Research
Flowering time and transcriptome variation in Capsella
bursa-pastoris (Brassicaceae)
Hui-Run Huang1,2, Peng-Cheng Yan3, Martin Lascoux4,5 and Xue-Jun Ge1
1
Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, the Chinese Academy of Sciences, Guangzhou 510650, China; 2State Key
Laboratory of Systematic and Evolutionary Botany, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China; 3MOE Key Laboratory for Biodiversity Science and Ecological
Engineering and College of Life Sciences, Beijing Normal University, Beijing 100875, China; 4Department of Ecology and Genetics, Evolutionary Biology Centre, Uppsala University, SE-752
36 Uppsala, Sweden; 5Laboratory of Evolutionary Genomics, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, the Chinese Academy of
Sciences, Shanghai, China
Summary
Author for correspondence:
Xue-Jun Ge
Tel: +86 20 3725 2551
Email: [email protected]
Received: 24 November 2011
Accepted: 4 February 2012
New Phytologist (2012) 194: 676–689
doi: 10.1111/j.1469-8137.2012.04101.x
Key words: adaptation, Capsella
bursa-pastoris, circadian rhythm, flowering
time, gene expression.
• Flowering is a major developmental transition and its timing in relation to environmental
conditions is of crucial importance to plant fitness. Understanding the genetic basis of flowering
time variation is important to determining how plants adapt locally.
• Here, we investigated flowering time variation of Capsella bursa-pastoris collected from
different latitudes in China. We also used a digital gene expression (DGE) system to generate
partial gene expression profiles for 12 selected samples.
• We found that flowering time was highly variable and most strongly correlated with day
length and winter temperature. Significant differences in gene expression between early- and
late-flowering samples were detected for 72 candidate genes for flowering time. Genes
related to circadian rhythms were significantly overrepresented among the differentially
expressed genes.
• Our data suggest that circadian rhythms and circadian clock genes play an important role in
the evolution of flowering time, and C. bursa-pastoris plants exhibit expression differences
for candidate genes likely to affect flowering time across the broad range of environments
they face in China.
Introduction
Flowering time is an important fitness trait for species with short
life cycles, in that flowering at the wrong time can result in the
failure of a plant to reproduce. Thus, geographically widespread
plant species often show extensive variation in flowering time
(Riihimaki et al., 2005; Franke et al., 2006; Matsuoka et al.,
2008), exhibiting genetically based clines for flowering time
along latitudinal and ⁄ or altitudinal gradients, for example
Solidago spp. (Weber & Schmid, 1998), Arabidopsis thaliana
(Stinchcombe et al., 2004; Montesinos-Navarro et al., 2011),
and Lythrum salicaria (Montague et al., 2008). Understanding
the genetic mechanisms controlling flowering time, especially
identifying the main genes responsible for natural variation in
flowering time between different populations, is clearly important
in determining how plants adapt locally and are able to reproduce
over a wide range of latitudes and altitudes.
In the model plant A. thaliana, genes belonging to four main
pathways (the vernalization, autonomous, light-dependent, and
GA pathways) are involved in the control of flowering time
(Mouradov et al., 2002; Amasino, 2010). Several flowering time
pathways (e.g. vernalization and autonomous) converge on
676 New Phytologist (2012) 194: 676–689
www.newphytologist.com
FLOWERING LOCUS C (FLC), a MADS-box transcription regulator that represses flowering (Crevillen & Dean, 2010). FLC
expression levels are correlated with flowering time (Michaels
et al., 2003; Lempe et al., 2005), and a flowering time quantitative
trait locus (QTL) cluster has been found in a region including
the locations of the entire FLC clade of transcription factor
genes (Salomé et al., 2011). FLC and its activator FRIGIDA
(Johanson et al., 2000; Choi et al., 2011) have been shown to be
major determinants of flowering time variation in A. thaliana
raised under experimental conditions (Le Corre et al., 2002;
Michaels et al., 2003; Shindo et al., 2005), although circadian
clock genes may possibly play an even more important part under
natural conditions. Indeed, several circadian clock-related genes,
such as CIRCADIAN CLOCK ASSOCIATED 1 (CCA1),
TIMING OF CAB EXPRESSION 1 (TOC1), CYCLING DOF
FACTOR 3 (CDF3) and CONSTANS-LIKE 1 (COL1), were
detected in an association mapping study of flowering time in
A. thaliana (Brachi et al., 2010). Gene expression variation in
the light-dependent pathway has been suggested to correlate with
photoperiodic flowering in nonmodel species, such as soybean
cultivars (Zhang et al., 2008) and common sunflower (Blackman
et al., 2011). Although less is known about the control of
2012 The Authors
New Phytologist 2012 New Phytologist Trust
New
Phytologist
flowering time in other plant species, the available data suggest
that the same pathways are involved, although individual genes
might have different importance (e.g. Lagercrantz, 2009).
Capsella bursa-pastoris is a herbaceous, predominantly selfing,
tetraploid plant, notable for its wide geographical distribution
(Neuffer et al., 2011). Capsella is a small genus that contains only
three species: the tetraploid C. bursa-pastoris and the two diploids
Capsella rubella and Capsella grandiflora. C. bursa-pastoris and
C. rubella are selfers, while C. grandiflora is an outcrosser. A
recent study indicates that C. bursa-pastoris is an autopolyploid
of C. grandiflora (St. Onge et al., 2012). Because Capsella is one
of the most closely related genera to A. thaliana (German et al.,
2009; Franzke et al., 2011), it is easy to transfer molecular
genetic resources developed for A. thaliana to C. bursa-pastoris.
There are both winter-annual (late-flowering) ecotypes and
summer-annual (early-flowering) ecotypes in China, and investigations in Europe and America have revealed significant
geographical differences in flowering time in the species (Neuffer
et al., 2011). For example, ecotypes in Scandinavia and southern
Spain flower early, whereas ecotypes from intermediate latitudes
flower late (Neuffer & Hurka, 1986; Neuffer & Bartelheim,
1989; Neuffer & Hoffrogge, 2000). Besides, C. bursa-pastoris
showed clinal differentiation in flowering time along a 2500 km
latitudinal transect in Russia: the most northern and most southern provenances flowered earlier than intermediate provenances
(Neuffer, 2011). Flowering time is delayed with altitude in alpine
climates in the Alps in Europe (Neuffer & Hurka, 1986) and the
Sierra Nevada in North America (Neuffer & Hurka, 1999),
whereas populations at high elevations in subarctic regions such
as Norway (Neuffer & Hurka, 1986) and at locations where
summers are hot and dry, as in southern Spain, flower early
(Neuffer & Hoffrogge, 2000).
The evolutionary history of C. bursa-pastoris may have contributed to the population differentiation of flowering time.
C. bursa-pastoris is believed to have originated in the eastern
Mediterranean region, and subsequently spread westwards to
Europe where introgressive hybridization with diploid C. rubella
took place, and eastwards to Asia where C. rubella does not grow
(Hurka & Neuffer, 1997; Ceplitis et al., 2005; Slotte et al.,
2008). C. bursa-pastoris was very recently introduced to North
America by European settlers, and variation patterns of flowering
time there can be traced back to the introduction of preadapted
genotypes (Neuffer & Hurka, 1999). Worldwide surveys of
nuclear ⁄ chloroplast genetic diversity in C. bursa-pastoris have
revealed limited variation within the species and suggest that it
recently went through a rapid expansion such that ecotypic differentiation of flowering time is likely to have evolved recently
(Ceplitis et al., 2005; Slotte et al., 2006, 2008). The latter is
likely to be true, particularly for the species in China where the
species has been shown to exhibit much lower nuclear genetic
diversity and a less pronounced genetic structure than in Europe
(Slotte et al., 2008, 2009). Although Chinese C. bursa-pastoris is
derived from European material that spread to eastern Eurasia
relatively recently (21–64 ka) (Slotte et al., 2008), the species is
today widely distributed across China occurring in a broad array
of complex environmental conditions ranging from subtropical
2012 The Authors
New Phytologist 2012 New Phytologist Trust
Research 677
climatic conditions in the south to more extreme environments
in the northwest and northeast. Chinese C. bursa-pastoris can
thus be used as an independent replicate to studies carried out in
the European part of the species range to address questions about
the importance of shared ancestry and parallel evolution in the
development of flowering time clines in different parts of the
world.
In Capsella, Linde et al. (2001) found that three major QTLs
accounted for onset of flowering, and this was the first evidence
of the multigenic control on this trait. Later studies have suggested
that there are both differences and similarities in the genetic
control of flowering time in the European and Chinese parts of
the natural range. An association study between flowering time
and sequence polymorphism at FLC, FRIGIDA, CRYPTOCHROME 1 (CRY1) and LUMINIDEPENDENS (LD) revealed
that single nucleotide polymorphisms (SNPs) at CRY1 and FLC
were significantly associated with flowering time variation in
western Eurasia, whereas in China CRY1 was monomorphic and
a different SNP at FLC was significantly associated with flowering time variation (Slotte et al., 2009). Other flowering time
genes, such as FRIGIDA, CRY1 and LD, were either monomorphic or exhibited almost no sequence variation in the Chinese
plants studied, despite notable variation in flowering time (Slotte
et al., 2009). On the other hand, Slotte et al. (2007) found good
agreement of flowering time gene expression differences in comparisons between two pairs of accessions, one pair comprising an
early-flowering accession from Taiwan and a late-flowering accession from northern Europe, and the other comprising both earlyand late-flowering accessions from California. They noted that
this could indicate that the genetic basis of expression differences
is shared by common ancestry, or that similar regulatory differences have evolved in parallel. Interestingly there were many key
circadian clock genes among the genes that were differentially
expressed between early- and late-flowering accessions. Further
analysis of gene expression differences among different flowering
C. bursa-pastoris ecotypes from a broad array of environmental
conditions in China would help to clarify the situation further.
