TECHNICAL ADVANCE

The AtGenExpress hormone and chemical treatment dataset: experimental design, data evaluation, model dataanalysis and data access

Hideki Goda1,†, Eriko Sasaki1,†, Kenji Akiyama1, Akiko Maruyama-Nakashita1, Kazumi Nakabayashi1, Weiqiang Li1, Mikihiro

Ogawa1, Yukika Yamauchi1, Jeremy Preston1, Ko Aoki1, Takatoshi Kiba2, Suguru Takatsuto3, Shozo Fujioka4, Tadao Asami5,

Takeshi Nakano4, Hisashi Kato1, Takeshi Mizuno2, Hitoshi Sakakibara1, Shinjiro Yamaguchi1, Eiji Nambara1, Yuji Kamiya1,

Hideki Takahashi1, Masami Yokota Hirai1, Tetsuya Sakurai1, Kazuo Shinozaki1, Kazuki Saito1, Shigeo Yoshida1 and

Yukihisa Shimada1,*

1RIKEN Plant Science Center, Tsurumi, Yokohama, Kanagawa 230-0045, Japan,2Laboratory of Molecular Microbiology, School of Agriculture, Nagoya University, Furocho, Chikusa-ku, Nagoya 464-8601, Japan,3Department of Chemistry, Joetsu University of Education, Joetsu-shi, Niigata 943-8512, Japan,4RIKEN Discovery Research Institute, Wako, Saitama 351-0198, Japan, and5Department of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi,

Bunkyo-ku, Tokyo 113-8657, Japan

Received 22 February 2008; accepted 20 March 2008; published online 21 May 2008.

*For correspondence (fax +81 45 503 9492; e-mail shimada@postman.riken.go.jp).†These authors contributed equally to this work.

Summary

We analyzed global gene expression in Arabidopsis in response to various hormones and in related

experiments as part of the AtGenExpress project. The experimental agents included seven basic phytohor-

mones (auxin, cytokinin, gibberellin, brassinosteroid, abscisic acid, jasmonate and ethylene) and their

inhibitors. In addition, gene expression was investigated in hormone-related mutants and during seed

germination and sulfate starvation. Hormone-inducible genes were identified from the hormone response data.

The effects of each hormone and the relevance of the gene lists were verified by comparing expression profiles

for the hormone treatments and related experiments using Pearson’s correlation coefficient. This approach was

also used to analyze the relationships among expression profiles for hormone responses and those included in

the AtGenExpress stress-response data set. The expected correlations were observed, indicating that this

approach is useful to monitor the hormonal status in the stress-related samples. Global interactions among

hormones-inducible genes were analyzed in a pairwise fashion, and several known and novel hormone

interactions were detected. Genome-wide transcriptional gene-to-gene correlations, analyzed by hierarchical

cluster analysis (HCA), indicated that our data set is useful for identification of clusters of co-expressed genes,

and to predict the functions of unknown genes, even if a gene’s function is not directly related to the

experiments included in AtGenExpress. Our data are available online from AtGenExpressJapan; the results of

genome-wide HCA are available from PRIMe. The data set presented here will be a versatile resource for future

hormone studies, and constitutes a reference for genome-wide gene expression in Arabidopsis.

Keywords: chemical genomics, phytohormone network analysis, co-expression network analysis, systems

biology, AtGenExpress.

Introduction

The sequencing of the Arabidopsis genome (Arabidopsis

Genome Initiative, 2000) is the first step toward under-

standing its function. Genomic sequencing and the collec-

tion of large numbers of cDNAs have enabled researchers to

predict genome-wide genetic structure and to design DNA

microarrays to monitor genome-wide gene expression. The

526 ª 2008 The AuthorsJournal compilation ª 2008 Blackwell Publishing Ltd

The Plant Journal (2008) 55, 526–542 doi: 10.1111/j.1365-313X.2008.03510.x

Affymetrix ATH1 array, an oligonucleotide-based DNA

microarray consisting of 22 746 probe sets that cover

approximately 23 700 genes (nearly the entire Arabidopsis

genome) was designed based on computer-predicted genes

(Redman et al., 2004). This system has been widely accepted

by the plant science community because of its high sensi-

tivity and reproducibility. Using this system, an international

research effort, the AtGenExpress consortium, was orga-

nized to enhance the knowledge of gene function in Ara-

bidopsis. Large-scale transcriptome data sets developed by

the AtGenExpress consortium have already detailed the

developmental process (Schmid et al., 2005) and stress

responses (Kilian et al., 2007) in Arabidopsis. Here, we

present a hormone-response data set based on treatment

with phytohormones and hormone-related inhibitors, and

analysis of hormone-related mutants, seed germination and

sulfate starvation.

Seven major plant hormones play central roles in the

regulation of plant growth, development and the stress

response: auxin, cytokinin (CK), gibberellin (GA), brassi-

nosteroid (BR), abscisic acid (ABA), jasmonate (JA) and

ethylene. Numerous studies have investigated the func-

tions of each hormone at the physiological and molecular

levels; however, no study has considered all seven

hormones at the same time. We conducted a comprehen-

sive analysis of the plant responses to all seven phyto-

hormones using defined conditions. Our results will

enable members of the plant science community to

compare hormonal effects and to study the complex

interactions among hormone networks. In fact, the data

presented here have already been used to analyze

hormone-induced expression profiles (Nemhauser et al.,

2006). We describe our experimental design and data

validation method, how to access the data, and the results

of a model analysis. In the model analysis, interactions

among hormone-inducible genes were analyzed in a

pairwise manner with respect to their time dependence

and direction (up or down). We also demonstrate that the

hormone-inducible genes of this data set may be used to

monitor hormone status in an experiment performed

using the ATH1 GeneChip.

Together with the other contributions from the AtGen-

Express project, our data set forms a large-scale tran-

scriptome database. One purpose of this study was to

establish a comprehensive database to facilitate searching

for gene expression patterns; however, another and more

ambitious motivation was to analyze genome-wide

co-expression in Arabidopsis so that the functions of

unknown genes can be predicted based on similarities

between their expression patterns and those of known

genes. Thus, we performed a genome-wide co-expression

analysis using HCA (Eisen et al., 1998) of data from the

AtGenExpress project to demonstrate the usefulness of

large-scale transcriptome data sets.

Results and discussion

Experimental design and data overview

The AtGenExpress data set presented here (referred to

hereafter as the hormone series) includes treatment with

seven phytohormones, their inhibitors, hormone-related

mutants, and abiotic treatments, including seed imbibition

and sulfate starvation. The data were collected in five labo-

ratories at the RIKEN Plant Science Center and by colleagues

outside RIKEN. All hormones and inhibitors used are sum-

marized in Table 1. Seven hormones were applied to wild-

type seedlings. Indole-3-acetic acid (IAA) was used as auxin,

trans-zeatin was used as cytokinin, and 1-aminocycropro-

pane-1-carboxylic acid (ACC) was used in place of ethylene,

unless otherwise noted. In addition to the experiments on

wild-type seedlings, CK was applied to an ARR22-overex-

pressing line (Kiba et al., 2004), GA3 was applied to GA-defi-

cient ga1-5 seedlings, and GA4 was applied to ga1-3 seeds

(Koornneef and Van Der Veen, 1980). In addition, ABA was

applied to germinating wild-type seeds, and brassinolide (BL)

was applied to BR-deficient det2-1 seedlings (Li et al., 1996);

BR precursors were also applied to det2 seedlings. Moreover,

wild-type seedlings were exposed to inhibitors of GA, BR,

auxin and ethylene, other plant growth inhibitors, as well as

to salicylic acid (Table 1). BR-, GA-, and CK-related mutants

were also analyzed (Table 2). Germination was analyzed

using imbibed seeds to study hormonal function at this

developmental stage (Table 3). The effect of temperature on

seed germination was also assessed (Table 4). Seedlings

were transferred to sulfate-deficient medium to test sulfate-

regulated gene expression (Table 4). Each experiment was

performed twice or three times as biological replicates.

The number of detected genes (defined by detection

P < 0.05) ranged from 51 to 72% of total genes in all

experiments (Figure S1). Dry seeds and germinating seeds

expressed smaller numbers of genes compared to the other

samples. The total numbers of detected genes were com-

pared among the three major experiments conducted by

AtGenExpress: the development, stress and hormone series.

