Journal of Plant Research

, Volume 126, Issue 2, pp 305–320 | Cite as

Global transcriptomic profiling of aspen trees under elevated [CO2] to identify potential molecular mechanisms responsible for enhanced radial growth

Regular Paper

Abstract

Aspen (Populus tremuloides) trees growing under elevated [CO2] at a free-air CO2 enrichment (FACE) site produced significantly more biomass than control trees. We investigated the molecular mechanisms underlying the observed increase in biomass by producing transcriptomic profiles of the vascular cambium zone (VCZ) and leaves, and then performed a comparative study to identify significantly changed genes and pathways after 12 years exposure to elevated [CO2]. In leaves, elevated [CO2] enhanced expression of genes related to Calvin cycle activity and linked pathways. In the VCZ, the pathways involved in cell growth, cell division, hormone metabolism, and secondary cell wall formation were altered while auxin conjugation, ABA synthesis, and cytokinin glucosylation and degradation were inhibited. Similarly, the genes involved in hemicellulose and pectin biosynthesis were enhanced, but some genes that catalyze important steps in lignin biosynthesis pathway were inhibited. Evidence from systemic analysis supported the functioning of multiple molecular mechanisms that underpin the enhanced radial growth in response to elevated [CO2].

Keywords

Carbon dioxide Aspen Transcriptome Vascular cambium zone Leaves 

Introduction

Atmospheric CO2 concentration has risen some 35–40 % since preindustrial times, and 23 % since 1959. Elevated [CO2] has been shown to have significant effects on tree growth because CO2 is one of the substrates required for photosynthesis. A twelve-year experiment conducted at the Aspen FACE (free-air CO2 enrichment) site near Rhinelander, Wisconsin, USA has demonstrated that the enhancement of CO2 levels can increase growth of aspen (Populus tremuloides) trees by up to 31 % in height or diameter, and 60 % in aboveground volume (Kubiske et al. 2007), depending on the genotype. Significant increase also occurs in fine root biomass (Pregitzer et al. 2008). The enhanced growth was accompanied by increases in light-saturated photosynthesis of 30–85 % for upper canopy leaves, with no acclimation in the response occurring from 2000 through 2008 (Darbah et al. 2010; Uddling et al. 2008). Elevated [CO2] can have a variety of physiological and ecological effects on plants, including improvements in nitrogen and water use efficiency (Leakey et al. 2009; Li et al. 2003), stimulation of dark respiration (Barbehenn et al. 2004; Leakey et al. 2009), alteration of plant nutrient content and plant–insect interactions (Hillstrom and Lindroth 2008), and altered decomposition of roots and aboveground litter (Angelis et al. 2004; Liu et al. 2005, 2009b). Growth under elevated [CO2] can reduce drought stress by increasing water use efficiency (Conley et al. 2001; Li et al. 2008); however, it is not clear if long-term CO2 fumigation can alter overall water consumption (Chen et al. 2006; Orcutt et al. 2000; Tuba and Lichtenthaler 2007).

To understand the molecular mechanisms underpinning significant enhancement tree growth under elevated [CO2], we have examined the mid-growing season transcriptome profiles of two tissues types, leaves and the vascular cambium zone (VCZ), at the same time using Affymetrix Poplar Whole Genome Arrays. These arrays contain 61413 features, including 61252 poplar design sequences collected from the Populus trichocarpa genome project (Tuskan et al. 2006), and other poplar sequences present in Genbank (Benson et al. 1993). These genome-wide microarray chips allowed us to generate informative results from which we could draw a more holistic picture of the transcriptional events and regulation occurred in aspen trees growing under elevated [CO2].

Several previous studies were conducted to identify the genes that were responsive to elevated [CO2] in Populus leaves (Druart et al. 2006; Gupta et al. 2005; Taylor et al. 2005). Gupta et al. (2005) used cDNA arrays containing only 4,600 ESTs and focused on the effect of O3 and the interaction between CO2 and O3. They were able to show 216 genes that were responsive to elevated [CO2]. Taylor et al. (2005) used POP1 and POP2 arrays containing 13,488 and 24,735 probes, respectively. The study by Druart et al. (2006) also used POP1 arrays, but interrogated genes expression from both leaves and the VCZ of stems of Populus deltoides. However, the VCZ tissues they used differed from those in this study in that they were harvested at regions fairly close (15–25 cm) to the apical meristems of coppice trees and were harvested in November when the trees might approach winter dormancy.

In the present study, we used genome-wide microarrays and focused primarily on leaves at about ¼ distance of canopy height from highest apical meristem, and lower stem VCZ tissues of 12-year-old aspen trees to identify significantly altered genes, and gene sets as well as possible molecular mechanisms underpinning the increased radial growth observed in response to elevated [CO2]. The research employed a number of bioinformatics analyses that included genome-wide homology gene mapping between poplar and Arabidopsis, multiple statistical analyses for identifying differentially expressed genes (DEGs), genome-wide protein domain and GO enrichment analysis, and large-scale pathway analysis. This allowed a more comprehensive biological examination and interpretation for revealing new information of the influence of elevated [CO2] on the transcriptional activity of many biological pathways and biological processes related to growth.

Results

Identification of DEGs in leaves and VCZ

Raw microarray data generated from 12 years old aspen trees grown under elevated [CO2] and control conditions with distinct discrepancies in growth (Fig. 1) were first normalized with robust multichip average (RMA) algorithm (Irizarry et al. 2003) and then were analyzed by rank product (RP) (Breitling et al. 2004) method to identify 1961 and 539 DEGs in VCZ and leaves. Some of these were summarized in Table 1. One of the significant discoveries from the microarray analysis was the up-regulation of genes whose functions are closely associated with cell loosening and expansion in VCZ tissues. For example, seven genes encoding glycosyl hydrolases were up-regulated in VCZ, with an increase between 60.4 and 121.1 % (Table 1). These enzymes function primarily to cleave the xyloglucan hemicellulose cross-linking chains that exist between cellulose microfibrils, leading to cell wall loosening (Cosgrove 2000). In addition to genes encoding glycosyl hydrolases, several other genes involved in modifying cell wall extensibility also increased in VCZ in response to elevated [CO2]. These included five expansins, all of which had at least 5 and up to more than 100 times higher expression in VCZ than leaves and three pectin esterases (Derbyshire et al. 2007) that are known to modify of cell wall rigidity during cell expansion and division (Derbyshire et al. 2007; Kutschera 1990), increased between 89.9 and 306.6 %.
Fig. 1

Average sizes of harvested aspen trees grown in elevated [CO2] and ambient conditions

Table 1

DEGs in response to elevated [CO2]

Probe

Genbank ID

Description

FC

EXPV/L

Poplar V2.0 ID

PtpAffx.132592.1.S1_at

XM_002313646a

EXLA2 (expansin-like A2)

2.26

>5

Ptp.6656.1.S1_s_at

XM_002312217a

EXPA1 (expansin A1)

1.97

>10

Ptp.584.1.A1_at

XM_002320365a

EXGT-A4 (endoxyloglucan transferase a4);

2.68

>10

POPTR_0006s19310

Ptp.5980.3.A1_a_at

XM_002309337a

Expansin-related protein 3 precursor

1.45

>100

POPTR_0004s18840

Ptp.617.1.S1_at

XM_002305509a

EXLA1 (expansin-like A1)