In the present study, we broadened the analysis of flowering
time variation in C. bursa-pastoris to samples collected from
multiple environments in China. We also constructed gene
expression profiles of 12 different samples representing extremes
of flowering time using the Solexa ⁄ Illumina’s Digital Gene
Expression (DGE) system. The DGE system allows an examination of variation in gene expression across many genes at the same
time and has been successfully applied to studies of gene expression in different animal species (Harhay et al., 2010; Veitch
et al., 2010; Pemberton et al., 2011), including transcriptome
response to virus infection (Hegedus et al., 2009; Basu et al.,
2011). A few studies have also used DGE to look at gene expression differences in plant species (e.g. maize (Eveland et al., 2010)
and cucumber (Qi et al., 2012)). The aims of the study, therefore, were to examine the pattern of flowering time variation for
C. bursa-pastoris in China; to examine further whether circadian
clock genes are strong candidates for the evolution of adaptive
flowering time variation as indicated previously (Slotte et al.,
2007; K. Holm et al., unpublished); and to assess whether
New Phytologist (2012) 194: 676–689
www.newphytologist.com
New
Phytologist
678 Research
C. bursa-pastoris plants exhibit different patterns of genetic
variation from one another for candidate genes likely to affect
flowering time across the broad range of environments they face
in China.
Materials and Methods
Sample collection and growth chamber experiment
Seeds were collected from 37 populations of C. bursa-pastoris (L.)
Medic., representing most of the species’ distribution range in
China (Fig. 1, Supporting Information Table S1). The seed
samples within populations were collected at intervals of a few m
apart to avoid the collection of the same individual. Geographical
coordinates of populations were determined using GPS. These
populations are distributed in a broad range of environmental
conditions, from subtropical to temperate ⁄ alpine. Data on mean
temperature of the coldest quarter of a year (MTCQ) and annual
precipitation (AP) of each population were extracted from
the software DIVA-GIS (http://www.diva-gis.org/) based on
the WORLDCLIM data set (resolution 2.5 min, Hijmans
et al., 2005). Data on day length of each population on 22
June 2011 were obtained from the Era Shuttle Calendar
(http://www.agr.cn/Calendar.htm). All of these data are presented
in Table S1.
Seeds were stratified on moist filter paper for 4 d at 4C and
subsequently transferred to soil in pots, which were fully
randomized in a growth chamber at 23C with a 16 : 8 h light :
dark regime. Flowering time was calculated as the number of d
from germination to flowering. A nonparametric test, H-test of
Kruskal & Wallis (1952), was performed to analyze the total variance of flowering time. For brevity, geographical data (latitude,
longitude, and altitude), MTCQ, AP and day length are referred
to as environmental factors in further analyses. To solve the problem of high correlations among some environmental factors, for
example, day length is highly correlated with latitude (r = 0.99,
P < 0.0001), principal component regression was used to investigate the relationships between flowering time and the environmental factors: first, a principal component analysis was used to
project the factors onto a lower dimensional space; second, a
regression analysis was performed using the first two principal
components as the independent variables and flowering time as
the response variable; and finally, the parameters of the regression
model were computed for the environmental factors according to
the coefficients of the principal components. The R package
(http://www.r-project.org/) was used for these analyses.
3¢ tag DGE
Eleven populations were chosen for the DGE experiment (Fig. 1,
Table S1). According to the records of flowering time, three individuals with similar flowering time were chosen to represent a
sample for each selected population; only for population 32 two
samples (F and L) were used. We estimated the mean flowering
Fig. 1 Distribution of the 37 analyzed Capsella bursa-pastoris populations in China. The populations are presented in numerical order, and the capital
letters in brackets indicate the samples used in the digital gene expression (DGE) analysis. The dashed line indicates the division between the northwest and
east populations. The northwest has high plateaus with very cold winters and the lowest annual rainfall in the country. The elevations of all northwest
populations are > 400 m, but the elevations of the east populations are < 200 m (except for populations 2 and 3).
New Phytologist (2012) 194: 676–689
www.newphytologist.com
2012 The Authors
New Phytologist 2012 New Phytologist Trust
New
Phytologist
time of the three individuals for each sample as follows: A (30 d),
B (31 d), C (40 d), D (38 d), E (35 d), F (39 d), G (129 d), H
(93 d), I (95 d), J (107 d), K (94 d) and L (78 d); accordingly
we divided these samples into two groups: early-flowering (A, B,
C, D, E, F) and late-flowering (G, H, I, J, K, L). Seeds of each
individual were stored at 4C for 4 d in distilled water to break
seed dormancy, and then sterilized using 75% ethanol and 0.1%
HgCl2. For each individual the surface-sterilized seeds were sown
on an 0.8% agar plate with Murashige and Skoog medium
(Murashige & Skoog, 1962), and subsequently the plates were
placed randomly under long-day conditions as stated earlier (16 :
8 h photoperiod, 23C). Two-week-old seedlings were sampled
and immediately flash-frozen in liquid nitrogen. We measured
gene expression in seedlings because previous studies have shown
that several flowering time regulators are expressed at a very early
stage in C. bursa-pastoris (Slotte et al., 2007) and A. thaliana
(Keurentjes et al., 2007). Sampling took place 7 h after dawn.
For each sample, seedlings of a similar size were chosen, and
equal numbers of seedlings for each individual were pooled
together for RNA extraction. RNA was extracted from frozen
seedlings using RNAiso Plus and RNAiso-mate for Plant Tissue
(TaKaRa, Dalian, China) according to the manufacturer’s
protocols. RNA integrity and concentration were evaluated on an
Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara,
CA, USA). In total, 12 RNA libraries were constructed.
Approximately 6 lg of RNA representing each library was run
on an Illumina Genome Analyzer for sequencing (Beijing
Genome Institute, Shenzhen, China). Tag library preparation
was performed using Tag Profiling for NlaIII Sample Prep kit
(Illumina, San Diego, CA, USA) according to the manufacturer’s
instructions. In brief, total RNA was incubated with magnetic
oligo(dT) beads to capture mRNA. First- and second-strand
cDNA was synthesized and bead-bound cDNA was subsequently
digested with NlaIII. GEX NlaIII adapter 1 was ligated at the site
of NlaIII cleavage. This adapter contains a restriction site for
MmeI that cuts 17 bp downstream from the NlaIII site, thus
creating 21 bp tags starting with the NlaIII recognition sequence,
CATG. After removing the 3́ fragment via magnetic bead precipitation, GEX NlaIII adapter 2 was ligated at the site of MmeI
cleavage. The adapter-ligated cDNA tags were subsequently
enriched using PCR-primers that annealed to the adapter ends.
The amplified and purified tags were then sequenced on an
Illumina Genome Analyzer according to the manufacturer’s
protocols. An Illumina pipeline was used for off-instrument data
processing, including image analysis, base calling, extraction of
17 bp tags and tag counting. After filtering adaptor tags,
low-quality tags and tags of copy number = 1, we classified the
remaining tags (clean tags) according to their copy number in the
library. The tag sequences and counts have been submitted to
Gene Expression Omnibus (GEO) under series GSE28624.
All tags were mapped to the A. thaliana genome TAIR9
released in The Arabidopsis Information Resource
(http://www.arabidopsis.org/index.jsp), the most closely related
fully annotated genome available to C. bursa-pastoris, using
MAQ program, ver. 0.7.1 (Li et al., 2008), allowing for a 2 bp
mismatch between the tags and the references. Tags that were
2012 The Authors
New Phytologist 2012 New Phytologist Trust
Research 679
generated with a poor mapping quality score (< 30) were
removed from further analysis. Gene expression levels were represented by high-quality tag count numbers. When there were
multiple types of tags aligned to different locations of the same
gene, the gene expression levels were represented by the sum of
all. Tags that mapped to multiple genomic locations were
excluded from further analyses. Differential gene expression of
36 early vs late pairwise comparisons based on the 12 libraries
was calculated. The Bioconductor package edgeR (Robinson
et al., 2010) was used for gene normalization and statistical
comparisons. The edgeR package uses empirical Bayes estimation
and exact tests based on the negative binomial distribution to
provide P-values associated with changes in expression between
samples (Robinson & Smyth, 2007, 2008). The false discovery
rate (FDR) was estimated to determine the threshold of P-value
in multiple tests (Benjamini & Hochberg, 1995). Our significance threshold for differential expression was P < 0.05 after
correction using FDR of 1%.
We assembled a list of 298 candidate genes involved in flowering regulation in A. thaliana (Mouradov et al., 2002; Slotte
et al., 2007), most of which have a gene ontology biological
process annotation that contained the terms ‘circadian rhythm’,
‘flower development’, ‘vegetative to reproductive phase transition’, ‘photoperiod’, ‘vernalization response’, or ‘gibberellic acid’
(Table S2). We tested for statistical overrepresentation of differentially expressed genes in the above six categories, using Fisher’s
exact test with P < 0.05 as the threshold to judge the significance
of overrepresentation. We used complete linkage with Euclidean
distance to generate the dendrograms of the 36 pairwise comparisons and the significant flowering time genes, based on the differential expression of these genes; up- and down-regulation of
expression levels in late-flowering samples were represented by ‘1’
and ‘–1¢, and nondifferential expression were represented by ‘0’
in the clustering matrix. The software Genesis (Sturn et al.,
2002) was used for clustering. Based on the clustering pattern, we
then justified the clusters of the pairwise comparisons according to
the sample sources, that is, cluster x contains the comparisons
between the sample named x and other samples.