As shown in Figure 1, 21 095 genes were detected from 236

GeneChips in the hormone series, compared with 20 499

genes from 250 GeneChips in the stress series (Kilian et al.,

2007) and 21 268 genes from 237 GeneChips in the develop-

ment series (Schmid et al., 2005). In total, 476 genes were

unique to the development data set, 121 were unique to the

stress data set, and 359 genes were unique to the hormone

series. Newly detected genes from our data set are listed in

Table S1.

Hormone-inducible genes and hormone response validation

The effects of each hormone were confirmed using the

marker genes listed in Table 5. Each marker gene was up or

AtGenExpress hormone data 527

ª 2008 The AuthorsJournal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 55, 526–542

Tab

le1

AtG

enE

xpre

ss:

ho

rmo

ne

and

inh

ibit

or

trea

tmen

ts

No

.E

xper

imen

tLa

bo

rato

ryC

on

tro

lG

eno

typ

eT

reat

men

tT

ime

po

int

Org

an/t

issu

eG

row

thco

nd

itio

nA

ge

Rep

licat

esA

bb

revi

atio

n

Bas

ich

orm

on

etr

eatm

ent

of

seed

ling

sa

1A

Sh

imad

a&

Yo

shid

aT

rue

Co

l-0

Mo

ck30

min

See

dlin

gM

Sliq

uid

7d

ays

2M

ock

30m

(A1)

2A

Sh

imad

a&

Yo

shid

aC

ol-

01

lMIA

A30

min

See

dlin

gM

Sliq

uid

7d

ays

2IA

A30

m(A

1)3

AS

him

ada

&Y

osh

ida

Co

l-0

1l M

zeat

in30

min

See

dlin

gM

Sliq

uid

7d

ays

2Z

eati

n30

m(A

1)4

AS

him

ada

&Y

osh

ida

Co

l-0

1l M

GA

330

min

See

dlin

gM

Sliq

uid

7d

ays

2G

A30

m(A

1)5

AS

him

ada

&Y

osh

ida

Co

l-0

10lM

AB

A30

min

See

dlin

gM

Sliq

uid

7d

ays

2A

BA

30m

(A1)

6A

Sh

imad

a&

Yo

shid

aC

ol-

010

l MM

J30

min

See

dlin

gM

Sliq

uid

7d

ays

2M

J30

m(A

1)7

AS

him

ada

&Y

osh

ida

Co

l-0

10l M

AC

C30

min

See

dlin

gM

Sliq

uid

7d

ays

2A

CC

30m

(A1)

8A

Sh

imad

a&

Yo

shid

aC

ol-

010

nM

BL

30m

inS

eed

ling

MS

liqu

id7

day

s2

BL

30m

(A1)

9A

Sh

imad

a&

Yo

shid

aT

rue

Co

l-0

Mo

ck1

hS

eed

ling

MS

liqu

id7

day

s2

Mo

ck1h

(A2)

10A

Sh

imad

a&

Yo

shid

aC

ol-

01

l MIA

A1

hS

eed

ling

MS

liqu

id7

day

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IAA

1h(A

2)11

AS

him

ada

&Y

osh

ida

Co

l-0

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hS

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ling

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liqu

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day

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Zea

tin

1h(A

2)12

AS

him

ada

&Y

osh

ida

Co

l-0

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GA

31

hS

eed

ling

MS

liqu

id7

day

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GA

1h(A

2)13

AS

him

ada

&Y

osh

ida

Co

l-0

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AB

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ling

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day

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(A2)

14A

Sh

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aC

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010

l MM

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AS

him

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Co

l-0

10lM

AC

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ling

MS

liqu

id7

day

s2

AC

C1h

(A2)

16A

Sh

imad

a&

Yo

shid

aC

ol-

010

nM

BL

1h

See

dlin

gM

Sliq

uid

7d

ays

2B

L1h

(A2)

17A

Sh

imad

a&

Yo

shid

aT

rue

Co

l-0

Mo

ck3

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eed

ling

MS

liqu

id7

day

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Mo

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(A3)

18A

Sh

imad

a&

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shid

aC

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01

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A3

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eed

ling

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liqu

id7

day

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3h(A

3)19

AS

him

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Co

l-0

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liqu

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3h(A

3)20

AS

him

ada

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Co

l-0

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33

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MS

liqu

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day

s2

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3h(A

3)21

AS

him

ada

&Y

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ida

Co

l-0

10l M

AB

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ling

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(A3)

22A

Sh

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l MM

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3)23

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him

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l-0

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liqu

id7

day

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AC

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(A3)

24A

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a&

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010

nM

BL

3h

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2B

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(A3)

25A

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liqu

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day

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mo

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m(A

4)26

AS

him

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29A

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id7

day

s2

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T3h

(B)

528 Hideki Goda et al.

ª 2008 The AuthorsJournal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 55, 526–542

Tab

le1

(Co

nti

nu

ed)

No

.E

xper

imen

tLa

bo

rato

ryC

on

tro

lG

eno

typ

eT

reat

men

tT

ime

po

int

Org

an/t

issu

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row

thco

nd

itio

nA

ge

Rep

licat

esA

bb

revi

atio

n

40B

Sh

imad

a&

Yo

shid

ad

et2-

11

lMca

thas

tero

ne

3h

See

dlin

gM

Sliq

uid

7d

ays

2C

T3h

(B)

41B

Sh

imad

a&

Yo

shid

ad

et2-

11

lM6-

deo

xote

aste

ron

e3

hS

eed

ling

MS

liqu

id7

day

s2

6DT

E3h

(B)

42B

Sh

imad

a&

Yo

shid

ad

et2-

11

lMte

aste

ron

e3

hS

eed

ling

MS

liqu

id7

day

s2

TE

3h(B

)43

BS

him

ada

&Y

osh

ida

det

2-1

1l M

3-d

ehyd

ro-6

-deo

xote

aste

ron

e3

hS

eed

ling

MS

liqu

id7

day

s2

6D3D

T3h

(B)

44B

Sh

imad

a&

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shid

ad

et2-

11

l M3-

deh

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teas

tero

ne

3h

See

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uid

7d

ays

23D

T3h

(B)

45B

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11

l M6-

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aste

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See

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uid

7d

ays

26D

TY

3h(B

)46

BS

him

ada

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ida

det

2-1

1lM

typ

has

tero

l3

hS

eed

ling

MS

liqu

id7

day

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TY

3h(B

)47

BS

him

ada

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ida

det

2-1

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6-d

eoxo

cast

aste

ron

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hS

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ling

MS

liqu

id7

day

s2

6DC

S3h

(B)

48B

Sh

imad

a&

Yo

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ad

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110

0n

Mca

stas

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ne

3h

See

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uid

7d

ays

2C

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(B)

49B

Sh

imad

a&

Yo

shid

ad

et2-

110

nM

bra

ssin

olid

e3

hS

eed

ling

MS

liqu

id7

day

s2

BL

3h(B

)C

yto

kin

intr

eatm

ent

of

seed

ling

sc

50C

Sak

akib

ara

&M

izu

no

Tru

eC

ol-

0N

otr

eatm

ent

0h

See

dlin

gM

Sag

ar21

day

s3

Co

nt

(C1)

51C

Sak

akib

ara

&M

izu

no

Co

l-0

20lM

t-ze

atin

3h

See

dlin

gM

Sag

ar21

day

s3

Zea

tin

20lM

(C1)

52C

Sak

akib

ara

&M

izu

no

Tru

eA

RR

22-o

xN

otr

eatm

ent

0h

See

dlin

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ar21

day

s3

AR

R22

-ox

(C2)

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Sak

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ara

&M

izu

no

AR

R22

-ox

20lM

t-ze

atin

3h

See

dlin

gM

Sag

ar21

day

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AR

R22

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Zea

tin

20lM

(C2)

Eff

ect

of

AB

Ad

uri

ng

seed

imb

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ion

d

54D

Nam

bar

a&

Kam

iya

Tru

eC

ol-

0D

ryse

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0h

See

ds

N/A

0d

ay2

Dry

seed

(D)

55D

Nam

bar

a&

Kam

iya

Tru

eC

ol-

0Im

bib

edse

eds

(wat

er)