2.03

>100

POPTR_0008s13200

PtpAffx.144673.1.S1_s_at

XM_002326285a

Pectinesterase

1.92

>5

POPTR_0006s13670

PtpAffx.132383.1.A1_s_at

XM_002326285a

Pectinesterase

1.89

>10

POPTR_0006s13670

Ptp.144.1.S1_at

ATPME3; pectinesterase

4.06

>25

PtpAffx.18355.1.S1_at

XM_002308836

CYC1BAT; cyclin-dep. protein kinase regulator

1.24

>5

PtpAffx.214175.1.S1_x_at

XM_002324798

CYC3B cyclin-dep. protein kinase regulator

1.30

>5

PtpAffx.200516.1.S1_at

XM_002298096

CYCA2;3 cyclin-dep. protein kinase regulator

1.38

>25

POPTR_0001s17730

PtpAffx.200879.1.S1_at

XM_002298415

CYCB1;4 cyclin-dep. protein kinase regulator

1.29

>10

POPTR_0001s27890

PtpAffx.204823.1.S1_at

XM_002313980

CYCB1;4 cyclin-dep. protein kinase regulator

1.25

>10

POPTR_0009s07100

Ptp.5638.1.S1_at

XM_002307755

CYCB2;3 cyclin-dep. protein kinase regulator

1.35

>10

Ptp.6857.2.S1_s_at

XM_002312770

CYCB2;4 cyclin-dep. protein kinase regulator

1.22

>5

POPTR_0009s16730

PtpAffx.147539.1.A1_at

CYCB2;4 cyclin-dep. protein kinase regulator

1.07

>2

PtpAffx.164104.1.S1_s_at

XM_002316115

CYCB3;1 cyclin-dep. protein kinase

1.27

>2

PtpAffx.56737.2.A1_a_at

XM_002319084

CDC20.2; signal transducer

1.41

>25

POPTR_0019s03850–

Ptp.2922.1.S1_at

XM_002313014

cyclin cyclase associated (CAP)

1.16

>5

POPTR_0003s05690

PtpAffx.202922.1.S1_at

XM_002304135

CYCA2;3 (cyclin A2;3);

1.64

>10

POPTR_0014s04930

PtpAffx.144614.1.S1_at

XM_002326989a

CYCP4;1 (cyclin p4;1);

1.81

>100

 

PtpAffx.46431.1.S1_at

XM_002311482a

GH9B1 glycosyl hydrolase 9B1 (cellulose)

2.08

>5

POPTR_0008s13200

PtpAffx.6557.1.A1_at

XM_002311482a

GH9B1 glycosyl hydrolase 9B1 (cellulose)

1.84

>20

Ptp.2717.2.S1_s_at

XM_002311482a

GH9B6 glycosyl hydrolase 9B6

1.87

>25

Ptp.6339.1.S1_s_at

XM_002328213

Glycosyl hydrolase family 17 protein

1.96

>2

 

PtpAffx.2710.3.S1_at

XM_002300469

Glycosyl hydrolase family 17 protein

1.60

>10

POPTR_0001s45320

PtpAffx.204306.1.S1_at

XM_002306147

Glycosyl hydrolase family 35 protein

1.90

>5

POPTR_0004s18070

PtpAffx.203068.1.S1_at

XM_002303369

Glycosyl hydrolase family 38 protein

2.21

>2

POPTR_0007s09730

Ptp.6069.1.S1_a_at

XM_002320147a

GH3 family protein

−2.61

<−5

Ptp.6069.2.S1_a_at

XM_002320147a

GH3 family protein

−2.26

<−2

PtpAffx.72392.1.A1_at

XM_002300212a

GH3.3; indole-3-acetic acid amido synthetase

−2.35

>5

PtpAffx.132894.1.S1_a_at

XM_002301363a

GH3 family protein

−1.84

<−2

POPTR_0002s16960

Ptp.4793.1.S1_at

XM_002302195a

ILL6; IAA-amino acid conjugate hydrolase

−2.34

>1

PtpAffx.100235.1.S1_s_at

XM_002302217a

LAX3 (LIKE AUX1 3); auxin influx transporter

2.78

>25

POPTR_0002s08750

PtpAffx.25167.2.A1_at

a

LAX3 (LIKE AUX1 3); auxin influx transporter

2.00

>4

PtpAffx.17419.1.A1_at

XM_002312937a

Auxin influx carrier component

1.43

>25

PtpAffx.204148.1.S1_at

XM_002305335a

PIN8 auxin:hydrogen symporter

−2.22

>2

PtpAffx.97482.1.S1_at

XM_002320273a

BXL1 (BETA-XYLOSIDASE 1); hydrolase,

2.10

>20

POPTR_0014s11730

PtpAffx.202369.1.S1_at

XM_002302723a

BXL2 (BETA-XYLOSIDASE 2); hydrolase,

1.92

>20

POPTR_0002s19830

PtpAffx.14026.1.S1_s_at

XM_002316779a

Cellulose synthase

1.71

>100

PtpAffx.5943.1.A1_at

XM_002308376a

CesA7 (IRX3); cellulose synthase

2.10

>5

Ptp.666.1.S1_at

XM_002301820

CesA4 cellulose synthase A4

1.08

>25

Ptp.296.1.S1_at

XM_002305024

CesA8 cellulose synthase A8

1.23

>25

Ptp.3250.1.S1_s_at

XM_002322676

CesA3 cellulose synthase A3

1.07

>10

POPTR_0014s12000

PtpAffx.157368.1.S1_s_at

XM_002303309a

UTP–glucose-1-phosphate uridylyltransferase

2.15

>5

PtpAffx.70110.1.A1_at

XM_002301244a

GAE3 UDP-glucuronate 4-epimerase

1.84

>5

POPTR_0002s14750

PtpAffx.103875.1.S1_at

XM_002318144a

GUT1; glucuronoxylan

1.97

>2

Ptp.126.1.S1_s_at

XM_002306028

UDP-glucose 4-epimerase

1.43

>5

Ptp.3953.1.S1_s_at

XM_002304442

UDP-glucose 4-epimerase

1.79

>2

POPTR_0003s12380

PtpAffx.147946.1.S1_at

a

RHM3 (UDP-L-rhamnose synthase)

1.69

>2

PtpAffx.59601.1.S1_a_at

XM_002317651a

MUR4 (UDP-arabinose 4-epimerase)

2.17

>1

PtpAffx.137959.1.S1_at

XM_002308172a

IRX12 (IRREGULAR XYLEM 12); laccase

2.04

>5

Ptp.6345.1.S1_s_at

XM_002308172a

IRX12 (IRREGULAR XYLEM 12); laccase

1.42

>25

POPTR_0016s11950

Ptp.1855.1.S1_at

XM_002308172a

Laccase 1C

1.65

>25

PtpAffx.5154.1.A1_s_at

XM_002325536a

LAC1 (Laccase 1); laccase

−3.50

>10

POPTR_0019s11820

PtpAffx.162923.1.S1_s_at

XM_002312150a

LAC12 (laccase 12); laccase

2.14

>100

POPTR_0008s07370

PtpAffx.44574.1.A1_at

XM_002315095a

LAC12 (laccase 12); laccase

2.10

>25

Ptp.6122.1.A1_at

XM_002317469a

LAC17 (laccase 17); laccase

1.44

>25

PtpAffx.224175.1.S1_s_at

XM_002299908a

MYB46 (MYB DOMAIN PROTEIN 46);

1.77

>5

POPTR_0001s27430

PtpAffx.207054.1.S1_at

XM_002309877a

MYB69 (MYB DOMAIN PROTEIN 69);

1.71

>5

POPTR_0007s04140

Ptp.6393.1.S1_at

XM_002324067a

UDP-glucose 6-dehydrogenase,

1.95

>2

None

PtpAffx.29653.1.S1_s_at

XM_002306028a

UDP-glucose 6-dehydrogenase,

1.82

>2

POPTR_0017s12760

Ptp.2708.1.S1_at

XM_002311295a

UDP-glucose 6-dehydrogenase,

1.82

>25

None

PtpAffx.55460.2.S1_a_at

XM_002302640a

OMT1 caffeate O-methyltransferase

−1.81

>10

POPTR_0002s18150

PtpAffx.34384.1.S1_s_at

XM_002327918a

4-coumarate-coa ligase (4LC)

−3.86

>1

POPTR_0017s06210

PtpAffx.12056.3.S1_a_at

XM_002325779

4-coumarate-coa ligase (4LC)

−1.42

<-5

POPTR_0019s07600

Ptp.3043.1.S1_s_at

XM_002297663

4-coumarate-coa ligase (4LC)

