procedures were as described by Jeong et al. (2004).
Means and SEs of normalized metabolite peak areas for each
line were calculated based on the mean value of duplicate analyses of leaves from 6–8 plants (pot experiments) and four
plants (hydroponic N-deprivation experiments). Principal
component analysis (PCA) of normalized metabolite peak
areas was performed using R statistical software (http://www.
r-project.org).
Construction of aspen cDNA microarray
The microarray used in this study contained replicate subarrays of 6313 previously characterized aspen expressed
sequence tags (ESTs) (Ranjan et al. 2004). The array also contained numerous internal control cDNAs related to monolignol, flavonoid and cellulose biosynthesis, and a series of
spike controls (Lucidea Universal ScoreCard, Amersham, Piscataway, NJ) to monitor target labeling and hybridization efficiency (the gene list and annotation can be found at http://
aspendb.mtu.edu/download_frame.html). Inserts of cDNA
clones were amplified by PCR with the M13 forward and reverse primers, and then the quality checked by agarose gel
electrophoresis. The PCR products were purified by ethanol
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precipitation, dried, resuspended in 20 µl of Corning Pronto!
spotting solution (Fisher) and transferred to 384-well plates
for arraying. The cDNAs were spotted in duplicate on amino
silane glass slides (Corning UltraGAPS, Fisher) with a 16-pin
print head and a GeneMachines OminGrid robot at the Genomics Technology Support Facility of Michigan State University. Spotted cDNAs were immobilized by UV crosslinking
and snap-baked for 1 min at 120 °C before use.
Microarray hybridization
Total RNA (20 µg) extracted from cottonwood leaves by the
CTAB (cetyl trimethyl ammonium bromide) method (Chang
et al. 1993) was used for cDNA synthesis in the presence of
aminoallyl-modified dUTP (Sigma) using anchored oligo
(dT)20 primer and SuperScript III reverse transcriptase (Invitrogen, Carlsbad, CA). Aminoallyl-modified cDNA was
purified with the QIAquick PCR purification kit (Qiagen, Valencia, CA) and resuspended in 0.1 M sodium carbonate buffer
(pH 9.0) for chemical coupling with Cy3 or Cy5 dye (Amersham). The fluorescent-labeled targets were purified with the
QIAquick PCR purification kit (Qiagen), and cDNA concentration and Cy dye labeling efficiency measured with a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies,
Wilmington, DE). The labeled targets were vacuum-dried,
stored at –20 °C and resuspended in hybridization buffer (50%
formamide, 5× SSC, 0.4% SDS and 0.1% BSA) before use.
Hybridization was carried out in an automated hybridization
station (Tecan, Maennedorf, Switzerland) at 40 °C for 36 h
(Sartor et al. 2004) using equal amounts of Cy3- and Cy5-labeled targets.
Microarray data analysis
The slides were scanned with a GenePix 4000B Scanner
(Axon Instruments, Union City, CA) and the fluorescence intensity quantified with the GenePix Pro 5.1 image analysis
software (Axon). Fluorescence intensities were computed by
subtracting local median background from median signal in
both channels. Hybridization signals satisfying the following
criteria were used for expression analysis: (1) intensity in both
channels greater than two standard deviations above local
background; (2) detection in half of the data points from each
experiment (see below); and (3) within-treatment coefficientof-variation (CV) less than 25%. Data for the two channels
were normalized with the LOWESS (locally weighted linear
regression) procedure (Clevel and Devlin 1988, Yang et al.
2002) using GeneSpring 6.2 software (Silicon Genetics, Redwood City, CA). For each experiment (between-line comparisons or within-line N effects), differentially expressed genes
were selected by one-way analysis of variance (ANOVA) with
multiple-testing correction at a false discovery rate of P =
0.001, and a threshold ratio cutoff of 1.5. Hierarchical cluster
analysis (HCA) was conducted using standard correlation as
the distance measure with the GeneSpring software (Silicon
Genetics).