Results
Phenotypic variation in flowering time
Descriptive statistics for flowering time of each population
are presented in Table S1. Flowering time in Chinese
C. bursa-pastoris varied from 23 to > 200 d across the 387 individuals and 37 populations examined under specific long-day
conditions (Fig. 2). A Kruskal–Wallis test showed that flowering
time variation among all studied populations was significant
(v2 = 255.9194, P < 0.001).
The proportion of variance and the loadings of each component in the principal component analysis are shown in Table S3.
The first principal component (PC1), which explained 68% of
the variation, was most strongly associated with MTCQ, AP, latitude and day length. The second principal component (PC2),
which explained 26% of the variation, was mainly associated with
New Phytologist (2012) 194: 676–689
www.newphytologist.com
New
Phytologist
680 Research
Fig. 2 Boxplot of flowering time of the 37 Capsella bursa-pastoris populations. These boxes are presented according to the latitudes. In each box, the
central mark is the median, the edges of the box represent the 25th and 75th percentiles (the lower and upper quartiles), the ends of the whiskers represent
the lowest datum within 1.5 · interquartile range (IQR) of the lower quartile and the highest datum within 1.5 · IQR of the upper quartile, and outliers are
plotted individually.
longitude and altitude. A PCA plot is presented in Fig. 3,
showing the similarities in environmental factors between the
populations. The regression analysis was carried out with PC1
Fig. 3 Principal component analysis of the environmental factors of the
Capsella bursa-pastoris populations studied. Component 1 explains 68%
of the variability, while component 2 explains 26%. The populations in the
open circle represent the northwest populations. LAT, latitude; LONG,
longitude; ALT, altitude; MTCQ, mean temperature of the coldest quarter;
AP, annual precipitation; DL, day length.
New Phytologist (2012) 194: 676–689
www.newphytologist.com
and PC2 as independent variables and flowering time as response
variable, and addressed the effects of the environmental factors
on flowering time (Table 1). Flowering time was most strongly
influenced by day length, and was moderately associated with
latitude, MTCQ and longitude. Although the regression analysis
suggested a positive relationship between latitude of origin and
flowering time among all studied populations, some populations
in northwest China flowered very early although they are located
in high-latitude regions (e.g. populations 26, 28; Fig. 2). These
populations are also from high altitudes, which separate them
from most of the other populations (Fig. 3, Table S1). The
northwest has high plateaus with very cold winters and the lowest
annual rainfall in the country. To investigate the local pattern of
flowering time diversification in this area, we characterized the
populations located in the range with latitude > 3350¢N and
longitude < 11240¢E as a group in the regression analysis (hereafter called northwest populations, for brevity) (Fig. 1). A regression analysis was also done for the remaining populations (east
populations). We found that the regression pattern of the east
populations was quite similar to that of the overall populations.
However, the pattern in the northwest was somewhat different
from the overall or east populations, with a contrasting relationship between flowering time and MTCQ and AP. Flowering
time decreased with MTCQ and AP in the east populations, but
increased with MTCQ and AP in the northwest populations.
Digital gene expression
The major characteristics of the 12 DGE libraries are summarized in Table 2. After filtering adapter tags, low-quality tags,
2012 The Authors
New Phytologist 2012 New Phytologist Trust
New
Phytologist
Research 681
Table 1 Coefficients of the regression models for overall, east and
northwest populations of Capsella bursa-pastoris, using environmental
factors as independent variables and flowering time as response variable
Environmental factors
Latitude Longitude Altitude Day length MTCQ AP
Overall
0.651
East
0.511
Northwest 0.474
0.336
0.476
0.813
)0.002
)0.003
)0.017
)0.409 )0.004
)0.479 )0.005
1.132 0.005
5.975
4.653
4.133
MTCQ, mean temperature of the coldest quarter; AP, annual
precipitation.
and tags of copy number = 1, there were c. 42.8 million clean
tags in all libraries, with the total number of clean tags per library
ranging from 3.3 to 3.8 million and the number of distinct clean
tag sequences ranging from 63 595 to 95 541. The G library had
the highest number of total sequence tags (3 742 840), while the
I library had the highest number of distinct sequence tags (95
541). In all libraries high-expression tags with copy numbers
> 100 were in absolute dominance (> 70%), whereas lowexpression tags with copy numbers < 10 occupied the majority of
distinct tag distributions (> 68%) (Fig. S1). There were 11 283
tag-mapped genes in the 12 libraries. The dynamic range of
DGE spanned five orders of magnitude. The most abundant
transcript in all 12 libraries was that for AT2G26010, a gene
predicted to encode a pathogenesis-related protein, with a
maximum tag count in the A library of 64 892 tags. However,
the tag counts for the majority of genes were low in all libraries.
Between 12.67 and 16.09% of distinct clean tags were mapped
to the Arabidopsis database and the ratio of number of
tag-mapped genes to reference genes per library ranged from
15.73 to 19.85% (Table 2).
Approximately 1000–2500 differentially expressed genes were
found in the 36 early vs late comparisons under specific long-day
conditions. Seventy-two genes listed in the prior flowering time
gene list were found to be differentially expressed in at least one
of the 36 pairwise comparisons (hereafter called ‘candidate genes
for flowering time’; Fig. 4, Table 3). The ratio of number of
candidate genes to the tag-mapped genes in the prior list for
each comparison ranged from 13.21% (D vs G) to 33.63%
(B vs G). The expression differences of flowering time genes
within the same population (F vs L) were among the smallest,
with a ratio of 13.27%. Interestingly, the Fisher’s exact test
showed a significant overrepresentation of differentially
expressed genes only in the category ‘circadian rhythm’ (circadian rhythm, P = 0.032; flower development, P = 1; vegetative
to reproductive phase transition, P = 0.785; photoperiod,
P = 0.068; vernalization response, P = 0.213; gibberellic acid,
P = 0.868). These genes were ARR4, LWD1, GI, FKF1,
MPK7, CCA1, CRY1, PRR7, CHE, PRR5, ZTL, and TOC1,
which are part of, or closely related to, the circadian clock.
Unexpectedly, the expression of FLC, which is involved in the
convergence of the autonomous and vernalization pathways in
Arabidopsis, was not detected in this study.
Based on the expression differences of these candidate genes,
the 36 pairwise comparisons between early- and late-flowering
samples could be grouped into six clusters: A, B, C, E and F, I
and D (Fig. 4). Each cluster contained several comparisons comprising one to two specific early-flowering samples (with the
exception of cluster I), suggesting that there were diverse regulatory patterns of flowering time among the early-flowering
samples, apart from cluster I. We divided the candidate genes for
flowering time into two clusters (a and b, Fig. 4). Half the genes
in cluster b, such as CHE, UBC2, CRY1, TOC1 and SEN4, in
contrast to genes in cluster a (13.2%), exhibited differential
expression in at least half of the comparisons between early- and
late-flowering samples. Unidirectional regulation in all differentially expressed comparisons only occurred in seven genes (CCA1,
FKF1, ZTL, RGL2, RGL3, VIP5, and AIM1). All of these genes
were up-regulated in late-flowering samples. The evening-phased
clock genes, such as TOC1, CHE, and GI, were expressed differentially in many comparisons (69.4, 77.8, and 50% of comparisons, respectively), but the morning-phased clock genes, such as
CCA1 and PRR7, were expressed differentially only in 16.7 and
11.1% of comparisons, respectively. However, ZTL, a gene
Table 2 Basic characteristics of the 12 digital gene expression (DGE) libraries
Raw tag count
Clean tag count
Tags mapped to genes
Libraries
Total
Distinct
Total
Distinct
Total
% of total
clean tags
Distinct
% of distinct
clean tags
Number
% of reference
genes
A
B
C
D
E
F
G
H
I
J
K
L
3585496
3740053
3504123
3822115
3726679
3617956
3836892
3601474
3656621
3536171
3736730
3744683
171544
193809
179774
169586
126115
167299
161056
177992
208585
181963
182827
147805
3457671
3606068
3385247
3726373
3633975
3520072
3742840
3490017
3536294
3427875
3624568
3661334
71397
87861
86678
82386
63595
77623
75556
74353
95541
81185
78732
73105
524887
647916
612121
601474
653263
677547
684991
544730
594573
609477
595565
690463
15.20
17.97
18.08
16.14
17.98
19.24
18.30
15.61
16.81
17.78
16.43
18.86
9410
11272
13123
11846
9785
12304
11308
10237
12105
12277
11075
11761
13.18
12.83
15.14
14.38
15.39
15.85
14.97
13.77
12.67
15.12
14.07
16.09
5972
6667
7291
6820
5779
6827
6317
6135
7022
6924
6290
6471
16.26
18.15
19.85
18.57
15.73
18.59
17.20
16.70
19.12
18.85
17.12
17.62
2012 The Authors
New Phytologist 2012 New Phytologist Trust
All tag-mapped genes
New Phytologist (2012) 194: 676–689
www.newphytologist.com
New
Phytologist
682 Research
Fig. 4 The regulatory pattern of the 72 candidate genes for flowering time in Capsella bursa-pastoris. Each column represents expression differences of all
candidate genes in a pairwise comparison between early- and late-flowering samples; each row represents expression differences for one candidate gene in
all comparisons between early- and late-flowering samples; red and green, genes were up- and down-regulated in late-flowering samples, respectively;
black, no differences of gene expression in the comparison. Based on the expression differences of these candidate genes, the 36 comparisons were divided
into six clusters (A, B, C, E and F, I, D). Each cluster contains the comparisons comprising one to two specific samples, for example, cluster A contains the
comparisons of sample A vs other samples; the 72 candidate genes were divided into two large clusters (a and b).
encoding a protein that interacts with GI to degrade TOC1 in
the dark, did not show great difference in contrast to other
evening-phased genes (8.3% of comparisons). Apart from some
evening-phased clock genes, several genes related to the circadian
rhythm, for example, ARR4, LWD1, and CRY1, were also
expressed differentially in many comparisons (ARR4, 47.2%;
LWD1, 63.9%; CRY1, 72.2%).