24h

See

ds

N/A

0d

ay2

Imb

ibed

24h

(D2)

56D

Nam

bar

a&

Kam

iya

Co

l-0

Imb

ibed

seed

s(3

lMA

BA

)24

hS

eed

sN

/A0

day

2Im

bib

ed24

h+

AB

A3

lM(D

2)57

DN

amb

ara

&K

amiy

aC

ol-

0Im

bib

edse

eds

(30

l MA

BA

)24

hS

eed

sN

/A0

day

2Im

bib

ed24

h+

AB

A30

lM(D

2)E

ffec

to

fg

ibb

erel

lind

uri

ng

seed

imb

ibit

ion

e

58E

Yam

agu

chi

&K

amiy

aT

rue

ga1

-3M

ock

3h

See

ds

Wat

er24

h2

ga1

-3im

bib

ed3h

(E1)

59E

Yam

agu

chi

&K

amiy

aT

rue

ga1

-3M

ock

6h

See

ds

Wat

er24

h2

ga1

-3im

bib

ed6h

(E2)

60E

Yam

agu

chi

&K

amiy

aT

rue

ga1

-3M

ock

9h

See

ds

Wat

er24

h2

ga1

-3im

bib

ed9h

(E3)

61E

Yam

agu

chi

&K

amiy

ag

a1-3

5l M

GA

43

hS

eed

sW

ater

24h

2g

a1-3

imb

ibed

3h+

GA

(E1)

62E

Yam

agu

chi

&K

amiy

ag

a1-3

5l M

GA

46

hS

eed

sW

ater

24h

2g

a1-3

imb

ibed

6h+

GA

(E2)

63E

Yam

agu

chi

&K

amiy

ag

a1-3

5l M

GA

49

hS

eed

sW

ater

24h

2g

a1-3

imb

ibed

9h+

GA

(E3)

Eff

ect

of

GA

inh

ibit

ors

on

seed

ling

sf .64

FS

him

ada

&Y

osh

ida

Tru

eC

ol-

0M

ock

3h

See

dlin

gM

Sliq

uid

7d

ays

2M

ock

3h(F

1)65

FS

him

ada

&Y

osh

ida

Tru

eC

ol-

0M

ock

12h

See

dlin

gM

Sliq

uid

7d

ays

2M

ock

12h

(F2)

66F

Sh

imad

a&

Yo

shid

aC

ol-

010

l Mp

rop

ico

naz

ole

3h

See

dlin

gM

Sliq

uid

7d

ays

2P

PI

3h(F

1)67

FS

him

ada

&Y

osh

ida

Co

l-0

10lM

pro

pic

on

azo

le12

hS

eed

ling

MS

liqu

id7

day

s2

PP

I12

h(F

2)68

FS

him

ada

&Y

osh

ida

Co

l-0

10l M

un

ico

naz

ole

3h

See

dlin

gM

Sliq

uid

7d

ays

2U

nic

3h(F

1)

AtGenExpress hormone data 529

ª 2008 The AuthorsJournal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 55, 526–542

Tab

le1

(Co

nti

nu

ed)

No

.E

xper

imen

tLa

bo

rato

ryC

on

tro

lG

eno

typ

eT

reat

men

tT

ime

po

int

Org

an/t

issu

eG

row

thco

nd

itio

nA

ge

Rep

licat

esA

bb

revi

atio

n

69F

Sh

imad

a&

Yo

shid

aC

ol-

010

lMu

nic

on

azo

le12

hS

eed

ling

MS

liqu

id7

day

s2

Un

ic12

h(F

2)70

FS

him

ada

&Y

osh

ida

Co

l-0

10lM

pac

lob

utr

azo

l3

hS

eed

ling

MS

liqu

id7

day

s2

Pac

3h(F

1)71

FS

him

ada

&Y

osh

ida

Co

l-0

10l M

pac

lob

utr

azo

l12

hS

eed

ling

MS

liqu

id7

day

s2

Pac

12h

(F2)

72F

Sh

imad

a&

Yo

shid

aC

ol-

010

lMp

roh

exad

ion

e3

hS

eed

ling

MS

liqu

id7

day

s2

PX

3h(F

1)73

FS

him

ada

&Y

osh

ida

Co

l-0

10l M

pro

hex

adio

ne

12h

See

dlin

gM

Sliq

uid

7d

ays

2P

X12

h(F

2)E

ffec

to

fau

xin

inh

ibit

ors

on

seed

ling

sg

74F

Sh

imad

a&

Yo

shid

aT

rue

Co

l-0

Mo

ck3

hS

eed

ling

MS

liqu

id7

day

s2

Mo

ck3h

(F1)

75F

Sh

imad

a&

Yo

shid

aC

ol-

010

l M2,

4,6T

3h

See

dlin

gM

Sliq

uid

7d

ays

224

6T3h

(F1)

76F

Sh

imad

a&

Yo

shid

aC

ol-

010

l MP

CIB

3h

See

dlin

gM

Sliq

uid

7d

ays

2P

CIB

3h(F

1)77

FS

him

ada

&Y

osh

ida

Co

l-0

10lM

TIB

A3

hS

eed

ling

MS

liqu

id7

day

s2

TIB

A3h

(F1)

78F

Sh

imad

a&

Yo

shid

aC

ol-

010

l MN

PA

3h

See

dlin

gM

Sliq

uid

7d

ays

2N

PA

3h(F

1)E

ffec

to

fb

rass

ino

ster

oid

inh

ibit

ors

on

seed

ling

sh

79F

Sh

imad

a&

Yo

shid

aT

rue

Co

l-0

Mo

ck3

hS

eed

ling

MS

liqu

id7

day

s2

Mo

ck3h

(F1)

80F

Sh

imad

a&

Yo

shid

aT

rue

Co

l-0

Mo

ck12

hS

eed

ling

MS

liqu

id7

day

s2

Mo

ck12

h(F

2)81

FS

him

ada

&Y

osh

ida

Co

l-0

10l M

Brz

220

3h

See

dlin

gM

Sliq

uid

7d

ays

2B

rz22

010

lM3h

(F1)

82F

Sh

imad

a&

Yo

shid

aC

ol-

010

l MB

rz22

012

hS

eed

ling

MS

liqu

id7

day

s2

Brz

220

10lM

12h

(F2)

83F

Sh

imad

a&

Yo

shid

aC

ol-

010

lMB

rz91

3h

See

dlin

gM

Sliq

uid

7d

ays

2B

rz91

3h(F

1)84

FS

him

ada

&Y

osh

ida

Co

l-0

10l M

Brz

9112

hS

eed

ling

MS

liqu

id7

day

s2

Brz

9112

h(F

2)85

GS

him

ada

&Y

osh

ida

Tru

eC

ol-

0M

ock

3h

See

dlin

gM

Sliq

uid

7d

ays

2M

ock

3h(G

)86

GS

him

ada

&Y

osh

ida

Co

l-0

3l M

Brz

220

3h

See

dlin

gM

Sliq

uid

7d

ays

2B

rz22

03

lM3h

(G)

Eff

ect

of

eth

ylen

ein

hib

ito

rso

nse

edlin

gsi .

