Differential Expression of Genes Important for Adaptation in Capsella bursa-pastoris (Brassicaceae)

doi:10.1104/pp.107.102632

Journal List > Plant Physiol > v.145(1); Sep 2007

Plant Physiol. 2007 September; 145(1): 160–173.

doi: 10.1104/pp.107.102632.

PMCID: PMC1976575

Differential Expression of Genes Important for Adaptation in Capsella bursa-pastoris (Brassicaceae)¹^[W]^[OA]

Tanja Slotte,^* Karl Holm, Lauren M. McIntyre, Ulf Lagercrantz, and Martin Lascoux

Department of Evolution, Genomics and Systematics, Uppsala University, SE–752 36 Uppsala, Sweden (T.S., K.H., U.L., M.L.); and Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, Florida 32610–0266 (L.M.M.)

^*Corresponding author; e-mail tanja.slotte/at/ebc.uu.se.

Received May 23, 2007; Accepted July 10, 2007.

This article has been cited by other articles in PMC.

Abstract

Understanding the genetic basis of natural variation is of primary interest for evolutionary studies of adaptation. In Capsella bursa-pastoris, a close relative of Arabidopsis (Arabidopsis thaliana), variation in flowering time is correlated with latitude, suggestive of an adaptation to photoperiod. To identify pathways regulating natural flowering time variation in C. bursa-pastoris, we have studied gene expression differences between two pairs of early- and late-flowering C. bursa-pastoris accessions and compared their response to vernalization. Using Arabidopsis microarrays, we found a large number of significant differences in gene expression between flowering ecotypes. The key flowering time gene FLOWERING LOCUS C (FLC) was not differentially expressed prior to vernalization. This result is in contrast to those in Arabidopsis, where most natural flowering time variation acts through FLC. However, the gibberellin and photoperiodic flowering pathways were significantly enriched for gene expression differences between early- and late-flowering C. bursa-pastoris. Gibberellin biosynthesis genes were down-regulated in late-flowering accessions, whereas circadian core genes in the photoperiodic pathway were differentially expressed between early- and late-flowering accessions. Detailed time-series experiments clearly demonstrated that the diurnal rhythm of CIRCADIAN CLOCK-ASSOCIATED1 (CCA1) and TIMING OF CAB EXPRESSION1 (TOC1) expression differed between flowering ecotypes, both under constant light and long-day conditions. Differential expression of flowering time genes was biologically validated in an independent pair of flowering ecotypes, suggesting a shared genetic basis or parallel evolution of similar regulatory differences. We conclude that genes involved in regulation of the circadian clock, such as CCA1 and TOC1, are strong candidates for the evolution of adaptive flowering time variation in C. bursa-pastoris.

Flowering time is a major life-history trait contributing to reproduction and adaptation, especially in annual plants (Roux et al., 2006). The timing of flowering in relation to the environment is of crucial importance for seed production, and different flowering strategies may have evolved in response to local climatic conditions (Engelmann and Purugganan, 2006; Mitchell-Olds and Schmitt, 2006). The genetic basis of flowering time variation is well understood in Arabidopsis thaliana. Four main pathways, the photoperiod, vernalization, GA, and autonomous pathways, allow the plant to perceive and respond to changes in daylength, temperature, and hormonal status (Mouradov et al., 2002; Simpson and Dean, 2002; Koornneef et al., 2004). Floral pathway integrator genes integrate signals from these pathways and fine-tune the transition from vegetative to reproductive development, although recent studies also indicate that there is direct cross talk between pathways (Edwards et al., 2006; Gould et al., 2006; Salathia et al., 2007).

Understanding the genetic basis of natural variation is of primary interest for evolutionary studies of adaptation (Mitchell-Olds and Schmitt, 2006). The precise role of flowering genes among and within species can vary significantly, and the effect of allelic variation for these genes in natural populations is a focus of current research (Werner et al., 2005; Engelmann and Purugganan, 2006; Roux et al., 2006; Salathia et al., 2007). Recent studies demonstrate that the main genes responsible for natural variation in flowering time can differ between populations or species, reflecting differences in genetic architecture, ecological niche, and history. In A. thaliana, variation at the genes FRIGIDA (FRI) and FLOWERING LOCUS C (FLC), which are involved in the vernalization response, can explain a great deal of genetic variation in flowering time (Johanson et al., 2000; Caicedo et al., 2004; Zhao et al., 2007), and selection for earlier flowering appears to have acted on FRI (Hagenblad and Nordborg, 2002; Le Corre et al., 2002; Toomajian et al., 2006). In Arabidopsis suecica allotetraploids, late flowering is accomplished by trans-activation of strong A. thaliana FLC by functional FRI from Arabidopsis arenosa (Wang et al., 2006). Although most natural flowering time variation in A. thaliana seems to act through FLC, photoreceptor genes such as CRYPTOCHROME2 and PHYTOCHROME C have also been implicated (El-Assal et al., 2001; Balasubramanian et al., 2006). Findings from A. thaliana have successfully been used to start to elucidate the genetic basis of natural flowering time variation in other crucifer species (Brassica rapa: Schranz et al., 2002; Brassica nigra: Österberg et al., 2002; Brassica oleracea: Okazaki et al., 2007). However, despite the availability of genomic tools, and although assessing the generality of patterns seen in A. thaliana is clearly important, there is a dearth of studies on the genetic control of natural variation in flowering time in the closest relatives of A. thaliana, such as Arabidopsis lyrata or Capsella.