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HARDING, JIANG, JEONG, CASADO, LIN AND TSAI
Experimental design
Six to eight pot-grown individuals of each hybrid line were
compared for CT-PG concentrations and metabolic profiles
among the seven cottonwood lines (n = 6–8). Lines 18, 1012
and RM5 were selected for comprehensive analysis of N treatment effects under hydroponic conditions, with four individuals from each of the N+ and N– treatments. Two recently
expanded leaves with a healthy appearance from each plant
were pooled for analyses. The CT-PG analysis and metabolic
profiling were performed for each plant (n = 4). For microarray analysis to evaluate between-line gene expression differences, frozen leaf powders were further pooled from two
randomly selected control (N+) plants for RNA extraction and
target preparation. Each hybridization was replicated with dye
reversal using a balanced loop design (i.e., Line 18 versus
1012; Line 1012 versus RM5 and Line RM5 versus 18), resulting in four technical replicates (2 slides × 2 subarrays per
slide) for each comparison. For evaluation of gene expression
changes during N starvation, two biological replicates (i.e.,
two independent pools of two plants each) were used for each
within-line comparison (N– versus N+), resulting in four data
points per array feature (2 biological replicates × 2 subarrays
each). For Lines 1012 and RM5, an additional technical replication with dye reversal was conducted, giving rise to six data
points per array feature.
and RL2 grew at about 80% of the rate of the other high CT-PG
lines.
Metabolite analysis by GC–MS
To determine whether leaf general metabolism differed among
the seven lines, metabolite profiles from methanol:chloroform:water (12:5:3, v/v) extracts were obtained by GC–MS.
Gas chromatography efficiently detects sugars, amino acids
and intermediates of glycolytic, Krebs cycle and photorespiratory activities in such extracts (Fiehn et al. 2000). We then
used PCA to cluster the various cottonwood lines on the basis
of their leaf metabolite profiles (Figure 2A). Over 100 identi-
Results
CT-PG characterization
When micropropagated plantlets were grown to heights of
1.5–2 m under greenhouse conditions, CT-PG concentrations
in expanding leaves ranged from 2 to 5.7% on a fresh mass
basis (Figure 1). Assuming a leaf water content of 80%, foliar
CT-PG concentrations ranged from 10 to 28.5% on a dry mass
basis. Based on changes in height increment over time, the
low-PG lines 18 and 1979 grew about twice as rapidly as the
high CT-PG lines under greenhouse conditions. Lines 1012
Figure 1. Foliar condensed tannin (CT) and salicylate-derived phenolic glycoside (PG) concentrations of pot-grown plants of seven cottonwood lines. The plants were maintained in a greenhouse. Error bars
represent SE of the mean (n = 6–8 plants).
Figure 2. Clustering of the seven cottonwood lines by principal component analysis (PCA) based on all identified metabolites (A) or 43
primary metabolites (B) from gas chromatography–mass spectroscopy. Total sample variances explained by the first two PCA vectors
are indicated.
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FUNCTIONAL GENOMICS OF PHENOLICS IN POPULUS
fied metabolites were used in the analysis. All metabolites previously used to profile sink–source relationships in developing
aspen leaves (Jeong et al. 2004) were detected in our study.
General leaf metabolism clearly differed among the CT-PG
phenotypes, with the three low-CT (18, 1979 and NUL) lines
readily distinguishable from the other lines based on the first
component (x-axis). The concentration of PG appeared to influence clustering as well, because the three low-CT lines
were also separable along the second component (y-axis) on
the PCA plot.
To determine whether CT-PG abundance and primary metabolism interacted in a way that would be discernable by cluster analysis, we performed PCA on a subset of 43 primary
metabolites (amino acids, sugars, glycolytic and respiratory
intermediates; Figure 2B). The cottonwood lines appeared to
differ in their primary metabolic activity, and the clusters
maintained a spatial grouping suggestive of a relationship between primary metabolic activity and CT-PG abundance. In
other words, high-CT lines 3200 and 1012 clustered near one
another, whereas the low-CT lines 18, 1979 and NUL formed a
separate group. The location of the RL2 cluster at a great distance from the other high-CT lines was consistent with a possible additive effect of high PG and high CT on primary
metabolism. The concentration of PG also appeared to affect
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the clustering of the moderate-CT Line RM5, which grouped
near the high-CT lines. Ultimately, the three lines with the
highest PG concentrations, NUL, RL2 and RM5, clustered far
apart from one another, apparently more in line with the large
differences in their CT concentrations, than their shared feature of high PG concentration.