87F

Sh

imad

a&

Yo

shid

aT

rue

Co

l-0

Mo

ck3

hS

eed

ling

MS

liqu

id7

day

s2

Mo

ck3h

(F1)

88F

Sh

imad

a&

Yo

shid

aC

ol-

010

l MA

gN

O3

3h

See

dlin

gM

Sliq

uid

7d

ays

2A

gN

O3

3h(F

1)89

FS

him

ada

&Y

osh

ida

Co

l-0

10l M

AV

G3

hS

eed

ling

MS

liqu

id7

day

s2

AV

G3h

(F1)

Eff

ect

of

oth

erin

hib

ito

rso

nse

edlin

gsj

90F

Sh

imad

a&

Yo

shid

aT

rue

Co

l-0

Mo

ck3

hS

eed

ling

MS

liqu

id7

day

s2

Mo

ck3h

(F1)

91F

Sh

imad

a&

Yo

shid

aC

ol-

010

lMC

HX

3h

See

dlin

gM

Sliq

uid

7d

ays

2C

HX

3h(F

1)92

FS

him

ada

&Y

osh

ida

Co

l-0

10lM

MG

132

3h

See

dlin

gM

Sliq

uid

7d

ays

2M

G13

23h

(F1)

93F

Sh

imad

a&

Yo

shid

aT

rue

Co

l-0

Mo

ck12

hS

eed

ling

MS

liqu

id7

day

s2

Mo

ck12

h(F

2)94

FS

him

ada

&Y

osh

ida

Co

l-0

1l M

PN

O8

3h

See

dlin

gM

Sliq

uid

7d

ays

2P

NO

81

lM3h

(F1)

95F

Sh

imad

a&

Yo

shid

aC

ol-

01

l MP

NO

812

hS

eed

ling

MS

liqu

id7

day

s2

PN

O8

1lM

12h

(F2)

96G

Sh

imad

a&

Yo

shid

aT

rue

Co

l-0

Mo

ck3

hS

eed

ling

MS

liqu

id7

day

s2

Mo

ck3h

(G)

97G

Sh

imad

a&

Yo

shid

aC

ol-

010

lMP

NO

83

hS

eed

ling

MS

liqu

id7

day

s2

PN

O8

10lM

3h(G

)98

FS

him

ada

&Y

osh

ida

Co

l-0

10l M

ibu

pro

fen

3h

See

dlin

gM

Sliq

uid

7d

ays

2Ib

up

3h(F

1)

530 Hideki Goda et al.

ª 2008 The AuthorsJournal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 55, 526–542

downregulated as reported in the studies cited for each

experiment. Differentially expressed genes between the

mock- and hormone-treated samples were identified as

hormone-inducible genes (see Experimental procedures for

details). Genes were first filtered by the detection P-value,

calculated using Affymetrix Microarray Suite software. Of

the 22 746 genes (probe sets) represented on the GeneChip,

18 775 passed this filtering. The signal intensities were then

analyzed by Welch’s t-test at each of three time points, and

further filtered based on the false discovery rate (FDR, q

value < 0.1; Storey and Tibshirani, 2003). The numbers of

hormone-inducible genes (at low stringency) are shown in

Table 6. Their probe ID, AGI code, P value, q value, signal

ratio, and annotation are shown in Table S2. ABA-inducible

genes formed the largest group. When GA or BR were

applied to wild-type seedlings, the gene expression

responses were smaller than in similarly treated GA- or

BR-deficient mutants (data not shown). This was probably

because the hormone levels or responses were saturated in

the wild-type plants. We therefore adopted the results for the

hormone-deficient mutants as the typical hormone response

(low-stringency lists, Table 6 and Table S2). These gene lists

were further used to analyze their overlap (Figure 5).

We assessed the relevance of the hormone responses and

hormone-inducible gene lists by comparing the results of

the hormone treatment experiments to those of additional

experiments or previous experiments using Pearson’s cor-

relation coefficient. In the BR experiment, we compared

BL-induced gene expression with that induced by a BR-bio-

synthesis inhibitor, brassinazole (Brz). The BR-deficient det2

mutant was treated with BL or given mock treatment for 3 h,

while wild-type seedlings were treated with Brz or given

mock treatment for 3 h. The resulting BL signal ratios (BL/

mock on the x axis) were compared with those of Brz (Brz/

mock on the y axis) for the BR-inducible genes listed in

Table S2. The correlation coefficient between the two

experiments was )0.58. Although a negative correlation

was observed, it was less significant than that observed in a

previous report ()0.79, Goda et al., 2002). Based on the FDR

q value threshold, we estimated that 10% of the identified

genes were false positives, which could create noise in the

Pearson’s correlation analysis. Thus, we optimized the

correlation coefficients by adjusting the threshold of gene

selection for each hormone (see Experimental procedures)

to further exclude false-positive genes. This made the

correlation coefficients more significant for each hormone

(Figure 2): )0.78 for BL versus Brz in the BR experiment (41

genes), )0.52 for GA versus prohexadione in the GA

experiment (49 genes), )0.86 for ACC versus ethylene

inhibitor aminoethoxy-vinylglycine (AVG) in the ethylene

experiment (41 genes), 0.70 for ABA versus mannitol

treatment in the ABA experiment (472 genes), 0.43 for zeatin

versus an ARR21C-overexpressing mutant in the CK exper-

iment (52 genes), 0.61 for MJ versus Botrytis cinereaTab

le1

(Co

nti

nu

ed)

No

.E

xper

imen

tLa

bo

rato

ryC

on

tro

lG

eno

typ

eT

reat

men

tT

ime

po

int

Org

an/t

issu

eG

row

thco

nd

itio

nA

ge

Rep

licat

esA

bb

revi

atio

n

99F

Sh

imad

a&

Yo

shid

aC

ol-

010

lMd

amin

ozi

de

3h

See

dlin

gM

Sliq

uid

7d

ays

2B

93h

(F1)

100

FS

him

ada

&Y

osh

ida

Co

l-0

10lM

salic

ylic

acid

3h

See

dlin

gM

Sliq

uid

7d

ays

2S

al3h

(F1)

aA

rab

ido

psi

sth

alia

na

seed

ling

s(g

eno

typ

esC

ol-

0,g

a1-5

,o

rd

et2-

1)w

ere

gro

wn

inh

alf-

stre

ng

thM

Sliq

uid

med

ium

for

7d

ays

at23

�Can

dtr

eate

dw

ith

IAA

,ze

atin

,G

A3,

AB

A,

MJ,

AC

Co

rB

Lfo

r30

min

,1

ho

r3

h.

bA

rab

ido

psi

sth

alia

na

seed

ling

s(d

et2-

1)w

ere

gro

wn

inh

alf-

stre

ng

thM

Sliq

uid

med

ium

for

7d

ays

at23

�Can

dtr

eate

dw

ith

cam

pes

tan

ol,

6-d

eoxo

cath

aste

ron

e,ca

thas

tero

ne,

6-d

eoxo

teas

tero

ne,

teas

tero

ne,

3-d

ehyd

ro-6

-deo

xote

aste

ron

e,3-

deh

ydro

teas

tero

ne,

6-d

eoxo

typ

has

tero

l,ty

ph

aste

rol,

6-d

eoxo

cast

aste

ron

e,ca

stas

tero

ne

or

bra

ssin

olid

efo

r3

h.

c Ara

bid

op

sis

thal

ian

ase

edlin

gs

(Co

l-0

or

AR

R22

-ox)

wer

eg

row

no

nM

Sag

arp

late

sfo

r21

day

sat

22�C

and

trea

ted

wit

ht-

zeat

info

r3

h.

dD

ryse

eds

of

Ara

bid

op

sis

thal

ian

a(C

ol-

0)w

ere

imb

ibed

inw

ater

or

3lM

AB

Afo

r24

hat

22�C

un

der

con

tin

uo

us

ligh

t.eS

eed

so

fAra

bid

op

sis

thal

ian

a(g

a1-3

)wer

eim

bib

edat

4�C

inth

ed

ark

for

48h

and

then

incu

bat

edfo

r24

hu

nd

erw

hit

elig

hta

t22�

C.T

he

seed

sw

ere

then

incu

bat

edu

nd

erth

esa

me

con

dit

ion

sw

ith

5l M

GA

4o

rw

ater

for

3,6

or

9h

.f A

rab

ido

psi

sth

alia

na

seed

ling

s(C

ol-

0)w

ere

gro

wn

inh

alf-

stre

ng

thM

Sliq

uid

med

ium

for

7d

ays

at23

�Can

dtr

eate

dw

ith

pro

pic

on

azo

le,u

nic

on

azo

le,p

aclo

bu

traz

olo

rp

roh

exad

ion

efo

r3

or

12h

.gA

rab

ido

psi

sth

alia

na

seed

ling

s(C

ol-

0)w

ere

gro

wn

inh

alf-

stre

ng

thM

Sliq

uid

med

ium

for

7d

ays

at23

�Can

dtr

eate

dw

ith

2,4,

6-tr

ihyd

roxy

ben

zam

ide,

p-c

hlo

rop

hen

oxy

iso

bu

tyri

cac

id,

2,3,

5-tr

iiod

ob

enzo

icac

ido

rn

aph

thyl

ph

thal

amic

acid

for

3h

.hA

rab

ido

psi

sth

alia

na

seed

ling

s(C

ol-

0)w

ere

gro

wn

inh

alf-

stre

ng

thM

Sliq

uid

med

ium

for

7d

ays

at23

�Can

dtr

eate

dw

ith

bra

ssin

azo

le22

0o

rb

rass

inaz

ole

91fo

r3

or

12h

.i A

rab

ido

psi

sth

alia

na

seed

ling

s(C

ol-

0)w

ere

gro

wn

inh

alf-

stre

ng

thM

Sliq

uid

med

ium

for

7d

ays

at23

�Can

dtr

eate

dw

ith

Ag

NO

3o

ram

ino

eth

oxy

vin

ylg

lyci

ne

for

3h

.j A

rab

ido

psi

sth

alia

na

seed

ling

s(C

ol-

0)w

ere

gro

wn

inh

alf-

stre

ng

thM

Sliq

uid

med

ium

for

7d

ays

at23

�Can

dtr

eate

dw

ith

CH

X(c

yclo

hex

imid

e),M

G13

2(c

arb

ob

enzo

xyl-

leu

cin

yl-l

euci

nyl

-leu

cin

al),

PN

O8

(N-o

ctyl

-3-n

itro

-2,4

,6-t

rih

ydro

xyb

enza

mid

e),

ibu

pro

fen

,d

amin

ozi

de

or

salic

ylic

acid

for

3o

r12

h.