−1.02

>5

POPTR_0001s07400

PtpAffx.141260.2.A1_at

XM_002317802

Caffeic acid 3-O-methyltransferase

1.79

>25

Ptp.4675.1.S1_s_at

XM_002308824

CYP98.1

1.45

>2

POPTR_0006s03180

PtpAffx.220781.1.S1_at

XM_002332733

CPY71B35

1.01

<−1

POPTR_0007s06350

Ptp.7180.2.S1_s_at

XM_002309241

Rhomboid family protein

1.51

>2

POPTR_0006s21220

PtpAffx.200787.1.S1_at

XM_002298315a

Rhomboid family protein

1.90

>5

POPTR_0001s24800

PtpAffx.204967.1.S1_at

XM_002313397

Rhomboid family protein

1.56

>2

POPTR_0009s03720

PtpAffx.32440.1.S1_at

XM_002298020a

Rhomboid family protein

1.72

>10

POPTR_0001s08920

PtpAffx.130641.1.S1_s_at

XM_002314170a

Kinesin motor family protein

1.52

>10

POPTR_0009s03110

PtpAffx.200613.1.S1_at

XM_002298181

Kinesin motor family protein

1.53

>5

POPTR_0001s18260

PtpAffx.147624.1.A1_at

XM_002310259

Kinesin motor family protein

1.59

>25

PtpAffx.57355.1.S1_at

XM_002299861

Kinesin motor protein-related

1.52

>2

PtpAffx.144831.1.S1_s_at

XM_002299432a

ADL1 (dynamin-like protein);

1.70

>2

PtpAffx.210802.1.S1_at

XM_002318256

DRP3A (dynamin-related protein 3A)

1.22

>10

POPTR_0007s02960

PtpAffx.117056.1.S1_s_at

XM_002314165a

TIP1;3 water channel

3.65

>10

PtpAffx.3989.1.S1_at

XM_002311110a

TIP1;3 water channel

2.43

>1

PtpAffx.52659.1.A1_at

XM_002299857a

TIP1;3 water channel

1.66

>5

PtpAffx.2848.1.S1_at

XM_002309090a

PIP subfamily

−1.98

>10

PtpAffx.202247.1.S1_at

XM_002301360

CRF2 (cytokinin response factor 2)

1.12

<−1

None

PtpAffx.5552.2.S1_s_at

XM_002304642a

Histidine kinase cytokinin receptor

−2.35

<−5

None

Ptp.811.1.A1_at

XM_002304642a

Histidine kinase cytokinin receptor

−2.19

>1

 

PtpAffx.201634.1.S1_at

XM_002300706

CKX5 (cytokinin oxidase 5)

−1.28

<−10

POPTR_0002s03190

PtpAffx.218925.1.S1_at

XM_002332387

CKX6 (cytokinin oxidase/dehydrogenase 6);

1.19

>6

POPTR_0001s05830

Ptp.938.1.A1_s_at

XM_002309432

CKX7 (cytokinin oxidase 7);

−1.13

>2

None

PtpAffx.31331.2.A1_a_at

XM_002304758

Type-a response regulator (ARR9)

1.28

>2

None

PtpAffx.44007.2.A1_at

XM_002312733

Type-a response regulator

1.37

>100

None

PtpAffx.44007.3.S1_a_at

XM_002312663

Type-a response regulator

1.42

>1

None

PtpAffx.201936.1.S1_at

XM_002301034a

PUP3 cytokinin transporter

−2.21

>1

POPTR_0002s09980

PtpAffx.205454.1.S1_s_at

XM_002307315

PUP2 cytokinin transporter

−1.18

>1

None

PtpAffx.39343.1.S1_at

XM_002302194

Type-a response regulator

1.26

>5

None

PtpAffx.158523.1.S1_s_at

XM_002310744a

Glycosyltransferase, CAZy family GT8

1.58

>10

None

PtpAffx.222470.1.S1_at

XM_002328075a

ABA2 xanthoxin dehydrogenase

−1.94

<−10

POPTR_0004s21040

PtpAffx.6684.2.S1_s_at

XM_002328075a

ABA2 xanthoxin dehydrogenase

−1.74

<−20

 

PtpAffx.221010.1.S1_s_at

XM_002307113a

ABA-responsive protein-related

−1.93

>10

POPTR_0005s09060

PtpAffx.205234.1.S1_s_at

XM_002333696a

ABA-responsive protein-related

−1.82

>20

POPTR_0005s09050

PtpAffx.202059.1.S1_at

XM_002301149a

CYP707A4; (+)-abscisic acid 8′-hydroxylase

1.75

>20

Ptp.2599.1.S1_s_at

XM_002300667a

Tubulin, beta chain

2.59

>5

 

Ptp.5472.1.S1_at

XM_002313977a

Tubulin, beta chain

1.97

>25

 

PtpAffx.135374.1.S1_at

XM_002298784

ATMAP65-1 microtubule binding

1.43

>25

POPTR_0001s36650

Ptp.7295.1.S1_at

XM_002307873

ATMAP70-5 microtubule binding

1.09

>5

POPTR_0006s01900

PtpAffx.1704.5.A1_at

TUB7, beta-tubulin 7

1.30

>25

POPTR_0001s27960

Ptp.5933.1.S1_x_at

XM_002298418

TUB3, beta-tubulin 3

1.40

>5

POPTR_0001s27960

Ptp.5651.1.S1_s_at

XM_002322573a

TUB3, beta-tubulin 6

1.80

>2

Ptp.770.1.A1_s_at

XM_002316365a

RABC2A, GTP binding

1.82

>10

PtpAffx.146219.1.S1_at

XM_002320185a

GTPase activating protein

2.00

<−1

POPTR_0014s09940

PtpAffx.157389.1.S1_at

XM_002301394a

Rac GTPase activating protein

1.96

>10

PtpAffx.211787.1.S1_s_at

XM_002321005a

Rac GTPase activating protein,

1.87

>2

POPTR_0014s13090

Ptp.7759.1.S1_at

XM_002322494a

Rho GDP-dissociation inhibitor

1.66

>2

Ptp.1990.1.S1_a_at

XM_002325552a

ROP2, rho-related protein

1.90

>10

POPTR_0019s12210

PtpAffx.10764.1.S1_at

XM_002309059a

ROPGEF7; Rho guanyl-nucleotide exchange

2.35

>20

POPTR_0006s09370

PtpAffx.213572.1.S1_at

XM_002323495a

ROPGEF7; Rho guanyl-nucleotide exchange

1.71

>20

POPTR_0016s10130

Ptp.5677.1.S1_at

XM_002318658a

Pectate lyase family protein

1.63

>10

Ptp.4652.1.S1_s_at

XM_002300033a

Pectate lyase family protein

1.12

>5

POPTR_0001s35960

PtpAffx.210733.1.S1_at

XM_002318179a

Pectinacetylesterase, putative

2.00

>10

POPTR_0012s13090

PtpAffx.137964.1.S1_s_at

XM_002318883a

Pectinacetylesterase, putative

1.75

>100

POPTR_0013s00280

PtpAffx.42470.1.S1_s_at

XM_002329532a

Clathrin adaptor, medium subunit

1.78

>1

PtpAffx.137956.1.S1_at

XM_002309487a

Clathrin assembly protein-related

2.01

>1

Ptp.1826.1.S1_at

XM_002308250

Clathrin adaptor, small chain

1.54

<-1

POPTR_0006s15350

PtpAffx.6486.2.S1_a_at

XM_002318496

Clathrin adaptor, small chain

1.39

>1

POPTR_0014s07500

PtpAffx.78955.1.S1_at

XM_002302304

Clathrin adaptor, medium subunit

1.37

>1

POPTR_0002s10530

PtpAffx.210842.1.S1_at

XM_002318903

Clathrin adaptor, medium subunit

1.39

>2

POPTR_0013s00780

PtpAffx.1066.10.S1_a_at

XM_002318255

Clathrin adaptor, medium subunit

1.57

>1

POPTR_0012s14780

PtpAffx.99026.1.S1_at

XM_002298450

Clathrin heavy chain

1.30

>25

POPTR_0001s28540

PtpAffx.162111.1.S1_at

XM_002298450

Clathrin heavy chain

1.40

>2

POPTR_0009s07740

FC is the fold change of expression in CO2 fumigated trees verse controls. EXPV/L is the range of expression ratio of VCZ to leaf. In this study, we defined a gene to be VCZ-specific if EXPV/L >5 and leaf-specific if EXPV/L <−5. The range is defined as follows: >1: FC = 1–2, >2: FC = 2–5, >5: FC = 5–10, >10: FC = 10–20, >25: FC = 25–100, >100: FC > 100