Capsella bursa-pastoris L. Medik. is a predominantly selfing, disomic tetraploid crucifer with a nearly worldwide distribution (Hurka and Neuffer, 1997). It is an annual plant species, characterized by great colonizing ability. Within C. bursa-pastoris, there is considerable variation for a range of life-history characteristics, including flowering time (Neuffer and Hurka, 1986; Paoletti et al., 1991; Ceplitis et al., 2005). As in A. thaliana, there is also variation in vernalization requirement, with some late-flowering accessions having an obligate requirement for vernalization in order to flower (A. Ceplitis, unpublished data). Flowering time differences are highly heritable (Linde et al., 2001), and correlation between flowering time and environmental factors indicates that flowering time may represent an adaptation to local climatic conditions (Neuffer and Hurka, 1986; Neuffer and Bartelheim, 1989; Neuffer, 1990). In C. bursa-pastoris, two to three major quantitative trait loci (QTL) for flowering time were found in an F2 population derived from crosses of two North American accessions (Linde et al., 2001; A. Ceplitis, B. Neuffer, M. Linde, T. Slotte, M. Kraft, and M. Lascoux, unpublished data), but so far little is known about the nature of the genetic differences underlying these QTL.

Changes in the balance between flowering time pathways can result in dramatic differences in flowering time (Lempe et al., 2005; Roux et al., 2006). To test whether gene regulation differences in known flowering time genes in Arabidopsis are also responsible for natural variation in flowering time in C. bursa-pastoris, we compare two accessions that differ widely in flowering time under a vernalization/nonvernalization regime for differences in gene expression and validate these differences in two accessions with less extreme differences in flowering time. This approach allows us to both identify flowering pathways that are differentially regulated between C. bursa-pastoris flowering ecotypes and to test whether these regulatory differences are shared across different early- and late-flowering ecotypes.

RESULTS

Flowering Time Variation in C. bursa-pastoris

Based on data from a survey of flowering time variation in a worldwide sample of C. bursa-pastoris (Ceplitis et al., 2005), we found that there was a significant correlation between flowering time and latitude (Pearson P = 0.64, P < 0.001; Fig. 1

), but not between longitude and flowering time. This clinal variation could indicate that flowering time has evolved as an adaptation to, for example, photoperiod. Although demographic processes can give rise to similar patterns (Mitchell-Olds and Schmitt, 2006), the lack of correlation to longitude suggests that at least part of the variation in flowering time in C. bursa-pastoris is adaptive. To accommodate the full range of flowering time variation, we chose to include two ecotypes to represent the extremes in flowering time variation (the early-flowering accession PL from Puli, Taiwan, and the late-flowering accession SE14 from Härnösand, Sweden; Fig. 1

). Two less extreme ecotypes, the early-flowering US721 from Shafter, CA, and the late-flowering US740, from Reno, NV, previously used as parents in a QTL-mapping cross (Linde et al., 2001; A. Ceplitis, B. Neuffer, M. Linde, T. Slotte, M. Kraft, and M. Lascoux, unpublished data), were selected for independent biological validation of gene expression differences between extreme flowering ecotypes (Fig. 1

Figure 1.

A, Flowering time is correlated with latitude in C. bursa-pastoris. The number of days to flowering from germination is significantly correlated with latitude (Pearson P = 0.64, P < 0.001), and linear regression is also significant (P (more ...)

Flowering Time Is Affected by Vernalization

We assessed the flowering time of ecotypes PL and SE14, with and without vernalization, using survival analysis, an analysis method for time-dependent developmental traits (see “Materials and Methods”) such as flowering time. We found that the survival function (i.e. the predicted probability of not flowering) was different across the four groups (P < 0.0001), and all pairwise comparisons, including that between vernalization treatments for the early-flowering accession PL, exhibited significantly different median flowering times (P < 0.001; Table I; Fig. 2

). Thus, vernalization had an effect on flowering time in both extreme flowering ecotypes, although the effect was greater for accession SE14 than accession PL (Table I).

Table I.

Flowering time of extreme flowering ecotypes with and without vernalization

Figure 2.

Predicted probability of survival (not flowering) for each of the four strata, using a nonparametric Cox proportional hazards model. The horizontal axis displays the flowering time (in days after germination), and the vertical axis displays the predicted (more ...)

Characterization of Gene Expression Differences between Flowering Ecotypes

To test whether genes involved in regulation of flowering time in A. thaliana were differentially expressed between flowering ecotypes of C. bursa-pastoris, we used A. thaliana CATMA 25k (Complete Arabidopsis Transcriptome Microarray; Allemeersch et al., 2005; www.catma.org) microarrays to assess genome-wide differential gene expression. Gene expression was measured in 1-week-old seedlings from each of the two extreme ecotypes, under a vernalization/nonvernalization regime (see “Materials and Methods”). This assay allows us to identify both genes that are differentially expressed between accessions and those that are differentially expressed as a result of vernalization treatment.

We assembled a list of 214 genes that have been identified as involved in flowering time in A. thaliana, based on Gene Ontology (GO) annotation (see “Materials and Methods”; Supplemental Appendix S2). Of these, 112 probes were analyzed for differential expression, and 21 were significantly differentially expressed (false discovery rate [FDR] ≤ 0.1; Table II). Interestingly, all significant differences were between accessions (Table II). Key circadian clock genes, such as the two myb-family transcription factor genes LATE ELONGATED HYPOCOTYL (LHY; At1g01060) and CIRCADIAN CLOCK-ASSOCIATED1 (CCA1; At2g46830) and TIMING OF CAB EXPRESSION1 (TOC1; At5g61380) involved in the core feedback loop of the circadian oscillator (Schaffer et al., 1998; Wang and Tobin, 1998; Alabadi et al., 2001; Mizoguchi et al., 2002), were differentially expressed, with LHY and CCA1 up-regulated in the late-flowering accession SE14 and TOC1 down-regulated. A casein kinase II β-subunit-encoding gene (CKB4, At2g44680), involved in regulation of circadian rhythm (Perales et al., 2006), was also down-regulated in accession SE14 compared to PL (Table II; Fig. 3

Table II.