The results of PCA clustering provided an indication that
primary and CT-PG metabolism interfaced in a complex way
that was at least partially explained by differences within the
suite of primary metabolites we routinely detected. The CTPG concentration appeared to correlate negatively with amino
acid concentration (Figure 3A). In the low-CT group, Line
1979 generally exhibited slightly lower amino acid concentrations. Among the high-CT lines, Line RL2 exhibited strikingly
low amino acid concentrations, and Line 3200, ranking second
in CT concentration, also exhibited somewhat lower amino
acid concentrations than other lines (Figure 3A). The concentrations of most Krebs cycle intermediates differed little
among lines, but fumarate concentration was much lower in
Line RL2 than in the other lines (Figure 3B). Among the
high-CT lines, the Gly:Ser ratio, determined as an index of
photorespiratory activity (Novitskaya et al. 2002), was also
much higher in Line RL2 (0.80 ± 0.36) than in other lines
(0.21 ± 0.11, 0.39 ± 0.24 and 0.20 ± 0.06 for Lines 1012, 3200
Figure 3. Histograms of amino acids (A) and Krebs cycle intermediates (B) in methanolic leaf extracts of high and low foliar condensed tannin
(CT), pot-grown cottonwood lines. Single-letter amino acid codes are also provided along the x-axis in panel A. Refer to the right-hand y-axis in
panel B for comparing fumarate concentrations. Error bars represent SE of the mean (n = 6–8 plants).
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HARDING, JIANG, JEONG, CASADO, LIN AND TSAI
Figure 4. Effects of nitrogen (N) starvation on foliar condensed tannin
(CT) and salicylate-derived phenolic glycosidase (PG) concentrations
of hydroponically grown cottonwood Lines 18, RM5 and 1012. Abbreviations: N+ = control plants; N– = N-stressed plants. Error bars
represent SE of the mean (n = 4 plants).
and RM5, respectively).
Effects of N deprivation on CT-PG and overall metabolism
Poor soil and drought can adversely affect N acquisition (e.g.,
Bahrun et al. 2002) and N assimilation (e.g., Kaiser and
Forster 1989, Champigny 1995, Medrano et al. 2002). In turn,
tree N nutritional status is posited by CNB and PCM to be an
important determinant of carbon allocation for CT-PG (Bryant
et al. 1983, Jones and Hartley 1999). To manipulate N availability in the root zone, micropropagated plantlets were grown
to a height of ~1 m in aerated hydroponics tanks in nutrient solution containing 2.5 mM N. Ten days before sampling, four
plants of each line were transferred to nutrient solution containing 0.25 mM N. Based on a preliminary screening of the
effects of the 10-day N-deprivation treatment on CT-PG pools,
three cottonwood lines were chosen for more detailed analyses
of gene expression and metabolic activity (Figure 4). Line
1012 was exceptional among the high-CT lines in that CT did
not increase during N stress. Instead, PG, previously unde-
tected in field (T.G. Whitham, personal communication),
greenhouse pot-grown plants (Figure 1) or N+ hydroponic
controls (Figure 4), accumulated to readily detectable
amounts. Line RM5 exhibited a large CT increase and a slight
decrease in PG in response to N deprivation. Line 18 exhibited
little change in CT-PG in response to N deprivation.