aThe identified DEGs in VCZ. Other genes are listed for comparison

Examination of gene lists for genes involved in the cell cycle revealed 13 genes that were up-regulated in response to elevated [CO2]. For example, genes XM_002304135 (cyclin A2;3), XM_002326989 (cyclin P4;1), XM_002298096 (cyclase associated protein (CAP)), XM_002313980 (CYC3B cyclin-dependent protein kinase), and XM_002307755 (CYCB2;3 cyclin-dependent protein kinase regulator), increased 64.5, 80.6, 37.9, 25.2 and 35.2 %, respectively, in the VCZ (Table 1), potentially promoting cell division through both G1 → S and G2 → M transitions, as similar genes have been known to do so (Cooper and Strich 2002; Ji et al. 2005; Joubes et al. 2001; Yu et al. 2003). Recently, the increased expression of the auxin carrier LAX3 was reported to induce of cell wall remodeling enzymes (Swarup et al. 2008). Our data revealed that two LAX3 genes increased 178.0 and 100.5 % in VCZ in response to elevated [CO2], and one auxin influx carrier gene, XM_002312937, increased 43 % while several genes that promote the formation of IAA-amino acid conjugate decreased in response to elevated [CO2]. In addition, several genes involved in ABA biosynthesis and response were down-regulated while the gene, XM_002301149, CYP707A4, involved in ABA catabolism (Okamoto et al. 2006, 2011), increased 74.6 % in VCZ. Taken together, evidence tends to support that elevated [CO2] can directly or indirectly drive cell division and expansion in VCZ.

Theoretically, cell division and expansion require the formation of new cell walls. Therefore, the genes encoding cellulose synthases, beta-xylosidase 1 and 2 (secondary cell wall hemicellulose metabolism; Goujon et al. 2003), UDP-l-rhamnose synthase, UDP-glucuronate 4-epimerase, UDP-arabinose 4-epimerase, UDP-glucose dehydrogenases (UGD), laccases and two transcription factors, XM_002299908 (MYB46) and XM_002309877 (MYB69) that are know to regulate cell wall biosynthesis (Zhong et al. 2008), were significantly upregulated in VCZ. In the VCZ, the up-regulation of six clathrin adaptor genes and one clathrin assembly gene suggests that intracellular protein transport was enhanced during CO2 fumigation.

Interestingly, the gene CesA7 encoding cellulose synthase was up-regulated 110.0 % in response to elevated [CO2] (Table 1). Two other genes, CesA 4 and CesA8, which are generally required for secondary cell wall biosynthesis were up-regulated 7.5 and 23.4 % (Table 1) respectively in VCZ. Two genes encoding UDP-glucose-4-epimerase, and one encoding UDP-glucuronate 4-epimerase, were up-regulated 42.8–84.0 %. UDP-glucose-4-epimerase is responsible for the interconversion of UDP-d-glucose and UDP-d-galactose. UDP-d-glucose is the precursor for sucrose, α-d-glucose 1-phosphate, UDP-l-rhamnose, and UDP-d-glucuronate. UDP-d-glucuronate, in turn, is the precursor for UDP-d-xylose and UDP-d-galacturonate. UDP-glucuronate 4-epimerase is an enzyme that catalyzes the formation of UDP-d-galacturonate from UDP-glucuronate.

As structural components of cells, microtubules are involved in many cellular processes including mitosis (Gregory et al. 2008), cytokinesis (Canman et al. 2000; Larkin and Danilchik 1999; Surka et al. 2002), and vesicular transport (Hamm-Alvarez and Sheetz 1998). We observed an increased VCZ-specific expression of seven microtubule genes (Table 1) in the VCZ of CO2 treated trees, which suggests the deployment of microtubules in cell division. In addition, expression levels of five genes encoding the kinesin motor were 28–52 % higher in VCZ of CO2 treated trees than in control trees, suggesting that microtubule-based vesicle transport was possibly driven by kinesin, which is known to move along microtubule cables and is powered by the hydrolysis of ATP (Muto et al. 2005; Schnitzer and Block 1997). Expression of two genes, XM_002299432 and XM_002318256, which encode dynamin-like protein, increased 70.8 and 22.8 %, respectively, in VCZ in response to elevated [CO2]. These pieces of evidence suggest an augmented cell growth and division in the VCZ of CO2 treated trees as compared to control trees.

The up- and down-regulation of genes encoding cytokinin oxidase suggest the metabolism of cytokinins was impacted due to the elevated [CO2]. What also changed is the activity of cytokinin transporters. For instance, XM_002301034 and XM_002307315 encoding homologs of PUP3, and PUP2, respectively, were down-regulated 121.1 and 18.8 %, respectively in VCZ. Four genes, XM_002312663, XM_002302194, XM_002304758 and XM_002312733, which encode a cytokinin-inducible type-a response negative regulator (To et al. 2007), were up-regulated 41.7, 26.4, 28.2 and 37.4 %, respectively. These type-a response regulators suppress the cytokinin signaling (Hirose et al. 2007) and can thus be a part of mechanism that wears down the increased cytokinin signaling.

Three genes, XM_002314165, XM_002311110, and XM_002299857, of the aquaporin TIP family protein, were up-regulated significantly in VCZ in response to elevated [CO2]. TIP family proteins function on the vacuolar membrane (Barkla et al. 1999; Maeshima 2001). We theorize that this increase facilitated expansion of the newly formed primary xylem cells. However, the genes encoding plasma membrane intrinsic protein (PIP) in VCZ were down-regulated. The changes of these genes suggest an alternation of inter- and intracellular communication through membrane transport properties of aquaporins.

Overall pathway expression changes

The existence of multiple enzymes that catalyze the same step of a biochemical reaction in a given pathway makes the evaluation of the on/off status by examining only the statistically DEGs quite inadequate. In the present study, we also calculated an overall pathway change in expression of each pathway and used this to identify the pathways in which genes were collectively up/down-regulated (Fig. 2).
Fig. 2

Boxplot of the overall percentage change of each pathway in response to elevated [CO2]. The median and the average percentage change of each pathway are represented by the black line and diamond inside the bar, respectively. The white and gray bars represent leaves and the VCZ, respectively. Pathway names corresponding to the numbers shown in the x-axis labels listed above, and the numbers following L or VCZ shown in the parentheses at the end of each pathway name are the pathway gene numbers used to calculate the overall pathway percentage change in leaves and VCZ. The genes with an expression level (<25 = 32) in both control and treatment were considered unexpressed and were excluded from pathway analysis given the maximal expression level is more than 9 magnitudes more (214.21 = 18,951). 1, Abscisic acid biosynthesis (L2, VCZ5); 2, acetyl-CoA biosynthesis (from citrate) (L3, VCZ6); 3, Calvin-Benson-Bassham cycle (L3, VCZ14); 4, cellulose biosynthesis (L9, VCZ13); 5, chorismate biosynthesis I (L2, VCZ3); 6, cytokinins 7-N-glucoside biosynthesis (L12, VCZ9); 7, cytokinins 9-N-glucoside biosynthesis (L13, VCZ9); 8, cytokinins-O-glucoside biosynthesis (L12, VCZ18); 9, GDP-glucose biosynthesis (L2, VCZ6); 10, GDP-mannose biosynthesis (L2, VCZ6); 11, gluconeogenesis I (L7, VCZ21); 12, glycolysis I (L2, VCZ17); 13, glycolysis II (L8, VCZ3); 14, glycolysis IV (plant cytosol) (L7, VCZ15); 15, glycolysis V (L7, VCZ16); 16, homogalacturonan biosynthesis (L3, VCZ3); 17, homogalacturonan degradation (L13, VCZ12); 18, pentose phosphate pathway (non-oxidative branch) (L1, VCZ4); 19, pentose phosphate pathway (oxidative branch) (L4, VCZ9); 20, phenylpropanoid biosynthesis (L5, VCZ9); 21, phenylpropanoid biosynthesis, initial reactions (L3, VCZ4); 22, photorespiration (L4, VCZ10); 23, starch biosynthesis (L2, VCZ2); 24, starch degradation (L8, VCZ7); 25, sucrose biosynthesis (L4, VCZ7); 26, sucrose degradation III (L3, VCZ13); 27 TCA cycle variation III (plant) (L9, VCZ7); 28, UDP-D-galacturonate biosynthesis I (from UDP-D-glucuronate) (3) (L1, VCZ3); 29 UDP-D-xylose biosynthesis (25) (L6, VCZ11); 30, UDP-galactose biosynthesis (salvage pathway from galactose using UDP-glucose) (L3, VCZ7); 31, UDP-glucose biosynthesis (from glucose 6-phosphate) (L6, VCZ14); 32, UDP-L-arabinose biosynthesis I (from UDP-xylose) (L1, VCZ3); 33, UDP-L-rhamnose biosynthesis (L1, VCZ1); 34, xylan biosynthesis (L3, VCZ4)