AtGenExpress hormone data 531

ª 2008 The AuthorsJournal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 55, 526–542

infection (Ferrari et al., 2007) in the JA experiment (758

genes), and 0.94 for IAA versus previous IAA treatment data

(Goda et al., 2004) in the auxin experiment (143 genes).

These data indicate that the gene expression profiles for

each hormone are well correlated with those in additional or

previous experiments, as were the direction of the correla-

Table 2 AtGenExpress: various genotypes

No. Experiment Laboratory Control Genotype TreatmentTimepoint Organ/tissue

Growthcondition

Age(days) Replicates Abbreviation

Comparison of plant hormone-related mutantsa

101 H Shimada & Yoshida True Ws-2 No treatment N/A Seedling MS liquid 7 2 WT-WS (H)102 H Shimada & Yoshida bri1-5 No treatment N/A Seedling MS liquid 7 2 bri1 (H)103 K Shimada & Yoshida True Ler-1 No treatment N/A Seedling MS liquid 7 2 WT-Ler (K)104 K Shimada & Yoshida ga1-5 No treatment N/A Seedling MS liquid 7 2 ga1 (K)ARR21C overexpressionb

105 L Sakakibara & Mizuno True Col-0 No treatment N/A Seedling MS agar 21 3 Cont (L)106 L Sakakibara & Mizuno ARR21C-ox No treatment N/A Seedling MS agar 21 3 ARR21C-ox (L)

aSeedlings were grown in half-strength MS liquid medium for 7 days at 23�C.bSeedlings were grown on MS agar plates for 21 days at 22�C.

Table 3 AtGenExpress: development (time course of early seed germination)a

No. Experiment Laboratory Control Genotype TreatmentTimepoint (h) Organ/tissue

Growthcondition

Age(days) Replicates Abbreviation

107 M Nambara & Kamiya True Col-0 No treatment 0 Seeds N/A 0 2 Dry seed (M)108 M Nambara & Kamiya Col-0 Imbibed seeds

(water)1 Seeds N/A 0 2 Imbibed

1 h (M)109 M Nambara & Kamiya Col-0 Imbibed seeds

(water)3 Seeds N/A 0 2 Imbibed

3 h (M)

aDry seeds of Arabidopsis thaliana (Col-0) were imbibed in water for 1 or 3 h at 22�C under continuous light.

Table 4 AtGenExpress: abiotic treatments

No. Experiment Laboratory Control Genotype TreatmentTimepoint(h)

Organ/tissueGrowthcondition

Age(days) Replicates Abbreviation

Differential temperature treatment of seedsa

110 N Yamaguchi &Kamiya

True Ler-0 Incubatedat 22�C

96 Seeds N/A 0 2 Imbibed96h22dg. (N)

111 N Yamaguchi &Kamiya

Ler-0 Incubatedat 4�C

96 Seeds N/A 0 2 Imbibed96h4dg. (N)

Response to sulfate limitationb

112 P Takahashi True Col-0 Mock 0 Root MGRL agar 10 2 +S 0h (P)113 P Takahashi True Col-0 Mock 2 Root MGRL agar 10 2 +S 2h (P2)114 P Takahashi True Col-0 Mock 4 Root MGRL agar 10 2 +S 4h (P3)115 P Takahashi True Col-0 Mock 8 Root MGRL agar 10 2 +S 8h (P4)116 P Takahashi True Col-0 Mock 12 Root MGRL agar 10 2 +S 12h (P5)117 P Takahashi True Col-0 Mock 24 Root MGRL agar 10 2 +S 24h (P6)118 P Takahashi Col-0 0 lM sulfate 2 Root MGRL agar 10 2 )S 2h (P2)119 P Takahashi Col-0 0 lM sulfate 4 Root MGRL agar 10 2 )S 4h (P3)120 P Takahashi Col-0 0 lM sulfate 8 Root MGRL agar 10 2 )S 8h (P4)121 P Takahashi Col-0 0 lM sulfate 12 Root MGRL agar 10 2 )S 12h (P5)122 P Takahashi Col-0 0 lM sulfate 24 Root MGRL agar 10 2 )S 24h (P6)

aSeeds of Arabidopsis thaliana (Ler-0), were irradiated with a far-red light pulse 1 h after imbibition, and then incubated for 96 h in the dark at22 or 4�C.bArabidopsis thaliana seedlings (Col-0) were grown vertically for 10 days on MGRL agar medium containing 1500 lM sulfate at 22�C under 16 h/8 hlight/dark cycles and then transferred to medium containing or lacking sulfate.

532 Hideki Goda et al.

ª 2008 The AuthorsJournal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 55, 526–542

tions (positive or negative). The number of hormone-induc-

ible genes decreased under these highly stringent condi-

tions because the number of false-negative genes increased.

These gene lists are considered highly stringent lists

(Table S5) and were used to analyze and estimate hormone

status as described below.

Analysis of hormone actions

Existing statistical analyses or clustering methods for

microarray data often fail to answer the specific questions of

interest to biologists (Dhollander et al., 2007; Hibbs et al.,

2007; Ivakhno and Armstrong, 2007). Moreover, compari-

sons of data across multiple laboratories have shown that the

data sets for each laboratory tend to be more similar to each

other than they are to those of other laboratories (data not

shown). Likewise, data on similar tissues from different

laboratories tend to be similar even though they focus on

different responses. Thus, developing methods to elucidate

clear biological relationships from high-dimensional bio-

logical data is very important. As shown in Figure 2, we

detected similarities between two independent experiments

using hormone-inducible genes selected by hormone treat-

ment, even when conducted in different laboratories using

samples of different tissues at different developmental

stages. We applied this approach to estimate hormone status

in multiple expression profiles from the AtGenExpress stress

series data set (Kilian et al., 2007). The experimental treat-

ments in this data set include cold, drought, UV-B, high salt,

high osmolarity, heat and wounding. Signal ratio values

(hormone treatment/mock, or stress treatment/control) were

calculated for both experiments and then transformed to a

log2 scale to adjust the origin (i.e. zero point). Then Pearson’s

correlations were calculated for each combination of stres-

sed sample and hormone-treated sample using the log signal

ratios for each hormone-inducible gene (highly stringent list

in Table S5). Significant correlations with ABA treatment

were observed soon after osmotic stress (Figure 4b).

Figure 1. Comparison of the numbers of detected genes in the AtGenExpress

project.

The numbers of genes detected at a significant level (detection level P < 0.05)

were compared among the hormone, stress and development data sets.