Although PCA clustering separated N+ from N– plants
(data not shown), the most striking examples of differential N
effects were changes in amino acid concentrations and a large
change in catechol concentration (Figure 5). Amino acid concentrations tended to decrease with N stress in Lines 18 and
1012, but not in Line RM5. Catechol concentration correlated
with the PG response during N stress. In Line RM5, where PG
decreased during N deprivation, catechol concentrations also
decreased. In Line 1012, catechol concentration increased
more than 10-fold during PG induction. Catechol concentration appeared to correlate more closely with active PG synthesis or turnover than with differences in the steady-state
concentration of PG, because no differences in catechol concentrations were detected among pot-grown lines (data not
shown).
Global gene expression analysis
Gene expression was analyzed with an aspen cDNA microarray. The array contains a subset of 6313 ESTs from a collection of over 11,000 aspen ESTs derived from apices, young
stems, young leaves and root tips (Ranjan et al. 2004). These
ESTs represent about 4560 unique sequences, corresponding
to ~4000 unique proteins (i.e., some proteins are represented
by multiple, non-overlapping unique sequences) based on the
annotation of Ranjan et al. (2004). Labeled cDNA from the
three cottonwood lines hybridized to the arrays as efficiently
as labeled aspen cDNA (Harding, Jiang and Tsai, unpublished
data), with 74 to 81% of spots passing our filtering criteria (see
Materials and methods). This was expected given the high degree of sequence homology (> 95%) among various Populus
species (Wullschleger et al. 2002). In all, 3882 array ESTs hybridized to cDNA from all three cottonwood lines, with a
Figure 5. Effects of nitrogen (N) starvation on amino acid and catechol concentrations in leaf methanolic extracts of hydroponically grown cottonwood Lines 18, RM5 and 1012. Data obtained from control (N+) and stressed (N–) plants are presented in solid and cross-hatch bars, respectively.
Refer to the left-hand y-axis for catechol, proline and glutamate comparisons. Error bars represent SE of the mean (n = 4 plants).
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FUNCTIONAL GENOMICS OF PHENOLICS IN POPULUS
mean CV of 9–12%. One-way ANOVA with multiple-testing
correction (P = 0.001) and a minimum expression change of
1.5-fold indicated that 377 ESTs were differentially expressed
between any two cottonwood lines.
Between-line differences in gene expression are depicted in
Figure 6. The Venn diagram illustrates the number of unique
gene sequences that were differentially expressed among
non-stressed plants. The most frequently up- or down-regulated genes in one line relative to the other two lines are depicted by their functional classification in the attached histograms. Most of the differences in gene expression between
Line 1012 and the other two lines were for genes associated
with secondary metabolism, whereas Line 18 differed from
the other two lines most notably in genes associated with general metabolism. A non-redundant list of differentially expressed genes associated with secondary metabolism and
amino acid biosynthesis is shown in Table 1. Several differences in phenylpropanoid pathway gene expression appeared
to correlate with between-line differences in leaf phenolic status. For example, several genes of the flavonoid biosynthetic
Figure 6. Venn diagram comparisons of genes differentially expressed
between cottonwood lines. Up-regulated genes are indicated in red
and down-regulated genes in blue. The numbers in the overlapping areas indicate genes that exhibited differential expression for the given
pairwise comparison. The number 12 in the 3-line overlapping area
indicates that 12 genes were differentially expressed in all three
pairwise comparisons. The numbers in the non-overlapping areas indicate genes that were uniquely up- or down-regulated in that line
relative to the other two lines. The three or four best-represented functional classes are depicted in histograms. Genes were selected with a
> 1.5-fold expression change cutoff and by one-way ANOVA (P =
0.001). Only non-redundant genes are presented.
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pathway were more abundantly expressed in the high-CT line
1012 than in Line 18 or Line RM5. Several core amino acid
biosynthesis genes (Table 1), such as those for glutamine
synthetase (GS) and glutamate dehydrogenase (GDH), as well
as genes coding for CTP synthase, carbamoyl phosphate
synthase, and the ACT domain protein, which is potentially involved in the regulation of GS (Hsieh et al. 1998, reviewed in
Miflin and Habash 2002), were differentially expressed. With
respect to poising, the elevated expression of the genes coding
for GS and GDH in Line 1012 may reflect line-to-line differences in the allocation and utilization patterns of uptake N, and
N from other sources including phenylalanine ammonia-lyase
(PAL) activity and photorespiration (Suarez et al. 2002).