CATMA GST identifiers, locus tags, and annotation for the a priori flowering time genes that had significant expression differences, all of which were differences among accessions

Figure 3.

Overview of flowering time pathways in A. thaliana. Figures are adapted from Mouradov et al. (2002), He and Amasino (2005), and Roux et al. (2006). Genes that were significantly differentially expressed between accessions are in boldface. Genes that were (more ...)

The expression of several genes in the GA pathway differed between accessions (Table II; Fig. 2

). Two genes involved in GA biosynthesis, GA4 encoding GA 3-β-dioxygenase/GA 3-β-hydroxylase (At1g15550; Talon et al., 1990; Chiang et al., 1995) and a gene encoding GA 20-oxidase (GA20OX2; At5g51810; Xu et al., 1995), were down-regulated in SE14 compared to PL, whereas genes encoding RGL1, a GA regulatory protein that represses GA signaling (Wen and Chang, 2002), and SLY1 (At4g24210), a gene involved in regulation of GA signaling (Dill et al., 2004), were up-regulated in the late-flowering accession SE14 compared to PL. In addition, six myb-family transcription factors whose expression is affected by GA (Chen et al., 2006) were differentially expressed across accessions. Three of these were up-regulated in accession SE14 (At5g65790, At5g02840, and At5g37260), and three were down-regulated (At3g46130, At5g67580, and At5g47390).

Other differentially expressed candidate genes for flowering included two genes in the vernalization pathway: VIP4 (At5g61150) and FRL1 (At5g16320), both involved in regulation of FLC expression (Zhang and van Nocker, 2002; Michaels et al., 2004), and the floral repressors EMF (At5g11530; Moon et al., 2003) and SVP (At2g22540; Hartmann et al., 2000; Gregis et al., 2006; Table II; Fig. 3

). The floral pathway integrator SOC1 (AGL20; At2g45660; Lee et al., 2000; Moon et al., 2003) was also differentially expressed, with a lower level of expression in accession SE14 than in PL (Table II; Fig. 3

Microarray data for an additional 10,859 probes were also analyzed for differential expression. The expression of a total of 1,642 differed significantly between groups at 10% FDR. The largest difference in gene expression was found between nonvernalized seedlings of accessions PL and SE14 (PLNV versus SENV, 1,493 genes). Fewer genes were differentially expressed between vernalized seedlings of the two accessions (PLV versus SEV, 874 genes), and very few gene expression differences were found between vernalized and nonvernalized seedlings (PLV versus PLNV, and SEV versus SENV, two genes). However, GO annotation of the 1,642 genes indicates that most of these genes function in various biological processes with no obvious relation to control of flowering time (Supplemental Appendix S3). Genes differentially expressed by vernalization encode a Gly-rich, endomembrane-located protein (At4g29030) and a microtubule-associated protein (MAP70-1) that have not been implicated previously in the vernalization response.

List Enrichment Analysis

We used list enrichment analysis to assess whether there was an overrepresentation of differentially expressed genes in GO categories of relevance to flowering time (see “Materials and Methods”). We found a significant overrepresentation of significantly differentially expressed genes in the category “circadian rhythm” (20 genes in category, seven significant, two-sided P = 2.3 × 10⁻², Fisher's exact test). There was also a significant overrepresentation of genes involved in GA metabolism and signaling (49 genes in category, 13 significant at FDR 0.1, two-sided P = 4.23 × 10⁻², Fisher's exact test).

Chromosomal Clustering of Differentially Expressed Genes on Ancestral Chromosome 4

To determine whether the positions of differentially expressed genes were random or clustered, we examined the chromosomal position of each differentially transcribed probe, based on the A. thaliana genome annotation. We found that part of A. thaliana chromosome 2, corresponding to ancestral chromosome 4 (ak4) in Capsella (Schranz et al., 2006), had a significantly higher proportion of differentially expressed genes in the PL-SE14 comparison than overall in the genome (0.185 of genes significant for ak4, 0.152 significant for all detected genes, χ² = 9.00, d.f. = 1, P = 2.7 × 10⁻³). This region of A. thaliana chromosome 2 constitutes an entire, separate chromosome in both A. lyrata and Capsella rubella. In A. thaliana, it corresponds to approximately 10 Mb of the lower part of chromosome 2 (delimited by the loci At2g21160 and At2g47730) containing a total of 2,867 annotated loci. In this study, 1,235 of these were labeled “present” and 228 were differentially expressed. In the US721-US740 comparison, we found no overrepresentation of differentially expressed genes for ak4.

Verification of Differential Expression

We selected four genes for verification of the microarray results (SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 [SOC1], TOC1, CCA1, and FLC). Although FLC was not differentially expressed after correction for multiple testing, there was some evidence for differential expression (P = 0.03), and the literature on this gene as well as the vernalization response led us to include it in our panel. Real-time reverse transcription (RT)-PCR ΔCT values for differentially expressed candidate genes (SOC1, TOC1, CCA1) were consistent with array results (Supplemental Appendix S4). Thus, we did not identify any false positives among the genes assessed. Analysis of real-time RT-PCR gene expression measurements indicated that FLC expression did not differ between accessions prior to vernalization and was diminished after vernalization in both accessions, but to a greater extent in SE14.