Table 5 Marker genes used to confirm the hormone responses

Gene symbol AGI locus Reference

AuxinIAA1 At4g14560 Abel et al. (1995)IAA2 At3g23030 Abel et al. (1995)

CytokininARR5 At3g48100 Taniguchi et al. (1998)ARR6 At5g62920 Taniguchi et al. (1998)ARR15 At1g74890 Kiba et al. (2002)

GibberellinAt-EXP1 At1g69530 Ogawa et al. (2003)GAI At1g14920 Ogawa et al. (2003)SCL3 At1g50420 Ogawa et al. (2003)

Abscisic acidRD29A At5g52310 Yamaguchi-Shinozaki

and Shinozaki (1993)COR15A At2g42540 Wilhelm and

Thomashow (1993)Jasmonate

JMT At1g19640 Seo et al. (2001)OPR3 At2g06050 Mussig et al. (2000)JIN1/MYC2/RAP1 At1g32640 Lorenzo et al. (2004)

EthyleneERS2 At1g04310 Hua et al. (1998)EFE At1g05010 Gomezlim et al. (1993)

BrassinosteroidBR6ox2/CYP85A2 At3g30180 Shimada et al. (2003)CPD/CYP90A At5g05690 Mathur et al. (1998)DWF4/CYP90B At3g50660 Noguchi et al. (2000)

Table 6 Number of genes differentially expressed followinghormone treatment

Time

30 min 1 h 3 h

WT + ABA (up) 14 267 1965WT + ABA (down) 17 225 1661WT + ACC (up) 164 145 29WT + ACC (down) 495 491 60det2 + BL (up) 0 76 923det2 + BL (down) 0 43 870ga1 + GA (up) 0 39 183ga1 + GA (down) 1 77 145WT + IAA (up) 33 61 250WT + IAA (down) 23 28 388WT + MJ (up) 231 415 576WT + MJ (down) 328 464 455WT + zeatin (up) 20 39 104WT + zeatin (down) 6 37 121

AtGenExpress hormone data 533

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Figure 2. Confirmation of the hormone res-

ponses and hormone-inducible genes.

Gene expression in response to hormone expo-

sure was confirmed as follows. A scatterplot of

the hormone-inducible genes (Table S2) was

drawn for each hormone. The x axis indicates

transcript responses as a log signal ratio (hor-

mone treatment/mock treatment), and the y axis

indicates transcript responses as a log signal

ratio for separate experiments.

(a) ABA treatment versus osmotic stress (man-

nitol treatment).

(b) ACC treatment versus ethylene biosynthesis

inhibitor treatment (AVG).

(c) BL treatment versus BR biosynthesis inhibitor

treatment (Brz220 for 3 h).

(d) Zeatin treatment versus an ARR21C-overex-

pressing mutant.

(e) GA treatment versus GA the biosynthesis

inhibitor treatment (prohexadione).

(f) IAA treatment (x) versus previous IAA exper-

iments (y).

(g) MJ treatment versus infection with Botrytis

cinerea.

534 Hideki Goda et al.

ª 2008 The AuthorsJournal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 55, 526–542

Interestingly, the correlation was observed at an early stage

in the roots, beginning at 30 min and reaching a plateau after

1 h, whereas it was observed in the shoot after 1 h and did

not reach a plateau until 3 h, suggesting that the stress

response begins in the roots and then spreads to the shoot.

The result was consistent with the design of the experiments,

as the osmotic stress was applied to the roots (Kilian et al.,

2007). Similarly, a correlation with ABA was also observed

for cold stress; however, it was delayed and only became

significant 6 h after treatment (Figure 4a). The correlation

with ABA was even weaker for drought treatment (data not

shown), suggesting that the conditions used were mild. This

is consistent with the observations by Kilian et al. (2007),

because the drought-induced transcript response was small

compared to other stress-induced responses. Correlations

were also detected between ACC treatments and wounding

or between MJ treatments and wounding (Figure 4c), con-

sistent with previous studies showing that ethylene and JA

function in the response to wounding. An additional corre-

lation was detected between ACC treatment and UV-B irra-

diation (Figure 4d). In contrast to the strong interaction

between stress and stress-related hormones, we detected

little interaction with growth-related hormones. Neverthe-

less, we detected correlations that probably indicate novel

hormone actions, as follows. GA was inactivated by various

stressors, including cold, high osmolarity, wounding and UV

light. In contrast, CK was activated transiently by cold and

osmotic stress, especially in the roots. Thus, this approach

allowed us to simultaneously predict the status of all seven

hormones from a single experiment. Formerly, it was nec-

essary to rely on marker genes and endogenous hormones to

study hormone function because there was no conclusive

means to monitor the in vivo status of multiple hormones at

the same time.

Analysis of the hormone network

To obtain insight into the relationships among phytohor-

mones, we investigated whether there was significant

overlap between each set of hormone-inducible genes. The

hormone-inducible genes (Table S2) were classified into

groups based on whether they were up or downregulated at

each of three time points (30 min, 1 h and 3 h), and the

groups were compared in a pairwise manner using Fisher’s

exact test. A significant number of overlapped genes were

detected (pink in Table S4), more than would be expected by

chance, indicating that hormones regulate shared target

genes at the transcriptional level. This result was clearer

when the overlap was considered in terms of the direction

and/or time dependence of the response. For example, if

there was significant overlap between genes that were

inducible by hormone A and those that were inducible by

hormone B genes, and if the overlap was observed in the

same direction (i.e. the genes upregulated by hormone A

overlapped with those upregulated by hormone B and the

genes downregulated by hormone A overlapped with those

downregulated by hormone B), the two hormones regulate

common target genes in the same direction. Such interac-

tions were termed complete positive interactions (Fig-

ure 5a). In contrast, if the overlap occurred in opposite

directions (i.e. the genes upregulated by hormone A over-

lapped with those downregulated by hormone B, and vice

versa), the interaction was termed a complete negative

interaction (Figure 5b). Finally, if the overlap was incomplete

(i.e. there was significant overlap between the genes up-

regulated by hormone A and those upregulated by hormone

B, but not between the genes downregulated by hormone A

and those downregulated by hormone B), the interaction

was designated a partial positive interaction (Figure 5c,d) or

a partial negative interaction (Figure 5e,f). In partial inter-

actions, it is unlikely that the interaction can be accounted

for by regulation at the endogenous hormone level, even if

the amount of change is controlled by its transport. Partial

interactions are probably the result of limited overlap

between signaling pathways (e.g. two hormones share a

transcriptional regulator that may function as either an

activator or repressor). A partial positive interaction was

observed between auxin and BR (Figure 5h and Table S4),

which is consistent with previous reports showing that these

two hormones act synergistically in monocots and dicots.

Cross-talk between the two signaling pathways has been

suggested in recent molecular and genetic studies (Nakam-

ura et al., 2006; Nemhauser et al., 2004).

A complete interaction can be accounted for by one of two

mechanisms: either one of the hormones promotes the

active level of the other or they share the same signaling

pathway to promote sensitivities of each other. These

possibilities may be distinguished by considering the timing

of the interaction as follows. If hormone A induces the

biosynthesis of hormone B, induction of gene expression by

hormone B will be observed at a later time point after

hormone A treatment, and no early induction of gene

expression by hormone A will be observed following

hormone B treatment (Figure 5g). Consistent with this

model, early ethylene-inducible gene expression was ob-

served 3 h after auxin treatment (Figure 5h and Table S4),

whereas no early auxin-inducible gene expression was

observed following ACC (ethylene) treatment. This is con-

sistent with the fact that exogenous auxin induces ethylene

biosynthesis in various organs in many species. Given that a

similar interaction was found between auxin and ABA

(Figure 5h and Table S4), auxin probably induces the bio-

synthesis of ABA. In fact, NCED5/At1g30100, an ABA

biosynthetic gene, was induced by IAA treatment (signal

ratio = 4.6, P-value = 0.064, q value = 0.12, t-test at 3 h).

Positive interactions were also identified among the stress-

related hormones ABA, ethylene and JA (Figure 5h and

Table S4). Interestingly, because no time-dependent inter-

AtGenExpress hormone data 535

ª 2008 The AuthorsJournal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 55, 526–542

action was observed, stress-related hormones are more

likely to share signaling pathways than to activate the

biosynthesis of one another. The hormone network pre-

sented in Figure 5(h) is based on expression profiles from

Arabidopsis seedlings; it may not be applicable to other

tissues or species. Nevertheless, our results demonstrate the

usefulness of this type of analysis in studying hormone

networks and functions. This approach will be applicable to

other tissues and species once additional experiments have

been conducted. Nemhauser et al. (2006) used our data to

analyze hormone networks from the point of view of

hormone metabolism. The data presented here provide a

starting point from which to advance the understanding of

plant hormone networks using a systems-based approach.