New Phytologist (2012) 194: 676–689
www.newphytologist.com
Discussion
Variation in flowering time
In the present study, as in previous ones (Neuffer, 1990; Hurka
& Neuffer, 1997; Ceplitis et al., 2005; Slotte et al., 2009), it was
found that flowering time is highly variable among
2012 The Authors
New Phytologist 2012 New Phytologist Trust
New
Phytologist
Research 683
Table 3 List of candidate genes for flowering time in the digital gene expression (DGE) analysis
Gene number
Gene name
Gene ontology biological process
Reference
AT1G06180
AT1G10470
AT1G12910
AT1G13260
AT1G14920
AT1G22690
AT1G22770
AT1G25560
AT1G26830
AT1G61040
AT1G68050
AT1G68480
AT1G68840
AT1G69490
AT1G69570
AT1G74840
AT2G01570
AT2G02760
AT2G14900
AT2G16720
AT2G18170
AT2G19520
AT2G26300
AT2G34720
AT2G36830
AT2G42200
AT2G46830
AT3G02380
AT3G02885
AT3G03450
AT3G04610
AT3G12810
AT3G15354
AT3G26640
AT3G28910
AT3G33520
AT3G44880
AT3G47500
AT3G48590
AT3G49600
AT3G50060
AT3G55730
AT3G63010
AT4G02440
AT4G08920
AT4G14540
AT4G16250
AT4G24540
MYB13
ARR4
LWD1
RAV1
GAI
AT1G22690
GI
TEM1
CUL3
VIP5
FKF1
JAG
TEM2
NAP
CDF5
AT1G74840
RGA
UBC2
AT2G14900
MYB7
MPK7
FVE
GPA1
NF-YA4
AT2G36830
SPL9
CCA1
COL2
GASA5
RGL2
FLK
PIE1
SPA3
LWD2
MYB30
ESD1
ACD1
CDF3
NF-YC1
UBP26
MYB77
MYB109
GID1B
EID1
CRY1
NF-YB3
PHYD
AGL24
Gibberellic acid
Circadian rhythm
Circadian rhythm
Flower development
Gibberellic acid
Gibberellic acid
Circadian rhythm, flower development
Photoperiod
Flower development
Flower development
Circadian rhythm, flower development
Flower development
Kirik et al. (1998)
Salomé et al. (2006)
Wu et al. (2008)
Hu et al. (2004)
Dill & Sun (2001)
AT4G29010
AT4G30270
AT4G32551
AT4G32980
AT4G36920
AT4G38620
AT4G39400
AT5G02030
AT5G02810
AT5G08330
AT5G13480
AT5G17490
AIM1
SEN4
LUG
ATH1
AP2
MYB4
BRI1
PNY
PRR7
CHE
FY
RGL3
2012 The Authors
New Phytologist 2012 New Phytologist Trust
Flower development
Gibberellic acid
Gibberellic acid
Vegetative to reproductive phase transition
Gibberellic acid
Gibberellic acid
Circadian rhythm
Flower development
Gibberellic acid
Vegetative to reproductive phase transition
Gibberellic acid
Vegetative to reproductive phase transition
Circadian rhythm, gibberellic acid
Flower development
Gibberellic acid
Gibberellic acid
Flower development
Flower development
Photoperiod
Gibberellic acid
Flower development
Flower development
Gibberellic acid
Gibberellic acid
Gibberellic acid
Photoperiod
Circadian rhythm
Vegetative to reproductive phase transition,
vernalization response, gibberellic acid
Flower development
Gibberellic acid
Flower development
Gibberellic acid
Flower development
Gibberellic acid
Flower development
Vegetative to reproductive phase transition
Circadian rhythm
Circadian rhythm
Flower development
Gibberellic acid
Mizoguchi et al. (2005)
Castillejo & Pelaz (2008)
Dieterle et al. (2005)
Oh et al. (2004)
Imaizumi et al. (2003)
Dinneny et al. (2004)
Castillejo & Pelaz (2008)
Sablowski & Meyerowitz (1998)
Fornara et al. (2009)
Chen et al. (2006)
Dill & Sun (2001)
Xu et al. (2009)
Li & Parish (1995)
Rao et al. (2009)
Pazhouhandeh et al. (2011)
Ullah et al. (2002)
Wenkel et al. (2006)
Wang et al. (2009)
Alabadi et al. (2001)
Ledger et al. (2001)
Zhang et al. (2009)
Tyler et al. (2004)
Lim et al. (2004)
Noh & Amasino (2003)
Laubinger et al. (2006)
Wu et al. (2008)
Vailleau et al. (2002)
Martin-Trillo et al. (2006)
Pruzinska et al. (2003)
Fornara et al. (2009)
Wenkel et al. (2006)
Schmitz et al. (2009)
Shin et al. (2007)
Chen et al. (2006)
Griffiths et al. (2006)
Dieterle et al. (2001)
Lin (2002)
Wenkel et al. (2006)
Aukerman et al. (1997)
Yu et al. (2002)
Richmond & Bleecker (1999)
Gan & Amasino (1997)
Liu & Meyerowitz (1995)
Proveniers et al. (2007)
Jofuku et al. (1994)
Hemm et al. (2001)
Domagalska et al. (2007)
Kanrar et al. (2008)
Nakamichi et al. (2007)
Pruneda-Paz et al. (2009)
Simpson et al. (2003)
Tyler et al. (2004)
New Phytologist (2012) 194: 676–689
www.newphytologist.com
New
Phytologist
684 Research
Table 3 (Continued)
Gene number
Gene name
Gene ontology biological process
Reference
AT5G23150
HUA2
Doyle et al. (2005)
AT5G24470
AT5G25900
AT5G37260
AT5G46210
AT5G47390
AT5G47640
AT5G51810
AT5G57360
AT5G60120
AT5G61380
AT5G65790
PRR5
GA3
CIR1
CUL4
MQL5.25
NF-YB2
GA20ox2
ZTL
TOE2
TOC1
MYB68
Vegetative to reproductive phase
transition, flower development
Circadian rhythm
Gibberellic acid
Gibberellic acid
Flower development, photoperiod
Gibberellic acid
Flower development, gibberellic acid
Circadian rhythm, flower development
Circadian rhythm
Gibberellic acid
C. bursa-pastoris populations. There was a significant, although
relatively weak, latitudinal cline in flowering time, suggesting that
flowering time variation among Chinese populations could be
adaptive. The weakness of the cline, also observed for circadian
rhythms (K. Holm et al., unpublished), could be the result of a
high diversity of environments across China, since different patterns of correlations between flowering time and environmental
factors were suggested to exist in diverse environments (Table 1).
The weak correlation could also be the result of the young age of
the Chinese populations (Slotte et al., 2008).
Day length is not only highly correlated with flowering time
among Chinese C. bursa-pastoris, but also a key environmental
signal that affects flowering time in other temperate plant species.
For instance, strong correlations between day length and phenotypes related to development time and flowering were observed
in A. thaliana (Hancock et al., 2011). Photoperiodic control of
flowering time is also believed to affect the latitudinal distribution
of soybean (Zhang et al., 2008). Apart from day length, winter
temperature might be one of the factors shaping the flowering
time variation of C. bursa-pastoris in local environments. In the
eastern part of China, early-flowering plants were found to occur
at low latitudes with mild winters where they can have multiple
generations per yr, as recorded for other species (e.g. A. thaliana
(Le Corre et al., 2002), Beta vulgaris (VanDijk et al., 1997) and
Delphinium (Katsutani & Ikeda, 1997)). In northwest China,
early-flowering phenotypes predominate in areas with severe winters, where rapid growth is needed before the temperatures
become too low. In China the 6C isotherm of average January
temperature has been identified as the cultivation border between
single- and double-crop rice, with the latter growing in areas
above 6C; the )6C January isotherm is also the boundary
between spring and winter wheat, with spring wheat growing in
regions below –6C (Lu, 1946). Because the distributions of
C. bursa-pastoris, rice and wheat generally overlap in China, the
coincidence of generation time between C. bursa-pastoris and
crop cultivation supports the hypothesis that flowering time
difference in C. bursa-pastoris could be affected by winter temperature. For C. bursa-pastoris, it was suggested that flowering was
strongly influenced not only by temperature but also by rainfall
over a wide range of different climatic conditions (Steinmeyer
New Phytologist (2012) 194: 676–689
www.newphytologist.com
Nakamichi et al. (2007)
Helliwell et al. (1998)
Zhang et al. (2007)
Chen et al. (2010)
Ikeda & Ohme-Takagi (2009)
Wenkel et al. (2006)
Rieu et al. (2008)
Somers et al. (2000)
Aukerman & Sakai (2003)
Alabadi et al. (2001)
Chen et al. (2006)
et al., 1985); however, this study indicated that annual precipitation only had little effect on flowering time (Table 1).
Although we used seeds collected from plants grown in the
field, maternal effects probably contributed little to the
among-population differentiation and the latitudinal cline of
flowering time observed, because seeds of C. bursa-pastoris are
very small, and the differences in flowering time among
populations from large-scale geographic ranges were considerable
(23–200 d). In addition, maternal effects are usually most pronounced in early life-history stages and are less likely to account
for large trait differences that persist during later stages (Rossiter,
1996).