The N-deprivation treatment affected gene expression differently in the three lines (Figure 7A). A breakdown of the responsive genes by functional classification is presented for
each line in Table 2. The number of secondary metabolism
genes responsive to N stress was higher in Line 1012 (18) than
in Line RM5 (10) or Line 18 (2). A nonredundant list of differently regulated genes is shown in Figure 7B. In response to N
stress, expression of the gene coding for phenylalanine ammonia-lyase (PAL1) increased twofold in Line 1012, along with
further increases in the flavonoid pathway genes coding for
leucoanthocyanidin dioxgenase and flavonoid 3′,5′-hydroxylase, which already exhibited higher basal expression in Line
1012 than in the other lines in non-stressed plants (Table 1).
Associated with the absence of a CT response in Line 1012,
the gene for anthocyanidin reductase, which is required for CT
biosynthesis (Xie et al. 2003), was not up-regulated during N
stress, but instead, two flavonol 3-O-glucosyltransferase genes
were up-regulated. Line RM5, which exhibited the clearest CT
response to N stress among the three lines, exhibited no increase in gene expression for PAL1, but a number of flavonoid
biosynthetic genes, including those coding for chalcone
synthase, flavonoid 3′,5′-hydroxylase, leucoanthocyanidin dioxygenase and anthocyanidin reductase were up-regulated by
the N treatment. The small N-stress-induced CT-PG response
in Line 18 appeared to concord with the induction of a comparatively small subset of genes associated with secondary metabolism.
One elicitor-inducible cytochrome P450 monooxygenase
gene was strikingly up-regulated by N stress, but only in Line
1012 (Figure 7B). The CYP monooxygenase family of genes
are active in lignin, lignan, hormone, sterol, terpene and
flavonoid biosynthesis (for review, see Schuler 1996). A gene
related to phenylcoumaran benzylic ether reductases, which
are associated with the reduction of benzylic ethers derived
from the phenylpropanoid pathway (Gang et al. 1999), was
relatively highly expressed in non-stressed Line 1012 leaves
(Table 1), but was not further up-regulated by N stress. In contrast, a broad substrate-specific benzoyl CoA:benzyl alcohol
benzoyl transferase associated with the wound induction of
defensive volatiles (D’Auria et al. 2002), was up-regulated by
N stress in Line 1012 (Figure 7B). Additional acyltransferases
that use flavonoid intermediates as substrates (Fujiwara et al.
1997) were also up-regulated by N stress in Line 1012. Overall, the changes in phenylpropanoid-related gene expression in
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HARDING, JIANG, JEONG, CASADO, LIN AND TSAI
Table 1. List of representative genes that were differentially expressed among cottonwood lines.