Co-expression analysis of Arabidopsis genes by

genome-wide hierarchical clustering

One of the purposes of this study was to establish a com-

prehensive database to facilitate gene expression profile

analysis. Our project allows access to gene expression pro-

files on an unprecedented scale, which allows more precise

analysis. Using this data set, we attempted to calculate the

correlation between all genes in Arabidopsis and to describe

their relationships in one network. To this end, we used the

HCA method (Eisen et al., 1998). Genome-wide gene-to-gene

correlations were calculated as described in Experimental

procedures. Example clusters were extracted from the

global clustering results. The BR biosynthetic genes

At5g05690/CPD (Szekeres et al., 1996), At3g50660/DWF4

(Choe et al., 1998) and At3g30180/BR6ox2 (Kim et al., 2005;

Nomura et al., 2005), and the BR signaling components,

At3g61460/BRH1 (Molnar et al., 2002) and At4g36780 [a

homolog of BZR1 (Wang et al., 2002)], are clustered together

with functionally unknown genes that encode a helix-loop-

helix DNA-binding protein (At5g57780) and a wound-

responsive protein homolog (At5G01740) (Figure 3). These

genes were generally upregulated in the BR-deficient

mutant det2 and in the BR-insensitive mutant bri1. They

were upregulated by triazole inhibitors such as Brz. As these

genes are expressed in a strictly coordinated manner, they

probably have coordinated biological roles. Many genes

encoding ribosomal proteins were clustered with the

ubiquitin genes UBQ1, UBQ2 and UBQ6, as well as with

other unknown genes (Figure S2a). The ubiquitin genes

have been used as stable, internal standard genes in gene

expression analysis. Interestingly, several other ubiquitin

genes were not clustered closely with these genes (data not

Figure 3. Genome-wide hierarchical cluster analysis.

All genes represented on the ATH1 chip were clustered hierarchically using

AtGenExpress data sets. A cluster related to BR biosynthesis was extracted

from the genome-wide results. The colors represent the relative expression

level of each experimental group, where red is higher expression and green is

lower expression. The AGI code is shown for each gene.

536 Hideki Goda et al.

ª 2008 The AuthorsJournal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 55, 526–542

shown), indicating that not all ubiquitin genes are expressed

in the same manner. The genes in this cluster were down-

regulated in dry seeds as well as in seeds in the early stages

of germination, but they were expressed relatively stably

throughout all experiments. Thus they may be appropriate

as internal controls for gene expression studies. The data set

provided useful information beyond the scope of the

experimental design of the AtGenExpress. For example,

although HCA analysis did not include data from experi-

ments focusing on the cell cycle, we observed a cluster of

cell cycle-related genes. Many histones, cyclins and cell

division-related genes were clustered together among many

unknown genes (Figure S2b). They were expressed

predominantly in the shoot apex. Similarly, although no

experiments concerning anthocyanin biosynthesis were

conducted, we observed a cluster of genes related to

anthocyanin metabolism. This cluster included PAP1, ANS,

DFR/TT3, AtGST12/TT19, TTG2 (Lepiniec et al., 2006),

UGT75C1 (Tohge et al., 2005) and UGT79B1 (Yonekura-

Sakakibara et al., 2007), which are predominantly expressed

in GA-deficient mutants (Figure S2c). These results indicate

that the AtGenExpress data set is sufficiently broad and

diverse to detect a large proportion of possible gene-to-gene

correlations.

Data presentation and its utility

Our hormone series data has been available to the public on

the AtGenExpress Japan Web site (Table 7) since 2004. The

data were also incorporated into the gene expression

resource home page of the Arabidopsis Information

Resource (TAIR; Table 7), NASCArrays (Table 7), and the

Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/

geo/), as recommended in MIAME (http://www.mged.org/

Workgroups/MIAME/miame.html). The data have also been

used actively by informatics biologists to develop various

tools or to conduct specific analyses. The amount and for-

mat of the original data were not user-friendly for non-

informatics biologists. Therefore, demand developed for a

biologist-oriented data presentation to facilitate the use of

such a large-scale data set. The results presented above,

based on HCA, represent our response to this demand.

Genome-wide gene-to-gene correlations calculated by HCA

can be used to show relationships between genes based on

Figure 4. Correlations between the expression profiles for the hormone and stress treatment data.

Hormone status in the AtGenExpress stress series data (Kilian et al., 2007) was analyzed using the expression profile of hormone-responsive genes (high-stringency

list). The stress series data were collected from shoots (gray bars) and roots (white bars) exposed to stress for 0.5–24 h. Pearson’s correlation coefficients were

calculated for cold (a), osmotic (b), wounding (c) and UV-B (d) stress data, and are shown as bar graphs for each time point.

AtGenExpress hormone data 537

ª 2008 The AuthorsJournal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 55, 526–542

Figure 5. Overlap between the hormone-induc-

ible genes and hormone networks of Arabidopsis

seedlings.

The relationships among the hormone-inducible

genes were analyzed using Fisher’s exact test.

The hormone-inducible genes (Table S2) were

classified as up or downregulated for each time

point (30 min, 1 h or 3 h). The groups

(7 · 3 · 2 = 42 groups in total) were then ana-

lyzed using Fisher’s exact test in a pairwise

manner.

The resulting relationships were classified as

complete positive (a), complete negative (b),

partial positive (c, d) or partial negative (e, f),

depending on the type of overlap. Circles indi-

cate a significantly higher frequency of overlap

than expected to occur at random. A cross

indicates that the overlap was not significant.

Each complete positive interaction was further

tested to assess whether or not the interaction

was time-dependent (g). If the interaction did not

fit the above criteria, it was defined as a complex

interaction. (h) Summary of the results for all

seven hormones.

Table 7 Web sites related to AtGenExpress

Name Address Reference

RIKEN AtGenExpress Japan http://pfg.psc.riken.jp/AtGenExpress/index.htmlTAIR (AtGenExpress) http://www.arabidopsis.org/info/expression/ATGenExpress.jspNASCArrays http://affymetrix.arabidopsis.info/narrays/experimentbrowse.pl Craigon et al. (2004)RIKEN PRIMe (Cluster Cutting) http://prime.psc.riken.jpGenevestigator http://www.genevestigator.ethz.ch Zimmermann et al. (2005)The Bio Array Resource http://bar.utoronto.ca/ Toufighi et al. (2005), Winter et al. (2007)ATTED-II http://www.atted.bio.titech.ac.jp/ Obayashi et al. (2007)Weigelworld http://www.weigelworld.org/resources/microarray/AtGenExpress/CSB-DB http://csbdb.mpimp-golm.mpg.de/ Steinhauser et al. (2004)

538 Hideki Goda et al.

ª 2008 The AuthorsJournal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 55, 526–542

their expression patterns and to visualize gene expression

levels at the same time. Although the results of HCA are

useful when used on a genome-wide scale, the amount of

information produced is too large to be visualized on a

personal computer. Therefore, we have developed a web-

based system called Cluster Cutting that visualizes portions

of our global data (Figure S3A). By inputting a gene’s AGI

code into the Locus ID window (Figure S3B), Cluster Cutting

will extract the HCA results for that gene together with its

co-expressed genes (default of 100 correlated genes). The

results can then be opened by clicking ‘View AtXgXXXXX

100-node’ using JAVA Treeview software (Saldanha, 2004).

In this way, the genes that are correlated with a gene of

interest can be visualized with their expression patterns. By

downloading the result files (result table file, gene tree file

and array tree file), the results can be opened locally using

JAVA Treeview (available at http://sourceforge.net/projects/

jtreeview/) for further data presentation. As far as we are

aware, this system is unique because it presents transcrip-

tional gene-to-gene correlations for the entire Arabidopsis

genome in a single network.

The data set from the AtGenExpress project has been

incorporated and used in web-based tools and databases.

For example, the Arabidopsis Information Resource (Rhee

et al., 2003) presents a microarray expression search site

that offers various search methods for genes and experi-

ments, and presents gene expression patterns both in terms

of the signal value and percentile. Genevestigator (Zimmer-

mann et al., 2004, 2005) was one of the first web sites to offer

AtGenExpress data, and provides the most versatile tools for

analyzing and visualizing gene expression data. The Botany

Array Resource provides electronic Northerns (Toufighi

et al., 2005), electronic fluorescent pictographs (Winter

et al., 2007) and other tools by means of a user-friendly

interface that presents gene expression patterns in a visually

appealing manner. ATTED-II provides a co-expression

analytical tool that is ready to draw gene-to-gene networks,

and the co-expression results are also used to predict

cis-regulatory elements (Obayashi et al., 2007).