Candidate genes for flowering time
Only 12.67–16.09% of distinct clean tags in the DGE analysis
could be mapped to the Arabidopsis database. One explanation
for the occurrence of a large number of unknown tags could be
the c. 10 million yr of divergence and the difference in number
of chromosomes between C. bursa-pastoris and A. thaliana (Koch
et al., 2000, 2001). For example, the expression of FLC was not
detected in this study; as a rapidly evolved gene, at least in
Arabidopsis (Nah & Chen, 2010), the FLC tag of A. thaliana
may have changed in Capsella. The proportion of tags mapped to
genes were very variable between some samples (e.g. > 4%
between A and F, Table 2), which could be the result of the rapid
divergence of expression levels between the samples, as rapid
evolution on quantitative traits can occur in a few generations
given a strong selection force (Cheptou et al., 2008). Alternatively, this could be the result of isolation by geographic distance;
however, we did not detect the significant link between
geographic distance and variability of the ratios using linear
regression (R2 = 0.0002, P = 0.9177).
In this study we found a significant overrepresentation of differentially expressed genes among genes related to circadian
rhythms, in agreement with our suggestion that day length is one
of the key environmental signals affecting flowering time in
C. bursa-pastoris. The Arabidopsis clock is composed of at least
three interlocking loops (Imaizumi, 2010; Pruneda-Paz & Kay,
2010), including a morning loop formed by CCA1 ⁄ LHY and
2012 The Authors
New Phytologist 2012 New Phytologist Trust
New
Phytologist
two PSEUDO-RESPONSE REGULATORS (PRR7 and PRR9)
and an evening loop formed by TOC1 and a hypothetical clock
component Y (e.g. GI; Locke et al., 2005). Here we found that
the morning-expressed genes expressed differentially only in a
few comparisons (CCA1: 16.7%, PRR7: 11.1%) between earlyand late-flowering C. bursa-pastoris, but the evening-expressed
genes expressed differentially in many comparisons (TOC1,
69.4%; CHE, 77.8%; GI, 50%). Thus, our results suggest that
genes expressed in the evening loop are more involved in flowering time variation than those involved in the morning loop, but
the reason for this difference is unclear. Circadian rhythm was
found to be important for flowering time variation in both
C. bursa-pastoris and A. thaliana (Slotte et al., 2007; Brachi
et al., 2010; Hancock et al., 2011), suggesting a parallel evolution of similar regulatory differences in these closely related
species.
Although C. bursa-pastoris is widely distributed across China,
it does not seem too likely that the Chinese populations would
have multiple origins, because there is no population structure
among the Chinese plants (Slotte et al., 2009). Also, the results
of genotyping PHYTOCHROME B suggest that all Russian
populations belong to one group and all the Chinese populations
to another, even for those close to the Chinese border (A. Keele
& K. Holm, unpublished). Despite a likely single and recent origin of Chinese populations, the DGE analysis showed that most
genes were polymorphic with respect to the direction of expression differences among different comparisons, indicating that the
evolution of flowering time in Chinese C. bursa-pastoris could
involve different sets of genes in different regions. Sample A, B,
C, and D came from a subtropical zone (Zheng et al., 2010)
where the early-flowering plants may be favored in mild climates
or have multiple generations in 1 yr. The two high-latitude
samples, E and F, originated from a temperate zone where they
may be selected to escape severe winters or forced by the very
long photoperiod (Neuffer, 2011). E and F were grouped
together, as were the two low latitude samples A and B (Fig. 4),
indicating that populations from similar latitudes can exhibit
similar expression patterns. However, owing to the young age of
the Chinese populations, the clinal variation for flowering time
and circadian rhythm was much weaker in China than in Europe
(K. Holm et al., unpublished), and thus the cline of expression
changes may not have had time to develop as yet. For example,
sample I was not grouped with E and F, which were from similar
latitudes, but grouped with sample D, which was from a lower
latitude. The independent local adaptation by different genetic
mechanisms among early-flowering C. bursa-pastoris from different regions may represent replicated events of adaptive evolution,
that is, parallel evolution (Elmer & Meyer, 2011), and facilitate
flexible evolutionary response to changing environments across
the species range (Fournier-Level et al., 2011).
A different set of candidate genes that might affect flowering
time was found in the present DGE study relative to the previous
microarray study reported by Slotte et al. (2007). In total, a common set of 60 genes was analyzed for differential expression in
both studies (Table S4). Of these genes, 28 and 13 genes were
identified as candidate genes for flowering time in the DGE and
2012 The Authors
New Phytologist 2012 New Phytologist Trust
Research 685
microarray studies, respectively. Seven genes (CCA1, CIR1,
GA20OX2, MPK7, MQL5.25, MYB68, TOC1) were characterized as candidate genes in both studies, which is roughly what
one would expect by chance. Five of these genes (CCA1 and
GA20OX2 excepted) showed different regulatory directions
between some early vs late comparisons in the DGE study relative
to the microarray study. The use of different samples in these two
studies could be a possible reason for this difference. The samples
used in the DGE systems were drawn from many regions of
mainland China, whereas those included in the microarray
study were from Sweden, Taiwan, and the United States.
There was no overlap in the distribution of samples used in
the two studies, although the sample PL (Taiwan) likely shares
the same origin with the mainland China samples. A shared
genetic basis of expression differences in Europe and North
America, but a different one in China, is in agreement with
the evolutionary scenario that C. bursa-pastoris originated in
the eastern Mediterranean region, and subsequently spread
eastwards to Asia and westwards to Europe, but that it was
recently introduced into North America by European settlers
(Hurka & Neuffer, 1997; Neuffer & Hurka, 1999; Ceplitis
et al., 2005; Slotte et al., 2008).
Conclusions
Asian populations of C. bursa-pastoris are believed to have
recently evolved independently of those in Europe and North
America, thereby allowing us to use them as an independent
‘replicate’ when trying to understand the genetic basis of flowering
time in this species. In the present study, we found that flowering
time of C. bursa-pastoris was highly variable in China. Day length
and winter temperature were found to be key environmental
signals that affected flowering time differentiation. There was a
significant overrepresentation of differentially expressed genes in
the category ‘circadian rhythm’. We suggest that genes involved
in regulation of the circadian clock are strong candidates for the
evolution of adaptive flowering time variation in this species,
especially since some of those genes were also identified in previous experiments. Finally, C. bursa-pastoris plants exhibit expression differences for candidate genes likely to affect flowering time
across the broad range of environments they face in China.
Acknowledgements
Special thanks are given to all seed collectors. We are grateful to
Prof. Richard Abbott for his comments on the manuscript. This
work was financially supported by the National Natural Science
Foundation of China (grant no. 31000108), the National Basic
Research Program of China (973 Program) (2009CB119200),
State Key Laboratory of Systematic and Evolutionary Botany,
Institute of Botany, and Key Laboratory of Plant Resources
Conservation and Sustainable Utilization, South China Botanical
Garden, the Chinese Academy of Sciences. M.L. thanks the
Chinese Academy of Sciences (visiting professorship), the
Swedish Research Council (VR) and the Erik Philip-Sörensens
Stiftelse for support.
New Phytologist (2012) 194: 676–689
www.newphytologist.com
686 Research
References
Alabadi D, Oyama T, Yanovsky MJ, Harmon FG, Mas P, Kay SA. 2001.
Reciprocal regulation between TOC1 and LHY ⁄ CCA1 within the Arabidopsis
circadian clock. Science 293: 880–883.
Amasino R. 2010. Seasonal and developmental timing of flowering. Plant Journal
61: 1001–1013.
Aukerman MJ, Hirschfeld M, Wester L, Weaver M, Clack T, Amasino RM,
Sharrock RA. 1997. A deletion in the PHYD gene of the Arabidopsis
Wassilewskija ecotype defines a role for phytochrome D in red ⁄ far-red light
sensing. Plant Cell 9: 1317–1326.
Aukerman MJ, Sakai H. 2003. Regulation of flowering time and floral organ
identity by a MicroRNA and its APETALA2-like target genes. Plant Cell 15:
2730–2741.
Basu U, Almeida L, Olson NE, Meng Y, Williams JL, Moore SS, Guan LL.
2011. Transcriptome analysis of the medulla tissue from cattle in response to
bovine spongiform encephalopathy using digital gene expression tag profiling.
Journal of Toxicology and Environmental Health, Part A: Current Issues 74:
127–137.
Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate: a practical
and powerful approach to multiple testing. Journal of the Royal Statistical
Society. Series B (Methodological) 57: 289–300.
Blackman BK, Michaels SD, Rieseberg LH. 2011. Connecting the sun to
flowering in sunflower adaptation. Molecular Ecology 20: 3503–3512.
Brachi B, Faure N, Horton M, Flahauw E, Vazquez A, Nordborg M, Bergelson
J, Cuguen J, Roux F. 2010. Linkage and association mapping of Arabidopsis
thaliana flowering time in nature. PLoS Genetics 6: e1000940.
Castillejo C, Pelaz S. 2008. The balance between CONSTANS and
TEMPRANILLO activities determines FT expression to trigger flowering.
Current Biology 18: 1338–1343.
Ceplitis ALF, Su Y, Lascoux M. 2005. Bayesian inference of evolutionary history
from chloroplast microsatellites in the cosmopolitan weed Capsella
bursa-pastoris (Brassicaceae). Molecular Ecology 14: 4221–4233.
Chen H, Huang X, Gusmaroli G, Terzaghi W, Lau OS, Yanagawa Y, Zhang Y,
Li J, Lee JH, Zhu D et al. 2010. Arabidopsis CULLIN4-damaged DNA
binding protein 1 interacts with CONSTITUTIVELY
PHOTOMORPHOGENIC1-SUPPRESSOR OF PHYA complexes to
regulate photomorphogenesis and flowering time. Plant Cell 22: 108–123.