Clone ID
(NREF ID) 1
Putative function
1.4
1.8
NF00676034
NF00796438
0.9
1.2
0.8
1.6
1.7
2.7
0.7
0.9
0.6
1.1
1.0
0.8
NF00993533
NF00627966
NF00661556
NF00993521
NF00684359
NF00876815
CTP Synthase, putative
5-Methyltetrahydropteroyltriglutamatehomocysteine S-methyltransferase
Carbamoyl phosphate synthase large subunit
Cysteine synthase
Dihydroxy-acid dehydratase, putative
Glutamate dehydrogenase
Glutamine synthetase
ACT Domain protein (uridylyltransferase-re-
0.4
0.5
0.6
0.7
0.7
0.7
1.4
1.5
1.5
1.5
0.5
0.8
1.0
1.0
1.2
1.3
0.9
3.5
3.5
2.2
0.9
1.3
1.6
1.7
1.9
1.9
0.6
2.4
2.4
1.4
NF01101549
NF00629408
NF00665039
NF01116961
NF00993180
NF00629447
NF01201088
NF00629386
NF00627495
NF00673919
1.6
1.8
2.3
2.3
2.7
2.9
3.6
3.6
4.2
6.3
1.9
1.2
2.2
2.3
2.6
3.2
4.5
7.2
3.2
3.1
1.3
0.7
1.2
1.2
1.1
1.2
1.1
1.7
1.0
0.4
NF01216836
NF00739939
NF00857011
NF00145034
NF00220261
NF01038413
NF01328192
NF01218169
NF00466263
NF00663358
1012
1012
18
18
RM5
RM5
0.5
0.6
0.7
0.8
1.5
1.5
1.6
1.7
1.8
3.6
Secondary metabolism
MTU5TS.P22.H10
MTU6CR.P1.F05
MTU7CL.P11.G03
MTU5CS.P18.B05
MTU7TL.P11.E12
MTU2TA.P2.C07
MTU7CL.P3.E04
U27116
MTU5TS.P25.B11
MTU7CL.P2.B08
MTU6CR.P9.G05
MTU4CA.P23.F01
MTU6CR.P18.E07
MTU7CL.P10.A04
MTU6TR.P15.H07
MTU6CR.P18.B06
MTU5CS.P8.B12
MTU6CR.P10.A11
MTU2CA.P9.D07
MTU7CL.P7.A09
Amino acid biosynthesis
MTU6TR.P18.B12
MTU2TA.P13.G04
MTU7CL.P5.H02
MTU2TA.P1.B04
MTU4CA.P22.C09
MTU6CR.P10.C11
MTU6CR.P10.C09
MTU6CR.P13.F10
lated)
Line 1012 in response to N stress were consistent with increased flux into the pathway at the step catalyzed by PAL, increased glycosylation of flavonoid intermediates, and a re-
Benzoyl CoA:benzyl alcohol benzoyl transferase
Cytochrome P450 oxydoreductase (CPR2)
Glycosyltransferase family protein
S-Adenosylmethionine synthase
S-Adenosylmethionine decarboxylase
Peroxidase
Hydroxycinnamoyl transferase
Caffeoyl-CoA O-methyltransferase
Peroxidase
Oxidoreductase, 2OG-Fe(II) oxygenase family
protein
Dihydroflavonol reductase
Flavonol 3-O-glucosyltransferase 2
Chalcone isomerase family protein
Phenylcoumaran benzylic ether reductase
Flavanone 3-hydroxylase
Leucoanthocyanidin dioxygenase
Flavonoid 3′,5′-hydroxylase
Anthocyanidin reductase
Chalcone synthase
Berberine bridge enzyme, putative
balancing of the relative activities of other flavonoid and
phenylpropanoid pathways at the expense of enhanced CT
production. This contrasts with the other lines, including the
Table 2. Functional classification of differentially regulated genes during nitrogen stress in each cottonwood line.1
Functional class
Protein synthesis
Protein fate
General metabolism
Energy metabolism
Secondary metabolism
Cell rescue and defense
Transport
Others
Unclassified
Total
1
1012
18
RM5
Up
Down
Up
Down
Up
Down
1
12
23
8
13
11
7
22
23
120
43
13
16
10
5
6
3
7
9
112
–
–
6
–
2
1
3
11
7
30
–
1
17
14
–
2
2
2
6
43
–
–
–
–
9
–
–
3
1
13
–
–
–
–
1
–
–
–
1
2
Only nonredundant genes (based on NREF annotation) are listed.
TREE PHYSIOLOGY VOLUME 25, 2005
FUNCTIONAL GENOMICS OF PHENOLICS IN POPULUS
1483
Figure 7. Hierarchical cluster analysis of 421 expressed sequence tags (ESTs) differentially regulated by N stress (A). Each EST is represented by
a single row of colored bars and the columns represent the cottonwood lines. A color scale for fold-change (N–/N+) is provided at the bottom: red
indicates up-regulation and blue down-regulation by N stress. Functional classification of the corresponding non-redundant genes is presented in
Table 2. Four functional groups are highlighted by vertical lines on the side and representative genes are listed. Black denotes the protein synthesis
and protein fate groups, blue the energy metabolism class, and red the secondary metabolism group. Selected genes involved in phenolic metabolism and amino acid biosynthesis are shown in B.