The overall average expression of Calvin cycle pathway genes in leaves increased 7.8 % in response to elevated [CO2] but almost has no increase (−2 %) occurred for these genes in the VCZ. Several genes in this pathway, including fructose-1,6-bisphosphatase (FBPase) transketolase, and aldolase that have been linked to growth in tobacco (Lefebvre et al. 2005; Tamoi et al. 2006; Uematsu et al. 2012), increased. The average expression of several down-stream pathways that assimilate the products of Calvin cycle were all up-regulated. Interested users can find more detailed information for these leaf responses in Supplemental File 1.

Acetyl-CoA is a central metabolite in many aspects of plant metabolism. Its biosynthesis pathway is a linked pathway of Glycolysis I, II, and IV, and the super pathway of TCA cycle, cytokinin, and gibberellins A12 biosynthesis. The overall average gene expression of acetyl-CoA biosynthesis from citrate was enhanced 75.1 % in VCZ under elevated [CO2] (Fig. 2). This enhancement of acetyl-CoA biosynthesis pathway genes possibly arose to fulfill the need for generation of energy and gibberellins to promote stem elongation as cytokinins are assumed to be synthesized mainly in root cap columella (Aloni et al. 2005; Ghanem et al. 2011). The overall average expression of TCA cycle variation V (plant) increased 40.2 % in the VCZ, as compared to 10.3 % in leaves. Concomitant with this enhancement of acetyl-CoA biosynthesis, an average decrease of 39 % of all genes involved in three cytokinin glucoside biosynthesis pathways was noted under elevated [CO2]. These pathways convert active cytokinins into inactive forms, suggesting a relative increase in the active forms of cytokinins in the VCZ. The abscisic acid (ABA) biosynthesis pathway decreased 13.8 % in VCZ and 22.5 % in leaves in response to elevated [CO2] (Fig. 2), suggesting that various hormones were coordinated to enhance cambial growth.

The overall average expression of cellulose biosynthesis pathway increased 33.7 % in VCZ and 0.2 % in leaves, which reflects more intensive cell wall biosynthesis in VCZ. In addition, expression of genes for UDP-d-galacturonate biosynthesis I (from UDP-d-glucuronate), UDP-d-xylose biosynthesis, UDP-galactose biosynthesis (salvage pathway from galactose using UDP-glucose), UDP-glucose biosynthesis (from glucose 6-phosphate), UDP-l-arabinose biosynthesis I (from UDP-xylose), UDP-l-rhamnose biosynthesis, and xylan biosynthesis increased 50.3, 47.8, 54.8, 35.8, 79.7, 36.5, and 46.8 %, respectively in the VCZ, but changed by 31, 14.7, 11.7, 23.7, −5.7, −3.8, and −33.9 %, respectively, in leaves (Fig. 2). This suggests a significant boost in hemicellulose and pectin biosynthesis in the VCZ. As to lignin biosynthesis, the expression of three genes involved in the phenylpropanoid biosynthesis initial reactions increased by 7 % in VCZ and 21.4 % in the leaves. However, the overall average expression of phenylpropanoid biosynthesis decreased 24 % in VCZ and 11.4 % in leaves (Fig. 2). The lignin pathway was mainly regulated at 4-coumarate-coa ligase (4LC) 4CL, and caffeate O-methyltransferase (OMT1). This is evidenced by the fact that three genes, XM_002327918, XM_002325779, and XM_002297663 encoding 4CL genes were down-regulated 286, 42 and 2 %, respectively in VCZ as the other gene, XM_002302640 encoding OMT1 decreased 81 % in VCZ.

Protein domain enrichment (PDE) analysis

Protein domain enrichment analysis can reveal which gene families are impacted by elevated [CO2]. We performed PDE analysis of the DEGs in VCZ and displayed the expression patterns of four types of functional groups in Table 2, each representing a biological theme. To make the enriched domains between leaves and VCZ comparable, the enriched protein domains of leaves shown in Table 2 were derived from the top 1,961 genes of DEG list of leaves (rather than from 542 DEGs of leaves only). Group I contains those domains whose carriers generally had an increased average expression in VCZ and a decreased average expression in leaves. In Group II, three genes encoding auxin efflux carrier domain were down-regulated 77 % in VCZ, as compared to 17 % increase in leaves. There are seven down-regulated genes containing Aux/IAA domain in VCZ, which acts as repressors of auxin-induced gene expression, possibly through modulating the activity of DNA-binding auxin response factors (Tiwari et al. 2004; Tiwari et al. 2001). Coordinately, five genes encoding GH3 auxin-responsive promoter and functioning in conjugating auxin, decreased 128 % on average in VCZ, suggesting elevated [CO2] can potentially increase auxin activity in VCZ. In Group III, six genes encoding water channel protein increased more than twofold in VCZ while some genes encoding water channel decreased in leaves nearly twofold. In Group IV, genes with domains that are known to be involved in cellulose biosynthesis, hemicellulose, and pectin biosynthesis increased more in VCZ than in leaves except that two domains, sucrose-6F-phosphate phosphohydrolase, and sucrose phosphate synthase, involved in sucrose biosynthesis decreased in VCZ but not in leaves.
Table 2

The protein domains enriched in the gene list of differentially expressed genes

Tissue

InterPro

Description

Gene num

EF

P-value

FC_AVG

Group I

 

 VCZ

IPR004367

Cyclin, C-terminal

3

1.40

0.1672048

1.18

 Leaves

IPR004367

Cyclin, C-terminal

1

−2.12

0.6324380

−1.03

 VCZ

IPR015451

Cyclin D

2

1.15

0.2140380

1.48

 Leaves

IPR015451

Cyclin D

1

1.09

0.2305270

−1.03

 VCZ

IPR013922

Cyclin-related 2

2

5.78

0.0041401

1.76

 Leaves

IPR013922

Cyclin-related 2

1

2.75

0.0490771

1.68

 VCZ

IPR016098

Cyclase-associated protein CAP

1

9.62

0.0035113

1.79

 VCZ

IPR002963

Expansin

2

1.65

0.1199281

1.48

 Leaves

IPR002963

Expansin

4

3.92

0.0015096

−1.51

 VCZ

IPR007118

Expansin/Lol pI

5

3.92

0.0034707

1.81

 Leaves

IPR007118

Expansin/Lol pI

5

4.47

0.0001407

−1.42

 VCZ

IPR007112

Expansin 45, endoglucanase-like

6

3.15

0.0027936

1.75

 Leaves

IPR007112

Expansin 45, endoglucanase-like

6

3.99

0.0001536

−1.34

Group II

 VCZ

IPR004776

Auxin efflux carrier

3

3.01

0.0171090

−1.77

 Leaves

IPR004776

Auxin efflux carrier

3

2.36

0.0371110

1.17

 VCZ

IPR003311

AUX/IAA protein

7

3.0

0.0024230

−2.70

 Leaves

IPR003311

AUX/IAA protein

3

1.71

0.0971630

−1.66

 VCZ

IPR003311

Aux/IAA-ARF-dimerisation

6

3.39

0.0017959

−2.02

 Leaves

IPR011525

Aux/IAA-ARF-dimerisation

3

0.94

0.3908440

−1.66

 VCZ

IPR004993

GH3 auxin-responsive promoter

5

7.22

4.35E−05

−2.28

 Leaves

IPR004993

GH3 auxin-responsive promoter

1

1.37

0.1637392

1.11

 Leaves

IPR015345

Cytokinin dehydrogenase 1

2

4.58

0.0082474

−1.55

Group III

 