Chen Y, Yang X, He K, Liu M, Li J, Cao Z, Lin Z, Zhang Y, Wang X, Qiu X
et al. 2006. The MYB transcription factor superfamily of Arabidopsis:
expression analysis and phylogenetic comparison with the rice MYB family.
Plant Molecular Biology 60: 107–124.
Cheptou PO, Carrue O, Rouifed S, Cantarel A. 2008. Rapid evolution of seed
dispersal in an urban environment in the weed Crepis sancta. Proceedings of the
National Academy of Sciences, USA 105: 3796–3799.
Choi K, Kim J, Hwang HJ, Kim S, Park C, Kim SY, Lee I. 2011. The FRIGIDA
complex activates transcription of FLC, a strong flowering repressor in
Arabidopsis, by recruiting chromatin modification factors. Plant Cell 23:
289–303.
Crevillen P, Dean C. 2010. Regulation of the floral repressor gene FLC: the
complexity of transcription in a chromatin context. Current Opinion in Plant
Biology 14: 1–7.
Dieterle M, Thomann A, Renou JP, Parmentier Y, Cognat V, Lemonnier G,
Muller R, Shen WH, Kretsch T, Genschik P. 2005. Molecular and
functional characterization of Arabidopsis Cullin 3A. Plant Journal 41:
386–399.
Dieterle M, Zhou YC, Schafer E, Funk M, Kretsch T. 2001. EID1, an F-box
protein involved in phytochrome A-specific light signaling. Genes &
Development 15: 939–944.
Dill A, Sun T. 2001. Synergistic derepression of gibberellin signaling by
removing RGA and GAI function in Arabidopsis thaliana. Genetics 159:
777–785.
Dinneny JR, Yadegari R, Fischer RL, Yanofsky MF, Weigel D. 2004. The role
of JAGGED in shaping lateral organs. Development 131: 1101–1110.
Domagalska MA, Schomburg FM, Amasino RM, Vierstra RD, Nagy F, Davis
SJ. 2007. Attenuation of brassinosteroid signaling enhances FLC expression
and delays flowering. Development 134: 2841–2850.
New Phytologist (2012) 194: 676–689
www.newphytologist.com
New
Phytologist
Doyle MR, Bizzell CM, Keller MR, Michaels SD, Song J, Noh YS, Amasino
RM. 2005. HUA2 is required for the expression of floral repressors in
Arabidopsis thaliana. Plant Journal 41: 376–385.
Elmer KR, Meyer A. 2011. Adaptation in the age of ecological genomics: insights
from parallelism and convergence. Trends in Ecology and Evolution 26:
298–306.
Eveland AL, Satoh-Nagasawa N, Goldshmidt A, Meyer S, Beatty M, Sakai H,
Ware D, Jackson D. 2010. Digital gene expression signatures for maize
development. Plant Physiology 154: 1024–1039.
Fornara F, Panigrahi KC, Gissot L, Sauerbrunn N, Ruhl M, Jarillo JA,
Coupland G. 2009. Arabidopsis DOF transcription factors act redundantly to
reduce CONSTANS expression and are essential for a photoperiodic flowering
response. Developmental Cell 17: 75–86.
Fournier-Level A, Korte A, Cooper MD, Nordborg M, Schmitt J, Wilczek AM.
2011. A map of local adaptation in Arabidopsis thaliana. Science 334: 86–89.
Franke DM, Ellis AG, Dharjwa W, Freshwater M, Fujikawa M, Padron A, Weis
AE. 2006. A steep cline in flowering time for Brassica rapa in Southern
California: population-level variation in the field and the greenhouse.
International Journal of Plant Sciences 167: 83–92.
Franzke A, Lysak MA, Al-Shehbaz IA, Koch MA, Mummenhoff K. 2011.
Cabbage family affairs: the evolutionary history of Brassicaceae. Trends in Plant
Science 16: 108–116.
Gan S, Amasino RM. 1997. Making sense of senescence (molecular genetic
regulation and manipulation of leaf senescence). Plant Physiology 113:
313–319.
German DA, Friesen N, Neuffer B, Al-Shehbaz IA, Hurka H. 2009.
Contribution to ITS phylogeny of the Brassicaceae, with special reference to
some Asian taxa. Plant Systematics and Evolution 283: 33–56.
Griffiths J, Murase K, Rieu I, Zentella R, Zhang ZL, Powers SJ, Gong F,
Phillips AL, Hedden P, Sun TP et al. 2006. Genetic characterization and
functional analysis of the GID1 gibberellin receptors in Arabidopsis. Plant Cell
18: 3399–3414.
Hancock AM, Brachi B, Faure N, Horton MW, Jarymowycz LB, Sperone FG,
Toomajian C, Roux F, Bergelson J. 2011. Adaptation to climate across the
Arabidopsis thaliana genome. Science 334: 83–86.
Harhay GP, Smith TP, Alexander LJ, Haudenschild CD, Keele JW,
Matukumalli LK, Schroeder SG, Van Tassell CP, Gresham CR, Bridges SM
et al. 2010. An atlas of bovine gene expression reveals novel distinctive tissue
characteristics and evidence for improving genome annotation. Genome Biology
11: R102.
Hegedus Z, Zakrzewska A, Agoston VC, Ordas A, Racz P, Mink M, Spaink HP,
Meijer AH. 2009. Deep sequencing of the zebrafish transcriptome response to
mycobacterium infection. Molecular Immunology 46: 2918–2930.
Helliwell CA, Sheldon CC, Olive MR, Walker AR, Zeevaart JA, Peacock
WJ, Dennis ES. 1998. Cloning of the Arabidopsis ent-kaurene oxidase
gene GA3. Proceedings of the National Academy of Sciences, USA 95:
9019–9024.
Hemm MR, Herrmann KM, Chapple C. 2001. AtMYB4: a transcription factor
general in the battle against UV. Trends in Plant Science 6: 135–136.
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. 2005. Very high
resolution interpolated climate surfaces for global land areas. International
Journal of Climatology 25: 1965–1978.
Hu YX, Wang YX, Liu XF, Li JY. 2004. Arabidopsis RAV1 is down-regulated by
brassinosteroid and may act as a negative regulator during plant development.
Cell Research 14: 8–15.
Hurka H, Neuffer B. 1997. Evolutionary processes in the genus Capsella
(Brassicaceae). Plant Systematics and Evolution 206: 295–316.
Ikeda M, Ohme-Takagi M. 2009. A novel group of transcriptional repressors in
Arabidopsis. Plant and Cell Physiology 50: 970–975.
Imaizumi T. 2010. Arabidopsis circadian clock and photoperiodism: time to
think about location. Current Opinion in Plant Biology 13: 83–89.
Imaizumi T, Tran HG, Swartz TE, Briggs WR, Kay SA. 2003. FKF1 is
essential for photoperiodic-specific light signalling in Arabidopsis. Nature 426:
302–306.
Jofuku KD, den Boer BG, Van Montagu M, Okamuro JK. 1994. Control of
Arabidopsis flower and seed development by the homeotic gene APETALA2.
Plant Cell 6: 1211–1225.
2012 The Authors
New Phytologist 2012 New Phytologist Trust
New
Phytologist
Johanson U, West J, Lister C, Michaels S, Amasino R, Dean C. 2000. Molecular
analysis of FRIGIDA, a major determinant of natural variation in Arabidopsis
flowering time. Science 290: 344–347.
Kanrar S, Bhattacharya M, Arthur B, Courtier J, Smith HM. 2008. Regulatory
networks that function to specify flower meristems require the function of
homeobox genes PENNYWISE and POUND-FOOLISH in Arabidopsis. Plant
Journal 54: 924–937.
Katsutani N, Ikeda Y. 1997. Studies on the flowering behaviour of perennial
Delphinium. Journal of the Japanese Society for Horticultural Science 66:
121–131.
Keurentjes JJ, Fu J, Terpstra IR, Garcia JM, van den Ackerveken G, Snoek LB,
Peeters AJ, Vreugdenhil D, Koornneef M, Jansen RC. 2007. Regulatory
network construction in Arabidopsis by using genome-wide gene expression
quantitative trait loci. Proceedings of the National Academy of Sciences, USA 104:
1708–1713.
Kirik V, Kolle K, Wohlfarth T, Misera S, Baumlein H. 1998. Ectopic expression
of a novel MYB gene modifies the architecture of the Arabidopsis inflorescence.
Plant Journal 13: 729–742.
Koch M, Haubold B, Mitchell-Olds T. 2001. Molecular systematics of the
Brassicaceae: evidence from coding plastidic matK and nuclear Chs sequences.
American Journal of Botany 88: 534–544.
Koch MA, Haubold B, Mitchell-Olds T. 2000. Comparative evolutionary
analysis of chalcone synthase and alcohol dehydrogenase loci in Arabidopsis,
Arabis, and related genera (Brassicaceae). Molecular Biology and Evolution 17:
1483–1498.
Kruskal WH, Wallis WA. 1952. Use of ranks in one-criterion variance analysis.
Journal of the American Statistical Association 47: 583–621.
Lagercrantz U. 2009. At the end of the day: a common molecular mechanism for
photoperiod responses in plants? Journal of Experimental Botany 60:
2501–2515.
Laubinger S, Marchal V, Le Gourrierec J, Wenkel S, Adrian J, Jang S, Kulajta C,
Braun H, Coupland G, Hoecker U. 2006. Arabidopsis SPA proteins regulate
photoperiodic flowering and interact with the floral inducer CONSTANS to
regulate its stability. Development 133: 3213–3222.
Le Corre V, Roux F, Reboud X. 2002. DNA polymorphism at the FRIGIDA
gene in Arabidopsis thaliana: extensive nonsynonymous variation is consistent
with local selection for flowering time. Molecular Biology and Evolution 19:
1261–1271.