CT-responsive line, RM5, where PAL expression remained
constant in response to N stress, and a redistribution of phenylpropanoid metabolites in favor of CT appeared to develop at
the expense of PG accumulation (Figures 4 and 5).
The number of stress-responsive N uptake and assimilation
genes was not as large as the number associated with secondary metabolism. However, N stress decreased the expression
of genes coding for enzymes associated with nitrate-inducible,
general/energy metabolism such as transketolase and malate
dehydrogenase (Wang et al. 2000) only in Lines 1012 and 18
(Figure 7A), where amino acid concentrations also decreased.
No changes were detected in GS expression, although the expression of two genes potentially bearing on the modulation of
GS activity, AMP-binding protein and alanine aminotrans-
ferase (reviewed by Miflin and Habash 2002) were responsive
to N manipulation (Figure 7B). The machinery of protein
biosynthesis and protein fate was most strikingly affected in
Line 1012, where the expression of numerous ribosomal proteins and high molecular weight heat shock protein (HSP)
chaperones decreased in response to N stress (Table 2, Figure 7A). Energy metabolism appeared to be most affected in
Lines 18 and 1012. Although genes associated with the photosynthetic light-harvesting apparatus, photosynthetic electron transport and carbon fixation were uniformly down-regulated by the N-deprivation treatment in Line 18, the scenario
was mixed in Line 1012 (Table 2, Figure 7A). In response to N
stress, several chlorophyll-binding proteins and accessory
proteins of the photosynthetic apparatus were up-regulated,
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HARDING, JIANG, JEONG, CASADO, LIN AND TSAI
whereas expression of the gene for the Rubisco small subunit
remained unaffected (Figure 7A).
Discussion
We applied a functional genomics approach toward an integrated analysis of gene expression patterns and phenolic sink
management strategies among related cottonwood backcross
lines. The hybrid lines have already been characterized on the
basis of RFLPs relative to the parent P. fremontii (Keim et al.
1989, Martinson et al. 2001), leaf phenolic content (Whitham
et al. 2003), seed size, seed abundance and ramet formation
(Schweitzer et al. 2002), and water-use efficiency (Fischer et
al. 2004). A subset of lines was chosen from this collection on
the basis of clear differences in CT-PG concentration and composition. Stress imposed by the N-deprivation treatment
yielded adaptive CT-PG responses ranging from a negligible
increase (Line 18) to an increase in CT (Line RM5) or an increase in PG (Line 1012), and also led to net reductions in the
steady-state concentrations of amino acids in Lines 18 and
1012. Leaf physical condition, assessed on the basis of sustained turgor and minor chlorophyll loss, was similar among
lines at the end of the N-stress period. Bearing in mind the caveats of transcriptome interpretation, particularly of correlating gene expression with protein activity (Gygi et al. 1999),
several between-line differences in the expression of genes associated with secondary metabolism and amino acid metabolism were noted.
If sensitivity to low-N is gauged by the severity of amino
acid pool depletion, and by the down-regulation of nitrate-inducible genes of primary metabolism, Line RM5 appeared to
be less N-stressed than the other lines (Figure 5). However,
RM5 generated a readily detected CT response to N stress.
Analysis of N-stress effects on phenylpropanoid gene expression revealed a repartitioning of carbon within the phenylpropanoid pathway, but without a costly increased flux into
that pathway. In contrast, PAL gene expression increased in response to N stress in Line 1012, consistent with an increased
allocation to that pathway, and perhaps indicative of a more
costly response in that line. In Line 1012, a large change in
catechol concentration detected by GC–MS metabolic profiling correlated with increased PG, a metabolically more expensive sink to maintain than CT (Kleiner et al. 1999).