 VCZ

IPR012269

Aquaporin

6

2.75

0.0060042

2.05

 Leaves

IPR012269

Aquaporin

4

1.74

0.0791068

−1.70

 VCZ

IPR000425

Major intrinsic protein (water channel)

6

1.86

0.0426369

2.05

 Leaves

IPR000425

Major intrinsic protein (water channel)

10

2.95

0.0005671

−2.10

Group IV

 

 VCZ

IPR005150

Cellulose synthase

2

0.93

0.3636904

1.93

 Leaves

IPR005150

Cellulose synthase

6

2.82

0.0071798

1.27

 VCZ

IPR016461

O-methyltransferase, COMT

2

1.75

0.1050379

−2.68

 Leaves

IPR016461

O-methyltransferase, COMT

2

1.66

0.1174360

−1.35

 VCZ

IPR006151

Quinate/shikimates5-dehydrogenas

2

1.31

0.1759511

−2.46

 VCZ

IPR010713

Xyloglucan endo-transglycosylase, C-terminal

5

2.83

0.0081207

1.45

 Leaf

IPR010713

Xyloglucan endo-transglycosylase, C-terminal

8

4.85

1.30E−05

−2.40

 VCZ

IPR014027

UDP-glucose/GDP-mannose dehydrogenase

5

10.5

1.92E−05

1.95

 VCZ

IPR002213

UDP-glucuronosyl/UDP-glucosyltransferase

2

3.39

0.0195882

2.28

 Leaves

IPR002213

UDP-glucuronosyl/UDP-glucosyltransferase

1

1.61

0.1256750

1.54

 VCZ

IPR005886

UDP-glucoses4-epimerase

1

1.81

0.1043882

2.17

 VCZ

IPR017761

Laccase

9

5.77

2.53E−06

1.22

 Leaves

IPR017761

Laccase

2

1.22

0.224673

1.11

 VCZ

IPR004963

Pectinacetyleasterase

2

2.22

0.0596068

1.88

 Leaves

IPR004963

Pectinacetyleasterase

2

2.11

0.0672154

−2.19

 VCZ

IPR006380

Sucrose-6F-phosphate phosphohydrolase

2

3.61

0.0165502

−2.63

 Leaves

IPR006380

Sucrose-6F-phosphate phosphohydrolase

1

1.72

0.1135182

1.65

 VCZ

IPR012819

Sucrose phosphate synthase, plant

2

7.22

0.0020352

−2.63

 Leaves

IPR012819

Sucrose phosphate synthase, plant

1

3.43

0.0320401

1.65

 VCZ

IPR005829

Sugars transporter, conserved site

4

0.92

0.432882

3.18

 Leaves

IPR005829

Sugars transporter, conserved site

6

1.09

0.310526

−1.06

 VCZ

IPR017853

Glycoside hydrolase, catalytic score

20

1.89

0.002459

2.99

 Leaves

IPR017853

Glycoside hydrolase, catalytic score

40

2.97

3.19E−10

1.14

EF enrichment factor in the background of all genomic genes, FC_AVG averaged fold change

Gene ontology (GO) term enrichment analysis

We performed GO term enrichment analysis on all DEGs from VCZ and found the cell wall organization and biogenesis associated biological processes were significantly enriched. Also enriched in VCZ were nucleoside metabolic processes, pigmentation processes, and transmembrane receptor protein tyrosine kinase signaling pathway. Interested readers can find more detailed information in Supplemental file 1.

Discussion

Why do trees grow faster under elevated [CO2]?

The results described in this study were from genome-wide microarray and thus may provide a more holistic picture of why elevated [CO2] can enhance tree growth. In our leaf samples, the genes in Calvin cycle were expressed at a 7.8 % higher rate under elevated [CO2] than in the control, while the genes involved in photorespiration were reduced 3.3 %. The linked pathways including gluconeogenesis, glycolysis I, glycolysis I (cytosolic), glycolysis IV (plant cytosol), glycolysis V, pentose phosphate pathway (non-oxidative branch) that consume the Calvin cycle products increased 8, 14.2, 21.8, 2.4, 21.8 and 10.5 %, respectively. The assimilated carbon can provide a stimulus for many biological processes through various mechanisms. Although most of these mechanisms remain elusive, work with algae indicated that elevated [CO2] can drive gene expression through activation of enhancers (Fukuzawa and Yamano 2005). In addition, it is well-known that newly synthetized carbohydrates can serve as signaling molecules to activate the expression of a large number of genes and change source/sink relationships (Avonce et al. 2005; Meyer et al. 2007; Nielsen et al. 2004; Thomas and Rodriguez 1994).

In VCZ, the enhancement of genes involved in carbohydrate metabolism is accompanied by the up-regulation of genes encoding cyclins, expansins, auxin amido synthetases, auxin influx carriers, ABA hydroxylase, glycosyl hydrolases, and Ras signaling proteins, and the down-regulation of ABA biosynthesis, and cytokinins degradation genes in the VCZ of CO2 treated trees. Since previous work clearly shows that cytokinins are the determinants of cambial activity (Nieminen et al. 2008), the ~39 % decrease in expression of the pathway genes responsible for cytokinin glucosylation, and the 28 and 13 % decrease in the expression of two genes, XM_002300706 and XM_002309432 encoding cytokinin oxidase, could result in increased levels of active cytokinins in the VCZ of CO2 fumigated trees. Additionally, the increased expression of multiple genes encoding clathrin, dynamin, tubule, kinesin and cyclins suggests the augmentation of activities of many cellular processes that include vesicular transport, mitosis, and cytokinesis in the VCZ in response to elevated [CO2].

Domain analysis shows genes encoding aquaporins and major intrinsic protein (water channel) significantly increased 74–195 % in VCZ under elevated [CO2], indicating elevated [CO2] can enhance water conductivity in developing wood. We assume this was to fulfill a need for cell expansion. We do not have sufficient data to explain why six genes encoding aquaporin and 10 genes encoding major intrinsic protein were on average down-regulated in leaves (Table 2) though we speculate this phenomenon has something to do with temporal drought, or water use efficiency. In previous study, Gupta et al. (2005) reported two genes encoding aquaporin PIP 2a were down-regulated 18–58 % in leaves in response elevated [CO2]. We also observed that two aquaporin genes encoding PIP 2b and one PIP subfamily gene in VCZ under elevated [CO2] were down-regulated 27–98 % in leaves (Table 1). Generally, it is assumed that elevated CO2 causes stomatal closure, and consequently, increases the water use efficiency (Conley et al. 2001; Li et al. 2008). Anyway, our finding of the up-regulation of TIP genes and down-regulation of PIP genes in VCZ upon CO2 fumigation can serve as a starting point for future research for exploitation of roles of aquaporin gene family in CO2 fumigated trees.

Lignocellulosic biosynthesis

The overall pathway of cellulose biosynthesis increased 33.7 % in VCZ and 0.2 % in leaves while the lignin biosynthesis pathway decreased 24 % in VCZ and 11.4 % in leaves. Most genes except three 4CL and one OMT1 in phenylpropanoid biosynthesis pathways were up-regulated. Since 4CL genes catalyze the 3rd step of phenylpropanoid biosynthesis pathway, beyond which, the pathway bifurcates into multiple branches, the down-regulation of three 4CL genes can constitute a rate limiting control on lignin biosynthesis. In addition, the expression of genes for ferulate and sinapate biosynthesis pathways that consume sinapaldehyde increased 14 % in VCZ and decreased 3.4 % in leaves. This piece of evidence is consistent with the idea that lignin synthesis is down-regulated in VCZ in response to elevated [CO2].