Ledger S, Strayer C, Ashton F, Kay SA, Putterill J. 2001. Analysis of the
function of two circadian-regulated CONSTANS-LIKE genes. Plant Journal
26: 15–22.
Lempe J, Balasubramanian S, Sureshkumar S, Singh A, Schmid M, Weigel D.
2005. Diversity of flowering responses in wild Arabidopsis thaliana strains.
PLoS Genetics 1: 109–118.
Li H, Ruan J, Durbin R. 2008. Mapping short DNA sequencing reads and
calling variants using mapping quality scores. Genome Research 18: 1851–1858.
Li SF, Parish RW. 1995. Isolation of two novel myb-like genes from Arabidopsis
and studies on the DNA-binding properties of their products. Plant Journal 8:
963–972.
Lim MH, Kim J, Kim YS, Chung KS, Seo YH, Lee I, Hong CB, Kim HJ, Park
CM. 2004. A new Arabidopsis gene, FLK, encodes an RNA binding protein
with K homology motifs and regulates flowering time via FLOWERING
LOCUS C. Plant Cell 16: 731–740.
Lin C. 2002. Blue light receptors and signal transduction. Plant Cell 14(Suppl):
S207–S225.
Linde M, Diel S, Neuffer B. 2001. Flowering ecotypes of Capsella bursa-pastoris
(L.) Medik. (Brassicaceae) analysed by a cosegregation of phenotypic characters
(QTL) and molecular markers. Annals of Botany 87: 91–99.
Liu Z, Meyerowitz EM. 1995. LEUNIG regulates AGAMOUS expression in
Arabidopsis flowers. Development 121: 975–991.
Locke JC, Southern MM, Kozma-Bognar L, Hibberd V, Brown PE, Turner
MS, Millar AJ. 2005. Extension of a genetic network model by iterative
experimentation and mathematical analysis. Molecular Systems Biology 1:
2005.0013.
Lu W. 1946. A new climate regionalization in China. Acta Geographica Sinica:
1–10.
2012 The Authors
New Phytologist 2012 New Phytologist Trust
Research 687
Martin-Trillo M, Lazaro A, Poethig RS, Gomez-Mena C, Pineiro MA,
Martinez-Zapater JM, Jarillo JA. 2006. EARLY IN SHORT DAYS 1 (ESD1)
encodes ACTIN-RELATED PROTEIN 6 (AtARP6), a putative component of
chromatin remodelling complexes that positively regulates FLC accumulation
in Arabidopsis. Development 133: 1241–1252.
Matsuoka Y, Takumi S, Kawahara T. 2008. Flowering time diversification and
dispersal in central Eurasian wild wheat Aegilops tauschii Coss.: genealogical and
ecological framework. PLoS ONE 3: e3138.
Michaels SD, He Y, Scortecci KC, Amasino RM. 2003. Attenuation of
FLOWERING LOCUS C activity as a mechanism for the evolution of
summer-annual flowering behavior in Arabidopsis. Proceedings of the National
Academy of Sciences, USA 100: 10102–10107.
Mizoguchi T, Wright L, Fujiwara S, Cremer F, Lee K, Onouchi H, Mouradov
A, Fowler S, Kamada H, Putterill J et al. 2005. Distinct roles of GIGANTEA
in promoting flowering and regulating circadian rhythms in Arabidopsis. Plant
Cell 17: 2255–2270.
Montague JL, Barrett SCH, Eckert CG. 2008. Re-establishment of clinal
variation in flowering time among introduced populations of purple loosestrife
(Lythrum salicaria, Lythraceae). Journal of Evolutionary Biology 21: 234–245.
Montesinos-Navarro A, Wig J, Pico FX, Tonsor SJ. 2011. Arabidopsis thaliana
populations show clinal variation in a climatic gradient associated with altitude.
New Phytologist 189: 282–294.
Mouradov A, Cremer F, Coupland G. 2002. Control of flowering time:
interacting pathways as a basis for diversity. Plant Cell 14(Suppl): S111–S130.
Murashige T, Skoog F. 1962. A revised medium for rapid growth and bioassays
with tobacco cultures. Physiologia Plantarum 15: 473–497.
Nah G, Chen ZJ. 2010. Tandem duplication of the FLC locus and the origin of a
new gene in Arabidopsis related species and their functional implications in
allopolyploids. New Phytologist 186: 228–238.
Nakamichi N, Kita M, Niinuma K, Ito S, Yamashino T, Mizoguchi T, Mizuno
T. 2007. Arabidopsis clock-associated pseudo-response regulators PRR9, PRR7
and PRR5 coordinately and positively regulate flowering time through the
canonical CONSTANS-dependent photoperiodic pathway. Plant and Cell
Physiology 48: 822–832.
Neuffer B. 1990. Ecotype differentiation in Capsella. Vegetatio 89: 165–171.
Neuffer B. 2011. Native range variation in Capsella bursa-pastoris (Brassicaceae).
Flora 206: 107–119.
Neuffer B, Bartelheim S. 1989. Gen-ecology of Capsella bursa-pastoris from an
altitudinal transect in the Alps. Oecologia 81: 521–527.
Neuffer B, Bernhardt K, Hurka H, Kropf M. 2011. Monitoring population and
gene pool dynamics of the annual species Capsella bursa-pastoris (Brassicaceae):
a review of relevant species traits and the initiation of a long-term genetic
monitoring programme. Biodiversity and Conservation 20: 309–323.
Neuffer B, Hoffrogge R. 2000. Ecotypic and allozyme variation of Capsella
bursa-pastoris and C. rubella (Brassicaceae) along latitude and altitude gradients
on the Iberian Peninsula. Anales del Jardin Botanico de Madrid 57: 299–315.
Neuffer B, Hurka H. 1986. Variation of development time until flowering in
natural populations of Capsella bursa-pastoris (Cruciferae). Plant Systematics and
Evolution 152: 277–296.
Neuffer B, Hurka H. 1999. Colonization history and introduction dynamics of
Capsella bursa-pastoris (Brassicaceae) in North America: isozymes and
quantitative traits. Molecular Ecology 8: 1667–1681.
Noh YS, Amasino RM. 2003. PIE1, an ISWI family gene, is required for FLC
activation and floral repression in Arabidopsis. Plant Cell 15: 1671–1682.
Oh S, Zhang H, Ludwig P, van Nocker S. 2004. A mechanism related to the
yeast transcriptional regulator Paf1c is required for expression of the
Arabidopsis FLC ⁄ MAF MADS box gene family. Plant Cell 16: 2940–2953.
Pazhouhandeh M, Molinier J, Berr A, Genschik P. 2011. MSI4 ⁄ FVE interacts
with CUL4-DDB1 and a PRC2-like complex to control epigenetic regulation
of flowering time in Arabidopsis. Proceedings of the National Academy of Sciences,
USA 108: 3430–3435.
Pemberton JM, Beraldi D, Craig BH, Hopkins J. 2011. Digital gene expression
analysis of gastrointestinal helminth resistance in Scottish blackface lambs.
Molecular Ecology 20: 910–919.
Proveniers M, Rutjens B, Brand M, Smeekens S. 2007. The Arabidopsis TALE
homeobox gene ATH1 controls floral competency through positive regulation
of FLC. Plant Journal 52: 899–913.
New Phytologist (2012) 194: 676–689
www.newphytologist.com
688 Research
Pruneda-Paz JL, Breton G, Para A, Kay SA. 2009. A functional genomics
approach reveals CHE as a component of the Arabidopsis circadian clock.
Science 323: 1481–1485.
Pruneda-Paz JL, Kay SA. 2010. An expanding universe of circadian networks in
higher plants. Trends in Plant Science 15: 259–265.
Pruzinska A, Tanner G, Anders I, Roca M, Hortensteiner S. 2003. Chlorophyll
breakdown: pheophorbide a oxygenase is a Rieske-type iron-sulfur protein,
encoded by the accelerated cell death 1 gene. Proceedings of the National Academy
of Sciences, USA 100: 15259–15264.
Qi X-H, Xu X-W, Lin X-J, Zhang W-J, Chen X-H. 2012. Identification of
differentially expressed genes in cucumber (Cucumis sativus L.) root
under waterlogging stress by digital gene expression profile. Genomics 99:
160–168.
Rao KP, Vani G, Kumar K, Sinha AK. 2009. Rhythmic expression of mitogen
activated protein kinase activity in rice. Molecules and Cells 28: 417–422.
Richmond TA, Bleecker AB. 1999. A defect in beta-oxidation causes abnormal
inflorescence development in Arabidopsis. Plant Cell 11: 1911–1924.
Rieu I, Ruiz-Rivero O, Fernandez-Garcia N, Griffiths J, Powers SJ, Gong F,
Linhartova T, Eriksson S, Nilsson O, Thomas SG et al. 2008. The gibberellin
biosynthetic genes AtGA20ox1 and AtGA20ox2 act, partially redundantly, to
promote growth and development throughout the Arabidopsis life cycle. Plant
Journal 53: 488–504.
Riihimaki M, Podolsky R, Kuittinen H, Koelewijn H, Savolainen O. 2005.
Studying genetics of adaptive variation in model organisms: flowering time
variation in Arabidopsis lyrata. Genetica 123: 63–74.
Robinson MD, McCarthy DJ, Smyth GK. 2010. edgeR: a Bioconductor package
for differential expression analysis of digital gene expression data. Bioinformatics
26: 139–140.
Robinson MD, Smyth GK. 2007. Moderated statistical tests for assessing
differences in tag abundance. Bioinformatics 23: 2881–2887.
Robinson MD, Smyth GK. 2008. Small-sample estimation of negative binomial
dispersion, with applications to SAGE data. Biostatistics 9: 321–332.