Flowering Time Ecotypes Differ in Rhythmic Expression of CCA1 and TOC1

Because the microarray data analysis indicated that circadian core genes were differentially expressed, we set up two experiments to assess differences in the expression of circadian genes over time. The rhythmic expression of the circadian core oscillator genes TOC1 and CCA1 differed between accessions PL and SE14 under both constant light and long-day conditions (Fig. 4

Figure 4.

Normalized expression levels (CT_target − CT_reference) for CCA1 and TOC1 at 12 time points following entrainment. Mean expression levels are indicated by black symbols for accession PL and white symbols for accession SE14. Error bars indicate (more ...)

Independent Biological Validation of Differential Expression

Independent biological validation of differentially expressed flowering time genes was obtained in a pair of less extreme flowering ecotypes, representative of the average part of the flowering time distribution (Fig. 1

; accessions US721 and US740). Gene expression microarray analysis (see “Materials and Methods”) indicated that a total of 97 probes for flowering time genes, including probes for 18 of the 21 differentially expressed flowering time genes in the PL-SE14 comparison, were in common between experiments (Supplemental Appendix S5). As an independent biological validation, we asked whether the set of 18 flowering time genes that were differentially expressed between the extreme flowering ecotypes also had evidence for differential expression in the US721-US740 comparison. Out of 33 significant contrasts between accessions for these genes in the PL-SE14 comparison, 12 contrasts corresponding to eight different genes were also significant in the US721-US740 comparison (Table III). These genes included circadian core genes such as LHY and TOC1, as well genes involved in GA biosynthesis and response (e.g. GA4, RGL1, MYB48, and the myb-family transcription factor At5g02840); FRL1, a gene involved in the vernalization response; and SVP, a floral repressor (Table III). Overall, this constitutes good agreement between experiments and indicates that flowering ecotypes with intermediate differences in flowering time also differ in the expression of genes regulating circadian rhythm and GA biosynthesis and response.

Table III.

Independent biological validation of differentially expressed flowering time genes

DISCUSSION

In this study we have characterized differential gene expression between flowering ecotypes of C. bursa-pastoris, to test whether gene regulation differences in known flowering time genes in Arabidopsis are also responsible for natural variation in flowering time in C. bursa-pastoris. In the close relative A. thaliana, a major part of natural flowering time variation is due to multiple independent mutations in the FRI gene, the function of which is to induce FLC expression that in turn represses the transition to flowering (Johanson et al., 2000). In our experiment, quantitative RT-PCR analysis of FLC expression showed that FLC was indeed down-regulated in both early- and late-flowering accessions as a result of vernalization, but did not differ significantly in expression between accessions before vernalization. Thus, although it seems likely that the function of FLC as an important mediator of the vernalization response is conserved across A. thaliana and C. bursa-pastoris, our data shows that similar mutations as those found in A. thaliana FRI have not been important in generating natural flowering time variation in the C. bursa-pastoris accessions we have studied. Other pathways and genes are more likely responsible for natural variation in flowering time in this species. Microarray analysis of differential expression between early- and late-flowering C. bursa-pastoris offered some insight as to which pathways these may be. Indeed, we found a significant enrichment of differentially expressed genes in two of the main A. thaliana flowering time pathways, the GA pathway and the photoperiodic pathway, and, more specifically, circadian clock-related genes in the latter. The fact that different pathways seem responsible for natural flowering time variation in A. thaliana and the studied accessions of C. bursa-pastoris could suggest that these species have experienced different selective constraints on flowering time, or that genetic variation at flowering time genes differed between species, providing different avenues to variation in flowering time.

In A. thaliana, variation in circadian rhythm among natural accessions contributes to fitness (Dodd et al., 2005), is correlated with latitude of origin (Michael et al., 2003), and can cause variation in flowering time (Imaizumi and Kay, 2006). Because differences in gene expression, especially for genes with circadian expression, may be difficult to interpret based on data from a single time point (Michael et al., 2003; Darrah et al., 2006; Keurentjes et al., 2007), we conducted a time-series study of gene expression for two core circadian genes, CCA1 and TOC1. Both of these genes differed in diurnal expression between the early-flowering PL and the late-flowering SE14 ecotype. In A. thaliana, changes in rhythmic expression of CCA1 or TOC1 have effects on flowering time (Strayer et al., 2000; Alabadi et al., 2001; Mizoguchi et al., 2002). Interestingly, in our microarray experiment, CKB4, which encodes a regulatory subunit of casein kinase II and leads to changes in circadian period and phase in A. thaliana when overexpressed (Perales et al., 2006), was up-regulated in the early-flowering accession PL. Circadian rhythm is a crucial component in the now generally accepted external coincidence model (Bunning, 1936). In a molecular version of this model, the circadian clock generates daily oscillation of CONSTANS (CO) mRNA. As protein stability of CO is controlled by light, the coincidence of light and high CO expression that only occurs in long days induce the pathway integrator FT and thereby flowering (Valverde et al., 2004; Corbesier et al., 2007). Thus, alterations in genes controlling the circadian clock are attractive candidates for the evolution of flowering time differences in C. bursa-pastoris.