As we indicated earlier, down-regulation of lignin biosynthesis is important for maintaining the growth phase of cells in the VCZ. However, Druart et al. (2006) found lignin biosynthesis was stimulated by growth under elevated [CO2]. This inconsistency may be due in part to differences between our study and theirs in location of sampled materials and harvest time. They harvested stem materials from 15–25 cm below the apical meristems of 3-year-old coppice trees. In addition, they harvested their materials in November, as the growing season was ending and new growth often is declining, and intensive lignification is more likely (Casler et al. 2002; Macdonald 1986). We harvested our experimental materials during a period of active growth in July from the VCZ within the 1.5–2.5 meter region from ground of 12-year-old trees. In addition, Druart et al. (2006) used 800 and 1,200 μmol mol−1 [CO2] to treat plants, a much higher level than we used. All of these factors could create differences that are not yet clearly understood.

Noncellulosic polysaccharide synthesis in cell wall is enhanced under elevated [CO2]

We have shown that the overall gene expression of several pathways, including UDP-d-galacturonate biosynthesis I (from UDP-d-glucuronate), UDP-d-xylose biosynthesis, UDP-galactose biosynthesis (salvage pathway from galactose using UDP-glucose), UDP-glucose biosynthesis (from glucose 6-phosphate), UDP-l-arabinose biosynthesis I (from UDP-xylose), and UDP-l-rhamnose biosynthesis and xylan biosynthesis increased by at least 35 % in VCZ when compared to controls, and at least 19 % more increase as compared to the increase in leaves(Fig. 2). In agreement with these changes, the average expression of all 29 genes encoding glycosyltransferase CAZy GT 8 family proteins increased 34.7 % in the VCZ of CO2 treated trees. The proteins of this family are localized in the Golgi apparatus, the site of synthesis of noncellulosic polysaccharides (Herrero et al. 2004). This family is implicated in pectic polysaccharide biosynthesis (Bootten et al. 2004), which possibly define the extensibility of the cell wall and the incorporation of new polymers into the expanding cell wall.

All evidence we have shown here supports a significant increase in pectin and hemicellulose biosynthesis in response to growth under elevated [CO2] (Fig. 2). We speculate that these increases can contribute to cell division and expansion by supporting the formation of new wall material when cells divide or grow during rapid cambial growth. Since pectin and hemicellulose biosynthesis are closely linked to cellulose biosynthesis, the significant biosynthesis of pectin and hemicellulose can counterbalance cellulose biosynthesis. This may be a characteristic of the VCZ, where proper cell wall rigidity and extensibility need to be maintained in order to fulfill maximal growth potential. We observed a clearly coordinated increase among these three pathways in response to growth under elevated [CO2].

Possible mechanisms underpinning cambial growth

Our analyses suggest the presence of multiple mechanisms that are responsible for the increased cambial growth in aspen trees under elevated [CO2]. These include the up-regulation of the genes encoding auxin influx carriers, expansin gene family, and TIP aquaporin TIP family, and the down-regulation of the genes involved in ABA biosynthesis, cytokinin and auxin inactivation. In addition, the up- and down-regulation of f-box gene family, and genes involved in Ras-signaling pathway may play an even more important role in radial growth in response to elevated [CO2]. This is because the changes of genes in the Ras-signalling pathway are not only differentially expressed in VCZ but also more VCZ-specific (Table 1). Ras proteins are small GTPases that regulate cell growth, proliferation, and differentiation (Ma 2007; Smith 1999; Yang 2002). They are known to control the mouse stem cell cycle via PI3K signaling pathways (Takahashi et al. 2003). In yeast, another signaling pathway, called ‘the target of rapamycin (TOR)’, merges with the PI3K signaling pathway to control cell growth (Deprost et al. 2007; Nakashima et al. 2008; Zaragoza et al. 1998). Evidence shows that the TOR pathway in plants determines organ sizes (Deprost et al. 2007; Krizek 2009), whereas the PI3K signaling pathway controls the G1/S transition in stem cells by shortening the cell cycle from 24 to 10 h (Liu et al. 2006). In Arabidopsis, the loss-of-function PI3K mutant causes severe defects in growth (Welters et al. 1994). The up-regulation of five xylem-specific genes involved in Ras signal transduction suggests a thriving of Ras signaling during cambial growth.

Conclusion

The transcriptome profiles of aspen trees grown under elevated [CO2] for 12 years were dissected and contrasted with control trees. Elevated [CO2] enhanced expression of the genes catalyzing most of the reactions of carbon fixation (Calvin cycle) in leaves, which presumably would lead to the biosynthesis of more metabolites. Some specific metabolites could serve as signaling molecules to cause changes in gene expression in the VCZ. These changes could include, but would not be limited to, the genes involved in hormone metabolism, polysaccharide transport, cell division, cell wall loosening, and cell wall formation. Although expression of these genes changed by small magnitudes, collectively, increased expression changes could augment cambial and xylem cell division and expansion, thereby leading to significantly increased biomass production in perennial trees. Among multiple molecular mechanisms identified, it appears that hormone metabolism, cell division and expansion, as well as Ras signaling pathway were more conspicuous in the data we generated. Certainly, more studies on spatiotemporal samples from other tissues and at various stages of the growing season will be needed to learn the whole story of underlying molecular events inside of the CO2 fumigated trees.

Materials and methods

Aspen FACE experiment

The Aspen FACE experiment site was located near Rhinelander, WI, USA (89.5°W, 45.7°N). The experiment operated from 1998 through 2009 and used a full factorial design consisting of twelve 30-m diameter rings, three of which were allocated to each of four treatments: control (ambient CO2 of ≈360 μmol mol−1, ambient O3 of ≈36 nmol mol−1); elevated [CO2] (560 μmol mol−1); elevated O3 (1·5 × ambient); and elevated [CO2] plus elevated O3 (Dickson et al. 2000). Only trees from the control and elevated [CO2] treatments from two rings were used in this study. During the portion of 2009 prior to the date when plant materials for this study were obtained, control rings had an average [CO2] of 380 μmol mol−1 and the elevated [CO2] treatment had average [CO2] of 560 μmol mol−1). The elevated [CO2] treatment was applied during daylight hours from bud break in the spring until leaf senescence in the fall, a period that averages 146 day per year (King et al. 2005). The twelve 30-m diameter rings were fumigated using a FACE technology system that combines a gas monitoring system with a delivery system of blowers and vertical pipes placed around the plot perimeter (Dickson et al. 2000). The 1-min average CO2 concentrations were within 20 % of the target. The precipitation at the FACE site from 2007 to 2009 is shown in Table 3. Compared to other years, there was a temporal water shortage in June and July of 2009.
Table 3

The precipitation at the FACE site from 2007 to 2009

Month

2007 (mm)

2008 (mm)

2009 (mm)

Mar

61.7

22.1

43.2

Apr

31.2

83.5

80.1

May

NA

58.9

75.4

Jun

82.3

45.2

36.8

Jul

113.4

66.3

30.0

Plant materials

The aspen trees in the experiment were planted from cuttings in 1997 at a 1 m × 1 m spacing. Five trembling aspen genotypes of differing CO2 and O3 responsiveness were used in the main experiment (Dickson et al. 2000; Kubiske et al. 2007). On July 28, 2009, leaves and VCZ tissues were harvested from two aspen clones, 42E and 271 that have similar positive responses under elevated [CO2] and show no significant difference in height and diameter (Isebrand et al. 2001; Kubiske et al. 2007). Eight aspen trees: 2 clones (42E and 271) × 2 treatments (ambient control and elevated [CO2] × 2 replicates (8 trees in total) were initially harvested (Fig. 1). For each tree, we harvested two tissue types: leaves and VCZ. Leaf tissues were collected from ¼ height of canopy from the highest apical meristem when fumigation was in full operation, and VCZ samples were harvested by gently scratching a thin layer of ~10 μm around the vascular cambium at approximately 1.5–2.5 m aboveground, based on an earlier description of morphological sections (Du et al. 2006). The 8 samples for each type of tissues (16 samples in total) were harvested and then immediately flash-frozen in liquid nitrogen and subsequently stored in −80 °C freezers. Size differences between the sampled control and elevated [CO2] fumigated trees (Fig. 1) were consistent with the long-term measured growth increases reported for elevated [CO2] earlier in the experiment (King et al. 2005, Kubiske et al. 2007).