There appeared to be either an adaptive reorganization or a
general decline in maintenance of the photosynthetic machinery in Line 18 compared with the other lines in response to N
deprivation (Figure 7). In addition to the noted down-regulation of Rubisco and chlorophyll a/b binding proteins, carbonic
anhydrase (CA) gene expression was also decreased by the
N-deprivation treatment. Carbonic anhydrase regulates the
equilibrium between CO2 and HCO3 in the cell and may contribute to plant fitness by modulating the partial pressure of
CO2 in leaves under varying external conditions (Price et al.
1994, Williams et al. 1996). In Line 18, therefore, the limited
availability of carbon skeletons under low-N conditions could
account for the limited CT-PG response.
The three backcross lines appeared to differ in when or how
they sensed changes in N supply or tissue N status. Line RM5
exhibited phenylpropanoid responses before the appearance of
low N effects on N metabolism, whereas Line 18 did not. The
response of Line 1012 to N stress is difficult to place in this
chronology because the timing of the changes in phenylpropanoid metabolism in relation to low-N effects on N metabolism
was missed. Whether differences lie in sensitivity of CT-PG
regulation to carbohydrate- and C:N ratio-dependent sensing-signaling networks (Paul and Driscoll 1997, Rolland et al.
2002, Sun et al. 2002 and references therein, Paul and Pellny
2003) awaits more detailed physiological and gene expression
analysis. A scenario that begins to emerge is that such signaling networks can trigger an active CT-PG response, but other
responses, for instance, the photosynthetic adjustment in Line
18, can also be triggered, possibly mitigating, or delaying,
CT-PG synthesis. In sum, N treatments revealed a complex basis for the CT-PG response in young source leaves. In sink
leaves of hybrid poplar, chemical partitioning into CT depends
on sink–strength and carbohydrate supply (Arnold and
Schultz 2002, Arnold et al. 2004). This is consistent with
CNB, to the extent that phenolics synthesis was enhanced in
accordance with increased carbohydrate supply (Arnold and
Schultz 2002, Arnold et al. 2004). The weak CT-PG response
in young source leaves of Line 18 was accompanied by reduced photosynthesis, i.e., decreased carbohydrate supply, and
thus also appears consistent with CNB. However, the CT response of RM5 preceded growth-limiting N effects, contrary
to the CNB prediction that carbon-based defenses like CT increase when N is limiting. Analysis of genotypic differences in
C:N sensing based on clonal resources and gene profiling may
offer a strategy for elucidating the plasticity of CT-PG responses, and the relevance of CNB, PCM and sink–strength
hypotheses to tree growth and defense.
The importance of understanding phenotypic plasticity in
traits of interest for Populus improvement has been noted
(Dickmann and Keathley 1996). Highly plastic traits like
CT-PG allocation exhibit strong genotype × environment effects that can be informative about mechanism. Prospects for
the simultaneous improvement of tree growth and defense will
likely rely on the deliberate manipulation of small clusters of
genes that control whole-plant regulation of carbon allocation
and partitioning. For example, recent discoveries about the
regulation of plant growth by metabolites such as trehalose, a
powerful mediator of carbohydrate sensing and signaling
(Paul et al. 2001), indicate the importance of small sets of unifying molecular principles for the control of certain complex
growth processes. The wide variation in CT-PG regulation
among natural Populus and Salix (willow) hybrids represents a
powerful resource, in combination with currently available
genomic tools, for elucidating the control mechanisms of such
traits.
Acknowledgments
We are grateful to Professor Thomas Whitham at the Northern Arizona University for providing the cottonwood hybrid lines. We also
thank Drs. Yu-Ying Kao and Maria Hernandez for EST and micro-
TREE PHYSIOLOGY VOLUME 25, 2005
FUNCTIONAL GENOMICS OF PHENOLICS IN POPULUS
array preparation, and Dr. Jeff Landgraf of the Genomics Technology
Support Facility at Michigan State University for his assistance on
microarray printing. This work was supported by the Michigan Life
Sciences Corridor Fund of the Michigan Economic Development
Corporation (Grant No. 085P1000477).
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