The GA pathway was also enriched for differentially expressed genes among the early-flowering accession PL and the late-flowering accession SE14. In A. thaliana, the GA pathway is generally considered as a default pathway acting mainly when flowering is not induced by long days. Although gene expression differences for genes in the GA pathway might be important for flowering time variation in C. bursa-pastoris, an attractive alternative hypothesis is that these expression differences are a secondary effect of altered circadian clock function, and that this altered clock function affects flowering time mainly through other pathways (e.g. through CO and FT). In this study, two GA biosynthesis genes displayed a higher expression in early flowering accession PL as compared to SE14, which might well be an effect of altered clock function (Blázquez et al., 2002). Blázquez et al. (2002) further concluded that GA contribution is not quantitatively important in the determination of flowering time by the photoperiod pathway in A. thaliana. Rather, the increase in GA concentration induced by long days might be relevant for cell expansion required during stem elongation, rather than the determination of flowering time.

Differential expression of flowering time genes was biologically validated in a pair of less extreme flowering ecotypes from North America. The good agreement of flowering time gene expression differences between both pairs of accessions could indicate that the genetic basis of expression differences is shared by common ancestry, or that similar regulatory differences have evolved in parallel. Although the two pairs of accessions were sampled in widely different geographical regions (the extreme flowering ecotypes PL and SE14 from Taiwan and Sweden, respectively, and the less extreme flowering accessions US721 and US740 from the United States), a shared genetic background is not unlikely, as the species has apparently attained its present distribution recently (Ceplitis et al., 2005). Indeed, both early- and late-flowering C. bursa-pastoris accessions were introduced into North America by European settlers (Neuffer and Hurka, 1999). To resolve the genetic basis of gene expression differences, a natural extension of this study is to map gene expression as a quantitative trait, as has been done e.g. in yeast (Brem et al., 2002), maize (Zea mays), humans, and mice (Schadt et al., 2003) and in A. thaliana (Keurentjes et al., 2007).

Overall, most genes differed in expression across accessions, and not as a result of the vernalization treatment, although vernalization had an effect on flowering time. This could indicate that vernalization affected the expression of very few genes, or that the effect on gene expression was generally small so that we had limited power to detect these differences. Similar results have been obtained in other species, for example, in Lolium perenne, where cDNA microarray analysis identified only a handful of genes differentially expressed as a result of vernalization treatment (Ciannamea et al., 2006). In A. thaliana, several known components of the vernalization pathway are not themselves regulated by vernalization (VRN1, VRN2) or regain their normal level of expression upon return to warmer temperatures (VIN3; Levy et al., 2002; Wood et al., 2006). Indeed, localized modification of FLC chromatin may be the main underlying mechanism for vernalization response in A. thaliana (Bastow et al., 2004; He et al., 2004; Sung and Amasino, 2004; Shindo et al., 2006; Swiezewski et al., 2007). Interestingly, we identified two novel vernalization-responsive genes, a cortical microtubule-associated protein (MAP70-1; Korolev et al., 2005) and a Gly-rich, endomembrane-located protein (At4g29030). Whether these expression changes are involved in vernalization is unclear, but they could be related to cold acclimatization because changes in membrane composition and cytoskeletal organization are both believed to play a role in this process (Browse and Xin, 2001).

Most of the differentially expressed genes were scattered across different chromosomal regions. However, the proportion of significant genes (out of all detected genes) was higher than expected for ancestral chromosome 4, which corresponds to the lower part of A. thaliana chromosome 2 (Schranz et al., 2006). No clear signs of amplification or deletion of specific chromosomal regions were observed, with approximately equal numbers of genes up- and down-regulated in each flowering ecotype. Chromosome-scale transcriptional profiling in rice (Oryza sativa) and Arabidopsis has identified variation in transcriptional activity across chromosomes (Li et al., 2005; Schmid et al., 2005). Such variation has been shown to be correlated with tissue and developmental stage as well as external factors such as cold stress (Yamada et al., 2003). A recent study on gene expression diversity among genotypes in A. thaliana (Kliebenstein et al., 2006) also reported a correlated variation of DNA sequence divergence and expression variation along chromosomes. In C. bursa-pastoris, increased localized sequence divergence between extreme flowering ecotypes or differences in chromatin structure between these accessions could explain the observed clustering of differentially expressed genes.

In this study we have characterized gene expression differences between early- and late-flowering accessions of C. bursa-pastoris. Flowering time variation may have evolved rapidly in this species and is probably of adaptive importance (Ceplitis et al., 2005). We have shown that natural variation in the C. bursa-pastoris flowering time ecotypes we have studied is likely not caused by variation at the FRI gene, as in A. thaliana. Instead, the evolution of flowering time variation appears to have involved changes in the expression of genes regulating the circadian rhythm, and possibly also regulatory changes in the GA pathway. While further study is needed to elucidate the full pathway and mechanisms involved, genes involved in regulation of the circadian clock, such as CCA1 and TOC1, clearly constitute strong candidates for adaptive evolution in C. bursa-pastoris.