RNA isolation and preparation

Total RNA was isolated with a modified Qiagen RNeasy Mini kit protocol (Qiagen: http://www.qiagen.com/) as we described earlier (Busov et al. 2003). Approximately 0.2 g of VCZ or mature leaf tissue was ground in liquid nitrogen, and then 1 ml of lysis buffer (addition of 0.01 g of polyvinylpyrrolidone to the buffer before using) was added to the fine powder. After homogenizing 45 s using a Polytron, 400 μl of 5 M K-acetate was added and the slurry was incubated on ice for 15 min. The extracts were then centrifuged for 10 min at 4 °C at 16,000×g. The supernatant was mixed with 700 μl of 100 % (w/v) ethanol and this was applied to the RNeasy mini column. The remaining steps followed the kit procedures precisely. Prior to labeling with fluorescent dye, RNA quality was assessed with an Agilent Bioanalyzer (Agilent Technologies, USA).

Poplar arrays and hybridization

GeneChip® Poplar Genome Arrays were obtained from Affymetrix Inc. (Santa Clara, CA, USA). This platform is based on NCBI Genbank sequences of various poplar species that were available as of April 26, 2005 and the earlier released gene models from the Populus trichocarpa genome sequences (Tuskan et al. 2006). Each array contains about 61,413 oligonucleotide probe sets representing more than 61,252 transcripts from multiple poplar species, with several internal control genes for normalization. During material storage and transfer, we lost one treated sample and one control sample of clone 271 from leaves. As a result, we had 14 RNA samples being hybridized. They are: 8 RNA samples from VCZ, 2 clones (42E and 271) × 2 treatments (Control and CO2 treatment) × 2 replicates (trees); and 6 RNA samples from leaves, 2 treatments × 2 replicates for Clone 42E, and 2 treatment × 1 replicate for Clone 271. Prior to labeling, RNA quality was assessed by Agilent Bioanalyzer (Agilent Technologies, USA) and 0.2 μg of total RNA was used to prepare biotinylated complementary RNA (cRNA) by following Affymetrix’s GeneChip® 3′ IVT Expression Labeling Assay protocol. The hybridization, and imaging procedures were performed according to Affymetrix’s GeneChip® Expression Analysis Technical Manual using GCOS software at the at the Integrated Genomics Facility, Kansas State University, Manhattan, KS 66506, following the recommended procedure from Affymetrix (Affymetrix.com).

Data quality control and preparation

We used box-plots to check the mean and approximate distribution of each data set (chip). The Studentized deleted residual for each chip was then compared against a t-distribution (Persson et al. 2005a; Trivedi et al. 2005). Significant deviation from t distribution of Studentized deleted residuals for each data set indicates that the quality of the data set is problematic and provides a criterion for excluding that data set. Calculation of Kolmogorov–Smirnov for each sample was followed with an empirical cut-off of 0.15 as described in our earlier publication (Persson et al. 2005b) to identify quality chips. We normalized the data with robust multichip average (RMA) algorithm (Irizarry et al. 2003).

Identification of differentially expressed genes

Rank product (RP) (Breitling et al. 2004) was implemented to the data sets resulting from RMA normalization to identify DEGs. All genes were first sorted by p-values and then corrected with Benjamini and Hochberg False Discovery Rate (Benjamini and Hochberg 1995). The genelist was sorted again by the corrected p-values, and those genes with corrected p-values smaller than 0.05 were treated as DEGs.

Homology gene mapping and pathway analysis

The BLASTN program contained in the Basic Local Alignment Search Tool (BLAST) (Altschul et al. 1990) was used to identify the homologous genes between Affymetrix target sequences and gene model sequences of V2.0 releases for Populus trichocarpa (www.phytozome.org). Cut-off criteria applied included an E-value of 10 and a match of at least 90 % to the shorter sequence of either query or database. If more than one gene was matched, the one with maximal match was regarded as the homolog in the Populus trichocarpa genome. Due to the lack of annotation for most Populus genes, we also took advantage of the annotation for Arabidopsis genes to interpret some results generated from expression data analysis. BLASTX was employed to identify Arabidopsis homologous genes between Affymetrix target sequences and Arabidopsis protein sequences. The Arabidopsis protein sequences of TAIR10 release were downloaded from http://www.arabidopsis.org/. The criteria for selecting orthologs were similar to the criteria for BLASTN, except that the criterion for percent match was at least 40 %. Only the best-matched gene was considered to be the homologous gene. The pathway genes were identified by mapping all genes to the PoplarCyc and Aracyc pathway data stored in Plant Metabolic Network (http://plantcyc.org/), and the percentage change of each gene was calculated based on the averaged control and treatment. The overall change of each pathway was calculated by averaging the increased percentage portion of each gene.

Protein domain enrichment analysis

Protein domains were analyzed with InterproScan (Zdobnov and Apweiler 2001). We downloaded InterproScan and associated databases and installed them to our Linux server so that we could perform the standalone analysis to identify protein domains of all target sequences provided by Affymetrix. The enrichment of each domain in the DEG list was analyzed in the background of all genomic genes, and two parameters were introduced to show the enrichment of each domain: (1) Enrichment factor, EF = k/(mn/N); and (2) the Escore, which is the hypergeometric probability of this domain calculated using the following formula:
$$ E_{score} = 1 - \sum\limits_{i = 0}^{k - 1} {\frac{{\left( {\begin{array}{*{20}c} M \\ i \\ \end{array} } \right)\left( {\begin{array}{*{20}c} {N - M} \\ {n - i} \\ \end{array} } \right)}}{{\left( {\begin{array}{*{20}c} N \\ i \\ \end{array} } \right)}}} $$

where N is the total number of protein domains for all protein sequences in the genome, n is the total number for a specific domain in the genome, M is the number of all protein domains in the DEG list, and x is the number of a specific domain present in the DEGs list. The E_score represents the upper boundary of geometric probability. In order to compare the VCZ with leaves, the same number of genes as the DEGs in VCZ were cut-off from the sorted gene (by p values) of leaves and used for PDE analysis.

Gene ontology term enrichment analysis

The DEGs from VCZ were used for gene ontology analysis using AmiGO’s Term Enrichment tool (http://amigo.geneontology.org/). This tool uses Perl module GO:TermFinder available at CPAN (http://search.cpan.org/) to identify the enriched GO terms associated with a DEG list via hypergeometric probability as we described for protein domain above. We set the threshold p-value = 0.01 as the significance level. Similar to PED analysis, the same number of genes as the DEGs in VCZ were cut-off from the sorted gene (by p values) of leaves and used for gene ontology term enrichment analysis.

Supplementary material

10265_2012_524_MOESM1_ESM.docx (213 kb)
Supplementary material 1 (DOCX 213 kb)

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Copyright information

© The Botanical Society of Japan and Springer Japan 2012

Authors and Affiliations

  • Hairong Wei
    • 1
    • 2
    • 3
  • Jiqing Gou
    • 1
  • Yordan Yordanov
    • 1
  • Huaxin Zhang
    • 4
  • Ramesh Thakur
    • 1
  • Wendy Jones
    • 1
  • Andrew Burton
    • 1
  1. 1.School of Forest Resources and Environmental ScienceMichigan Technological UniversityHoughtonUSA
  2. 2.Department of Computer ScienceMichigan Technological UniversityHoughtonUSA
  3. 3.Biotechnology Research CenterMichigan Technological UniversityHoughtonUSA
  4. 4.Research Institute of ForestryChinese Academy of ForestryBeijingChina

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