Several studies have been conducted using AtGenExpress

data. For example, Nemhauser et al. (2006) used the data

pertaining to the basic hormone treatments, and reported

that hormones regulate non-overlapping transcriptional

responses. Czechowski et al. (2005) identified genes with

very stable expression patterns as reference genes for gene

expression studies. Many other studies have utilized the

gene expression data available on the web sites. Geneves-

tigator has helped researchers to develop new hypotheses

(Grennan, 2006), and this reference has been cited >400

times in the last two years. While some of these reports

simply refer to gene expression patterns based on the data

from AtGenExpress, others present more advanced analy-

ses. For example, Nafisi et al. (2007) identified the function

of CYP71A13, a P450 enzyme involved in the biosynthesis of

the phytoalexin camalexin. They used Genevestigator to

identify similarities in the gene expression patterns of

CYP71A13 and CYP71B15, another enzyme involved in

camalexin biosynthesis. Yonekura-Sakakibara et al. (2007)

used co-expression analysis (ATTED-II) to identify the gene

encoding flavonol 7-O-rhamnosyltransferase. Co-expres-

sion analysis using large-scale expression profile data is

becoming an essential tool in plant biology (see Aoki et al.,

2007; Saito et al., 2008). The AtGenExpress data presented

here will be an indispensable resource for both plant

biologists and informaticians.

Experimental procedures

Plant material and growth conditions

Our plant materials, growth conditions and sample treatment pro-tocols are described in Tables 1–4. Further details on these proto-cols can be obtained from the references cited: treatment ofseedlings with hormones and inhibitors (Goda et al., 2002, 2004;Sawa et al., 2002), CK mutants (Kiba et al., 2004, 2005), sulfatestarvation (Maruyama-Nakashita et al., 2005), GA treatment duringimbibition (Ogawa et al., 2003), temperature variation during imbi-bition (Yamauchi et al., 2004), and ABA treatment during imbibition(Nakabayashi et al., 2005).

GeneChip analysis

RNA preparation and GeneChip (Affymetrix) analysis were con-ducted as described previously (Goda et al., 2002, 2004). Shimada’s,Nambara’s and Yamaguchi’s groups performed the hybridizationand scanning steps at a high level of sensitivity to focus on thosegenes that function in hormone signaling, because their transcriptsare relatively less abundant. Under these conditions, signals ofhighly abundant transcripts may reach saturation. Other groupsperformed these steps under standard conditions following manu-facturers’ instructions. The data were analyzed using MicroarraySuite version 5 (MAS5; Affymetrix) to calculate the signal valuesand detection P-values. The 50th percentile of all measurementswas used as the positive control for each sample (i.e. each Gene-Chip). The signal for each probe set on a particular chip was dividedby the synthetic positive control (per-chip normalization). Thequality of each repeated experiment was controlled by r2 and slopeusing linear regression analysis (Table S3). This analysis revealedthat the data collected from germinating seeds at 22�C (experiment110 in Table 4) were not reproducible. Therefore, the data shouldnot be treated as replicates.

Data analysis

The signal values obtained using MAS5 were transformed to a log2

scale and further analyzed using R (http://www.r-project.org). Thehormone-inducible genes (low-stringency list; Table S2) weredetected as follows. The genes that were significantly expressed(detection level P < 0.05) in at least one of the basic hormonetreatments (60 GeneChips) were used for the subsequent analysis(18 775 in total), and were analyzed using Welch’s t-test for eachhormone treatment at 30 min, 1 h and 3 h. The data were furtherfiltered based on the FDR (q value < 0.1; Storey and Tibshirani,2003). The genes were then classified as up or downregulated ateach time point and analyzed in a pairwise manner using Fisher’sexact test (threshold P = 0.001). Comparisons of data at the 3 h timepoint were excluded from this analysis, as we observed conflicting

AtGenExpress hormone data 539

ª 2008 The AuthorsJournal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 55, 526–542

relationships that were probably caused by secondary responses.To create the high-stringency gene list (Table S5), the followinganalysis and thresholds were applied to each hormone. The genesthat were significantly expressed (detection level P < 0.05) in at leastone of 12 GeneChip experiments (including mock treatment) wereincluded in the analysis. Then, four groups of data were analyzedusing standard one-way ANOVA: mock, 30 min, 1 h and 3 h. Thegenes were then filtered by their FDR q values (<0.05) and furtherfiltered based on the absolute value of their signal ratios (SR). Thethreshold SR and recovered gene number for each hormone wereas follows: ABA, SR = 2.25, n = 472; ACC, SR = 0.25, n = 41; BL,SR = 2.25, n = 41; CK, SR = 0.25, n = 143; GA, SR = 0, n = 49; IAA,SR = 1.25, n = 143; MJ, SR = 0.75, n = 758. Our conditions (n > 40)correspond to a Pearson’s correlation coefficient of 0.4 (P < 0.01).

Global hierarchical clustering

Data from all or a proportion of the following groups were includedin our analysis. Blaesing (diurnal), the Nottingham ArabidopsisStock Centre (light and pathogen), RIKEN: Shimada’s group (basichormone treatments, inhibitors, etc), RIKEN: Kamiya’s group (seedgermination, imbibition, ABA, low temperature, GA experiments),Mizuno and Sakakibara’s group (zeatin and ARRs), RIKEN: Takah-ashi’s group (sulfur starvation), the German Resource Centre forGenome Research (stress series), Scheible’s group (nitrogen star-vation), Weigel’s group (floral transition), Weigel’s group (organsand development). The data from the Nottingham ArabidopsisStock Centre, the German Resource Centre for Genome Researchand Weigel’s group were subdivided into smaller groups. TheNottingham Arabidopsis Stock Centre data were divided into a lightseries and pathogen series. The data from the German ResourceCentre for Genome Research were divided into three parts: rootexperiments, shoot experiments, and cell-culture experiments.Weigel’s organ-development experiments were divided into thefollowing three subgroups: floral stage 12, shoot apices, and other.All signal values were imported into GeneSpring (Agilent/Silicon-Genetics) and normalized per chip, assuming that the signal was atleast 10 (signals lower than 10 were corrected to 10). Per-genenormalization was also applied in the above 15 experimental sub-groups. The expression levels of each gene were normalized to theirown median as a synthetic positive control. The data sets were thenimported into Cluster 3 (de Hoon et al., 2004), transformed to a log2

scale. The genes and arrays were clustered using centroid linkage.Similarity metrics were defined using Pearson’s correlation.

Acknowledgements

We thank the Arabidopsis Information Resource for providing theAtGenExpress web site, and Ms Chitose Takahashi, Mr Hiroaki Yuasaand Mr Narumasa Miyauchi for technical assistance with data pro-cessing, and construction and the maintenance of the web system.We thank Dr Thomas Altmann (University of Potsdam) for coordi-nating the AtGenExpress project, Dr Alok Saldanha (StanfordUniversity) for providing JAVA Treeview, the RIKEN Super Com-bined Cluster (RSCC) for supplying computational resources, andthe data visualization service of Advanced Center for Computing andCommunication at RIKEN for assisting with data visualization.

Supplementary Material

The following supplementary material is available for this articleonline:

Figure S1. The number of significantly expressed genes (detectionlevel P < 0.05) from each GeneChip.Figure S2. Genome-wide hierarchical cluster analysis using AtGen-Express data.Figure S3. Cluster Cutting, a web-based co-expression/expressionviewer from RIKEN PRIMe.Table S1. Significantly expressed genes (detection level P < 0.05)from the hormone series data.Table S2. Hormone-inducible genes (low stringency).Table S3. Assessment of the replicated experiments using linearregression analysis.Table S4. Overlapping transcriptional targets in response tohormone treatment.Table S5. Hormone-inducible genes (high stringency).This material is available as part of the online article from http://www.blackwell-synergy.com.Please note: Blackwell publishing are not responsible for the contentor functionality of any supplementary materials supplied by theauthors. Any queries (other than missing material) should bedirected to the corresponding author for the article.

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