MATERIALS AND METHODS

Flowering Time

We compared vernalized and nonvernalized plants for each of the two accessions (PL and SE14). Thus, for this experiment there were four groups: PL nonvernalized (PLNV), PL vernalized (PLV), SE14 nonvernalized (SENV), and SE14 vernalized (SEV). For each accession, a single mother plant grown from seed collected in the wild was selected and selfed. Two seeds from this plant were grown and selfed to produce two lines. For each of the four groups, seed from the two lines was used to set up eight plates as follows. Approximately 50 surface-sterilized seeds were sown on each 0.8% agar plate with Murashige and Skoog medium (Duchefa). For the vernalization treatment, four plates per line were set up and incubated at 2.6°C for 28 d. On day 25 of the vernalization treatment, four plates per line for the nonvernalized treatment were set up in the same manner and stratified at 2.6°C for 4 d in order to break seed dormancy. On the 29th day of the experiment, all 32 plates (two lines for both accessions and two treatments, four plates per line and treatment) were placed in a growth chamber under long-day conditions (16/8 h photoperiod, 22°C/18°C), in a randomized complete block design (Cochran and Cox, 1992). The growth chamber was divided into two blocks depending upon light intensity (block 1 had a higher average light intensity of 250 μmol m⁻² s⁻¹ and block 2 had a lower average light intensity of 180 μmol m⁻² s⁻¹). Within each block four plates (two plates for each line) of each of the four groups were placed in a randomized position. After 7 d seeds had germinated and seedlings from all lines had a pair of true leaves.

Two plates, representing the two lines, from each of the two blocks for each vernalization treatment and accession were used to select 15 seedlings, which were transferred to individual pots. Pots were placed in a growth chamber under long-day conditions as before (16/8 h photoperiod, 18°C/22°C, average light intensity 200 μmol m⁻² s⁻¹), again in a randomized block design consisting of five blocks where each block was a tray that contained three plants of each treatment-accession combination or a total of 12 plants. Flowering time was recorded as the time from germination to the opening of the first flower. In addition, the number of true leaves at the onset of flowering was recorded.

Analysis of Flowering Time Data

The time to flowering is a time-dependent developmental trait. Survival analysis was initially developed to model human lifetimes (Cox, 1972). Survival analysis can be applied to any time-dependent occurrence and can be thought of as the analysis of the time until an event. In this case the event is flowering, and so survival time is time until flowering and the survival function is the predicted probability of not flowering. Survival analysis has previously been used to model flowering time in plants (e.g. Vermerris et al., 2002); a tutorial of how to apply these methods to flowering time data can be found in Vermerris and McIntyre (1999) and a more general statistical introduction can be found in Kleinbaum (1996). In brief, the distribution of time until event data is often long tailed (not normal), and this implies that the mean is often not equal to the median. The distributional assumptions necessary for the tests of the parameters in a linear regression are violated and the resulting P values from these tests are suspect. Survival analysis makes no such assumption. We used a nonparametric Cox proportional hazards model, which assumes no specific baseline hazard. Instead, that function is estimated from the data using partial likelihood approaches (Cox, 1972; Lawless, 1982). This is an attractive option, as the baseline hazard is often unknown. We tested equality over groups (strata) comprised of the different genotype-treatment combinations (i.e. PLNV, PLV, SENV, and SEV) using a Wilcoxon rank sums test (Kleinbaum, 1996). Analyses of flowering time data were performed in SAS 9.1 (SAS Institute).

Microarray

Seven-day-old seedlings from the experiment described above were sampled from the plates in block 1. From each of the four independent plates, two plates for each of the two lines, 15 whole seedlings were sampled and immediately flash-frozen in liquid nitrogen, to give four independent biological replicates of each treatment accession combination. Sampling took place at midday, 7 h after dawn. Sampling occurred in the same order as the randomized block design and, therefore, the order of sampling was random with respect to vernalization-treatment and accession. We measured gene expression in seedlings because previous studies have shown that several key flowering time regulators are apparent at a very early stage in Arabidopsis thaliana (Kobayashi et al., 1999; Keurentjes et al., 2007), and to minimize differences in developmental stage and/or tissue composition between accessions.

Total RNA was extracted using the RNeasy plant mini kit (Qiagen), including DNase treatment using the RNase-free DNase set (Qiagen), according to the manufacturer's instructions. Protocols for RNA amplification, labeling, and hybridization were modified from those used by Wellmer et al. (2004), and a detailed description is found in Supplemental Appendix S1. Briefly, first-strand cDNA was synthesized using 5 μg of total RNA as template, 0.5 μg of T7dT primer, and the SuperScript III reverse transcriptase system (Invitrogen). The Lucidea Universal Scorecard control mixes (GE Healthcare Bio-Sciences) were diluted 10 times, and 1 μL of spike-in mix was added to each sample prior to cDNA synthesis. Second-strand cDNA was synthesized using Invitrogen's Escherichia coli polymerase I and second-strand buffer, and the resulting cDNA was phenol-chloroform purified. The purified cDNA was in vitro transcribed using the Megascript T7 kit (Ambion). Purified aRNA (5 μg) was reverse transcribed using random hexamer primers (Invitrogen) and SuperScript III (Invitrogen), with incorporation of aminoallyl-dUTP (Sigma-Aldrich). Following purification, Cy-3 and Cy-5 esters (GE Healthcare) were coupled to the aminoallyl-labeled cDNA. Unincorporated dye esters were removed using a QIAquick spin column (Qiagen).

Hybridization was conducted according to a loop design (Kerr and Churchill, 2001a, 2001b; Churchill, 2002) with the four independent biological replicates of each treatment-accession described above (supplemental figure in Supplemental Appendix S1). Preliminary studies in the lab conducted on technical replicates indicated a high degree of reliability (Fleiss, 1981), and so technical replicates were not performed for this study. A detailed protocol for the microarray hybridizations is available in Supplemental Appendix S1. Briefly, A. thaliana CATMA 25k microarrays (Allemeersch et al., 2005; www.catma.org) were prehybridized at 42°C for 30 to 45 min in a buffer containing 5× SSC, 25% formamide, 0.1% SDS, and 0.1% BSA; rinsed; and dried by centrifugation. Labeled cDNA was mixed with Ambion's SlideHyb glass array hybridization buffer number 1 (Ambion) prior to hybridization. Hybridizations were carried out at 42°C for a minimum of 60 h. Following posthybridization washes, microarrays were scanned with an Axon 4000B scanner (Molecular Devices).