Rossiter M. 1996. Incidence and consequences of inherited environmental
effects. Annual Review of Ecology and Systematics 27: 451–476.
Sablowski RWM, Meyerowitz EM. 1998. A homolog of NO APICAL
MERISTEM is an immediate target of the floral homeotic genes
APETALA3 ⁄ PISTILLATA. Cell 92: 93–103.
Salomé PA, Bomblies K, Laitinen RA, Yant L, Mott R, Weigel D. 2011.
Genetic architecture of flowering-time variation in Arabidopsis thaliana.
Genetics 188: 2421–2433.
Salomé PA, To JP, Kieber JJ, McClung CR. 2006. Arabidopsis response
regulators ARR3 and ARR4 play cytokinin-independent roles in the control of
circadian period. Plant Cell 18: 55–69.
Schmitz RJ, Tamada Y, Doyle MR, Zhang X, Amasino RM. 2009. Histone
H2B deubiquitination is required for transcriptional activation of
FLOWERING LOCUS C and for proper control of flowering in Arabidopsis.
Plant Physiology 149: 1196–1204.
Shin R, Burch AY, Huppert KA, Tiwari SB, Murphy AS, Guilfoyle TJ,
Schachtman DP. 2007. The Arabidopsis transcription factor MYB77
modulates auxin signal transduction. Plant Cell 19: 2440–2453.
Shindo C, Aranzana MJ, Lister C, Baxter C, Nicholls C, Nordborg M, Dean C.
2005. Role of FRIGIDA and FLOWERING LOCUS C in determining
variation in flowering time of Arabidopsis. Plant Physiology 138: 1163–1173.
Simpson GG, Dijkwel PP, Quesada V, Henderson I, Dean C. 2003. FY is an
RNA 3’ end-processing factor that interacts with FCA to control the
Arabidopsis floral transition. Cell 113: 777–787.
Slotte T, Ceplitis A, Neuffer B, Hurka H, Lascoux M. 2006. Intrageneric
phylogeny of Capsella (Brassicaceae) and the origin of the tetraploid C.
bursa-pastoris based on chloroplast and nuclear DNA sequences. American
Journal of Botany 93: 1714–1724.
Slotte T, Holm K, McIntyre LM, Lagercrantz U, Lascoux M. 2007. Differential
expression of genes important for adaptation in Capsella bursa-pastoris
(Brassicaceae). Plant Physiology 145: 160–173.
Slotte T, Huang H-R, Holm K, Ceplitis A, St. Onge K, Chen J, Lagercrantz U,
Lascoux M. 2009. Splicing variation at a FLOWERING LOCUS C homeolog
is associated with flowering time variation in the tetraploid Capsella
bursa-pastoris. Genetics 183: 337–345.
New Phytologist (2012) 194: 676–689
www.newphytologist.com
New
Phytologist
Slotte T, Huang H, Lascoux M, Ceplitis A. 2008. Polyploid speciation did not
confer instant reproductive isolation in Capsella (Brassicaceae). Molecular
Biology and Evolution 25: 1472–1481.
Somers DE, Schultz TF, Milnamow M, Kay SA. 2000. ZEITLUPE encodes a
novel clock-associated PAS protein from Arabidopsis. Cell 101: 319–329.
St. Onge K, Foxe JP, Li J, Li H, Holm K, Corcoran P, Slotte T, Lascoux M,
Wright SI. 2012. Coalescent-based analysis distinguishes between allo- and
autopolyploid origin in shepherd’s purse (Capsella bursa-pastoris). Molecular
Biology and Evolution. doi: 10.1093/molbev/mss024.
Steinmeyer B, Wöhrmann K, Hurka H. 1985. Phänotypenvariabilität and
Umwelt bei Capsella bursa-pastoris (Cruciferae). Flora 177: 323–334.
Stinchcombe JR, Weinig C, Ungerer M, Olsen KM, Mays C, Halldorsdottir
SS, Purugganan MD, Schmitt J. 2004. A latitudinal cline in flowering
time in Arabidopsis thaliana modulated by the flowering time gene
FRIGIDA. Proceedings of the National Academy of Sciences, USA 101:
4712–4717.
Sturn A, Quackenbush J, Trajanoski Z. 2002. Genesis: cluster analysis of
microarray data. Bioinformatics 18: 207–208.
Tyler L, Thomas SG, Hu J, Dill A, Alonso JM, Ecker JR, Sun TP. 2004. Della
proteins and gibberellin-regulated seed germination and floral development in
Arabidopsis. Plant Physiology 135: 1008–1019.
Ullah H, Chen JG, Wang S, Jones AM. 2002. Role of a heterotrimeric G
protein in regulation of Arabidopsis seed germination. Plant Physiology 129:
897–907.
Vailleau F, Daniel X, Tronchet M, Montillet JL, Triantaphylides C, Roby D.
2002. A R2R3-MYB gene, AtMYB30, acts as a positive regulator of the
hypersensitive cell death program in plants in response to pathogen
attack. Proceedings of the National Academy of Sciences, USA 99:
10179–10184.
VanDijk H, Boudry P, McCombie H, Vernet P. 1997. Flowering time in wild
beet (Beta vulgaris ssp. maritima) along a latitudinal cline. Acta Oecologica 18:
47–60.
Veitch NJ, Johnson PC, Trivedi U, Terry S, Wildridge D, MacLeod A. 2010.
Digital gene expression analysis of two life cycle stages of the human-infective
parasite, Trypanosoma brucei gambiense reveals differentially expressed clusters
of co-regulated genes. BMC Genomics 11: 124.
Wang JW, Czech B, Weigel D. 2009. miR156-regulated SPL transcription
factors define an endogenous flowering pathway in Arabidopsis thaliana. Cell
138: 738–749.
Weber E, Schmid B. 1998. Latitudinal population differentiation in two species
of Solidago (Asteraceae) introduced into Europe. American Journal of Botany
85: 1110–1121.
Wenkel S, Turck F, Singer K, Gissot L, Le Gourrierec J, Samach A, Coupland
G. 2006. CONSTANS and the CCAAT box binding complex share a
functionally important domain and interact to regulate flowering of
Arabidopsis. Plant Cell 18: 2971–2984.
Wu JF, Wang Y, Wu SH. 2008. Two new clock proteins, LWD1 and LWD2,
regulate Arabidopsis photoperiodic flowering. Plant Physiology 148: 948–959.
Xu L, Menard R, Berr A, Fuchs J, Cognat V, Meyer D, Shen WH. 2009. The
E2 ubiquitin-conjugating enzymes, AtUBC1 and AtUBC2, play redundant
roles and are involved in activation of FLC expression and repression of
flowering in Arabidopsis thaliana. Plant Journal 57: 279–288.
Yu H, Xu Y, Tan EL, Kumar PP. 2002. AGAMOUS-LIKE 24, a
dosage-dependent mediator of the flowering signals. Proceedings of the National
Academy of Sciences, USA 99: 16336–16341.
Zhang Q, Li H, Li R, Hu R, Fan C, Chen F, Wang Z, Liu X, Fu Y, Lin C.
2008. Association of the circadian rhythmic expression of GmCRY1a with a
latitudinal cline in photoperiodic flowering of soybean. Proceedings of the
National Academy of Sciences, USA 105: 21028–21033.
Zhang S, Yang C, Peng J, Sun S, Wang X. 2009. GASA5, a regulator of
flowering time and stem growth in Arabidopsis thaliana. Plant Molecular Biology
69: 745–759.
Zhang X, Chen Y, Wang ZY, Chen Z, Gu H, Qu LJ. 2007. Constitutive
expression of CIR1 (RVE2) affects several circadian-regulated processes and
seed germination in Arabidopsis. Plant Journal 51: 512–525.
Zheng J, Yin Y, Li B. 2010. A new scheme for climate regionalization in China.
Acta Geographica Sinica 65: 3–13.
2012 The Authors
New Phytologist 2012 New Phytologist Trust
New
Phytologist
Supporting Information
Additional supporting information may be found in the online
version of this article.
Fig. S1 Distribution of total sequence tags and distinct sequence
tags in each library.
Table S1 Sampling locations of C. bursa-pastoris and descriptive
statistics of flowering time for each population
Table S2 A prior list of genes involved in the control of flowering
time in A. thaliana, and genes indicated as candidate genes for
Research 689
flowering time in DGE and microarray studies in C.
bursa-pastoris
Table S3 Proportion of variance and descriptor loadings of the
components in principal component (PC) analyses
Table S4 Common genes analyzed for differential expression in
both DGE and microarray studies
Please note: Wiley-Blackwell are not responsible for the content
or functionality of any supporting information supplied by the
authors. Any queries (other than missing material) should be
directed to the New Phytologist Central Office.
New Phytologist is an electronic (online-only) journal owned by the New Phytologist Trust, a not-for-profit organization dedicated
to the promotion of plant science, facilitating projects from symposia to free access for our Tansley reviews.
Regular papers, Letters, Research reviews, Rapid reports and both Modelling/Theory and Methods papers are encouraged.
We are committed to rapid processing, from online submission through to publication ‘as ready’ via Early View – our average time
to decision is <25 days. There are no page or colour charges and a PDF version will be provided for each article.
The journal is available online at Wiley Online Library. Visit www.newphytologist.com to search the articles and register for table
of contents email alerts.
If you have any questions, do get in touch with Central Office ([email protected]) or, if it is more convenient,
our USA Office ([email protected])
For submission instructions, subscription and all the latest information visit www.newphytologist.com
2012 The Authors
New Phytologist 2012 New Phytologist Trust
New Phytologist (2012) 194: 676–689
www.newphytologist.com