Microarray images were quantitated using the Spot 3.0 R-based package (CSIRO), using the GOGAC segmentation option, and signal median was background corrected using the morph.open.close background estimate. Previous work has demonstrated that this is a reliable quantification approach (Slotte and McIntyre, 2007).

The spot quality was assessed as follows. For each microarray and dye, all spots were ranked and divided into quartiles. Quartiles were compared using the kappa coefficient and spots that differed in rank by more than one quartile between replicates were flagged. In addition, individual spots that were saturated were flagged.

To determine whether there was evidence for hybridization for a given probe, the distribution of negative controls was used. There are 16 negative controls on the CATMA slide distributed across the slide. Two of these negative controls have evidence of contamination (data not shown) and were excluded from consideration, leaving 14 spots per slide. To conclude that the sample has hybridized to a particular spot, the signal from the spot should be above the 90th percentile of the signal of negative control spots (Li et al., 2004). For each of the four replicates, if at least three of the four spots for that probe were not detected then the probe was labeled as “absent” for that treatment. All spots that were labeled “absent” by this criterion in all accession-treatment combinations were excluded from further analysis. Scripts implementing reliability assessment in R 2.0.1 (R Development Core Team, 2004) are available from the authors upon request.

When comparing different genotypes directly on a microarray, there is always a possibility that differences in gene expression are confounded with sequence divergence (Gilad and Borevitz, 2006). This is likely to be less of a problem in this study, due to the low levels of genetic diversity in Capsella bursa-pastoris (Ceplitis et al., 2005), especially in exonic regions (Slotte et al., 2006). Accordingly, quantitative RT-PCR on differentially expressed genes verified the gene expression differences observed using microarrays. Exonic sequence divergence between A. thaliana and C. bursa-pastoris could potentially also result in reduced hybridization intensities and reduced power to detect true differential expression, although gene expression measurements should not be biased as long as only intraspecific comparisons are made. In this study, the percentage of probes reliably detected in this study, 44.6%, was however similar to observed levels in studies of gene expression in A. thaliana using the same platform (Allemeersch et al., 2005). We note that this microarray assay does not allow us to separate the two duplicate copies of each gene in C. bursa-pastoris, as these are highly similar at the exonic level (Slotte et al., 2006), but that this could be done using allele-specific quantitative RT-PCR methods such as those described by de Meaux et al. (2006).

Intensity values for each microarray (log2 background-corrected signal) were lowess-transformed (Cleveland, 1979; Dudoit et al., 2002) and then normalized by subtracting the median for that particular slide and dye. The normalized intensity values (Y) for spots present in at least one treatment accession combination were analyzed in an ANOVA modeling framework (i.e. Kerr et al., 2000; Kerr and Churchill, 2001b; Wolfinger et al., 2001; Churchill, 2002; Oleksiak et al., 2002; Wayne and McIntyre, 2002). The model Y_ijkl = μ + d_i + g_j + ρ_kl + _ijkl was fit, where Y is a function of the fixed effects of dye (d), g is the effect of group where there are four groups (PLNV, PLV, SENV, SEV), and the random effect of slide ρ with is the random error. The mean over all observations for a particular probe is μ. We used the Shapiro-Wilk's statistic to test for deviation from normality of the residuals. Four pairwise contrasts were examined, PLNV versus SENV, PLNV versus PLV, SENV versus SEV, and PLV versus SEV, and the group effect was deemed significant if any one of the pairwise contrasts was significant. Each individual test was controlled at 10% FDR, to balance type 1 and type 2 errors (Benjamini and Hochberg, 1995; for a review, see Verhoeven et al., 2005). Probes that were flagged before analyses were scrutinized closely if they were declared significant. Microarray data are deposited in ArrayExpress under accession numbers E-ATMX-22 and E-ATMX-23.

List Creation

We downloaded A. thaliana locus tags and GO annotation corresponding to the probes on the CATMA array from The Arabidopsis Information Resource (www.arabidopsis.org). While the species are different and one cannot be certain of the similarity of annotation across species, the species are closely related (e.g. Galloway et al., 1998; Koch et al., 2000), so it is likely that the annotation for A. thaliana is largely appropriate for Capsella. Comparative mapping studies have shown that, although the species differ by a few major chromosomal rearrangements (Koch and Kiefer, 2005; Yogeeswaran et al., 2005), there is virtually complete conservation of gene order and content between A. thaliana and Capsella (Acarkan et al., 2000; Rossberg et al., 2001; Boivin et al., 2004). Thus, it is reasonable to expect that flowering time pathways are also largely conserved between Capsella and A. thaliana.

We assembled a list of genes that have been identified as involved in flowering time. An overview of the current knowledge of A. thaliana flowering time pathways is found in Figure 3

. For the development of the flowering time list, we included a total of 214 probes (which were also present on the CATMA array) whose GO biological process annotation contained the terms “circadian rhythm” (GO:0007623), “flower development” (GO:0009908), “vegetative to reproductive phase transition” (GO:0010228), “photoperiod” (GO:0009648), “vernalization response” (GO:0010048), or “gibberellic acid” (gibberellic acid biosynthetic process, GO:0009686; gibberellic acid metabolic process, GO:0009685; or gibberellic acid-mediated signaling, GO:0009740; gibberellic acid catabolic process, GO:0045487). The final list was manually curated to include additional flowering time genes that were not annotated using these terms (e.g. FRL1, CATMA5a14630). The resulting list represents a group for which we were a priori interested in their responses, and they are listed in Supplemental Appendix S2.

We tested for statistical overrepresentation or underrepresentation of significantly differentially expressed genes in the six categories listed above, using Fisher's exact tests. List enrichment analyses, lowess and median normalization, ANOVA, and FDR correction of microarray data were performed using SAS 9.1 (SAS Institute) and JMP 6.0 microarray (SAS Institute).

Microarray Verification

Total RNA from the four biological replicates of each group was used as source for the real-time RT-PCR verification of specific transcript levels. For each replicate, 0.5 μg of total RNA was reverse transcribed to cDNA using random hexamer primers (Invitrogen) and SuperScript III reverse transcriptase (Invitrogen) following the manufacturer's instructions. cDNA samples were diluted 1:100 and amplified using the Platinum SYBR Green qPCR SuperMix (Invitrogen), on an ABI PRISM 7000 sequence detection system (Applied Biosystems). The two-step cycling program was as follows: 50°C for 3 min and 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 30 s. Melt curve analyses were performed after each amplification to confirm specificity of products. Each cDNA sample was run in technical triplicates. As a further data quality control, PCR efficiencies were calculated for each individual amplification with the software LinRegPCR (Ramakers et al., 2003). Any wells showing strongly deviating PCR efficiencies of either target or reference genes were omitted from further analysis. Among the 384 reactions run in the RT-PCR verification test panel, five wells were omitted from analysis due to amplification problems.

Primers were designed to amplify both homoeologous loci based on direct sequences for TOC1 and CCA1. In other instances, we tested and used primers originally designed for A. thaliana, SOC1 (Czechowski et al., 2004), or Arabidopsis lyrata, TUB and FLC (our laboratory). Whenever possible, each primer set was designed to include one primer that bridges an intron to avoid amplification of possible remaining genomic DNA. Primer sequences are listed in the supplemental table in Supplemental Appendix S1. We used transcription level measurements for the TUB gene, which displayed consistent and even amplification over all accessions and treatments, as a reference to normalize target gene transcription levels. The threshold cycle (CT) values of replicates were averaged, and the difference of the mean CT values for reference and target genes (ΔCT) was calculated for each accession and treatment combination.

Real-Time RT-PCR Assay for Time-Series Analysis of TOC1 and CCA1

Expression levels of TOC1 and CCA1 were monitored in two time-series experiments under two light regimes: constant light and long day (16 h light/8 h dark). For each time series, approximately 40 plants of each accession for each time point were germinated on two separate 0.8% agar plates with Murashige and Skoog medium (Duchefa). The two plates were randomly positioned in the growth chamber, yielding two environmental replicates of each accession at each time point. Seeds were stratified for 5 d at 2.6°C, followed by entrainment at 22°C under long-day conditions with a light intensity of 52 μmol m⁻² s⁻¹ for 7 d, before release into either constant light (52 μmol m⁻² s⁻¹) or continued long-day (52 μmol m⁻² s⁻¹) conditions. Two pools of 15 to 20 seedlings were sampled from each plate on 12 time points over 48 h, at 4-h intervals. Sampling of the constant light time series was initiated at 4 h after dawn, whereas sampling of the long-day time series was initiated at dawn.

Total RNA was isolated in two separate extractions per accession and plate, using the RNeasy plant mini kit (Qiagen). cDNA synthesis and amplification were conducted as for the real-time RT-PCR verification (see above). Each accession for each time point was run in technical PCR duplicates, which enabled the comparison of both accessions on one RT-PCR plate. TOC1 and CCA1 were amplified with primer sets CbpTOC1_1043Fq/1240Rq and CCA1_5/6, respectively. TUB expression levels were used for normalization.

Biological Validation of Gene Expression Differences

To obtain an independent biological validation of flowering time gene expression differences, we assessed gene expression differences between two North American accessions of C. bursa-pastoris (US721 and US740), which are less extreme in their differences in flowering time (Fig. 1

). Gene expression was measured using CATMA microarrays, in a setup identical to that described above except that sampling took place at 9 h after dawn, 2 h later than for the experiment including accessions PL and SE14. Differential expression was analyzed as outlined above.

Supplemental Data

The following materials are available in the online version of this article.

Supplemental Appendix S1. Detailed experimental protocols.
Supplemental Appendix S2. List of flowering time genes printed on the CATMA 25k microarray.
Supplemental Appendix S3. Results of microarray analysis for ecotypes PL and SE14.
Supplemental Appendix S4. Microarray verification.
Supplemental Appendix S5. Independent biological validation of differential expression for flowering time genes.

Supplementary Material

[Supplemental Data]

Click here to view.

Acknowledgments

We thank Mattias Myrenås and Myriam Heuertz for experimental assistance.

Notes

¹This work was supported by grants from the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (to M.L. and U.L.); a grant from the Swedish Research Council (to U.L.); and grants from the Nilsson-Ehle, Wallenberg, Sederholms, and Tullberg foundations (to T.S.).

The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Martin Lascoux (martin.lascoux/at/ebc.uu.se).

^[W]The online version of this article contains Web-only data.

^[OA]Open Access articles can be viewed online without a subscription.

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