Differential Expression of Genes Important forAdaptation 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.)
Understanding the genetic basis of natural variation is of primary interest for evolutionary studies of adaptation. In Capsellabursa-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 accessionsand compared their response to vernalization. Using Arabidopsis microarrays, we found a large number of significant dif-ferences in gene expression between flowering ecotypes. The key flowering time gene FLOWERING LOCUS C (FLC) was notdifferentially expressed prior to vernalization. This result is in contrast to those in Arabidopsis, where most natural floweringtime variation acts through FLC. However, the gibberellin and photoperiodic flowering pathways were significantly enrichedfor 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 differentiallyexpressed between early- and late-flowering accessions. Detailed time-series experiments clearly demonstrated that thediurnal rhythm of CIRCADIAN CLOCK-ASSOCIATED1 (CCA1) and TIMING OF CAB EXPRESSION1 (TOC1) expressiondiffered between flowering ecotypes, both under constant light and long-day conditions. Differential expression of floweringtime genes was biologically validated in an independent pair of flowering ecotypes, suggesting a shared genetic basis orparallel evolution of similar regulatory differences. We conclude that genes involved in regulation of the circadian clock, suchas 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 contrib-uting to reproduction and adaptation, especially inannual plants (Roux et al., 2006). The timing of flower-ing in relation to the environment is of crucial impor-tance for seed production, and different floweringstrategies may have evolved in response to local cli-matic conditions (Engelmann and Purugganan, 2006;Mitchell-Olds and Schmitt, 2006). The genetic basisof flowering time variation is well understood inArabidopsis thaliana. Four main pathways, the photo-period, vernalization, GA, and autonomous pathways,allow the plant to perceive and respond to changesin daylength, temperature, and hormonal status
(Mouradov et al., 2002; Simpson and Dean, 2002;Koornneef et al., 2004). Floral pathway integrator genesintegrate signals from these pathways and fine-tunethe transition from vegetative to reproductive devel-opment, although recent studies also indicate that thereis direct cross talk between pathways (Edwards et al.,2006; Gould et al., 2006; Salathia et al., 2007).
Understanding the genetic basis of natural variationis of primary interest for evolutionary studies of ad-aptation (Mitchell-Olds and Schmitt, 2006). The pre-cise role of flowering genes among and within speciescan vary significantly, and the effect of allelic variationfor these genes in natural populations is a focus ofcurrent research (Werner et al., 2005; Engelmann andPurugganan, 2006; Roux et al., 2006; Salathia et al.,2007). Recent studies demonstrate that the main genesresponsible for natural variation in flowering time candiffer between populations or species, reflecting dif-ferences in genetic architecture, ecological niche, andhistory. In A. thaliana, variation at the genes FRIGIDA(FRI) and FLOWERING LOCUS C (FLC), which areinvolved in the vernalization response, can explaina 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 tohave acted on FRI (Hagenblad and Nordborg, 2002;Le Corre et al., 2002; Toomajian et al., 2006). In Arabidopsissuecica allotetraploids, late flowering is accomplished
1 This work was supported by grants from the Swedish ResearchCouncil for Environment, Agricultural Sciences and Spatial Plan-ning (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.).
* Corresponding author; e-mail [email protected] author responsible for distribution of materials integral to
the findings presented in this article in accordance with the policydescribed in the Instructions for Authors (www.plantphysiol.org) is:Martin Lascoux ([email protected]).
[W] The online version of this article contains Web-only data.[OA] Open Access articles can be viewed online without a sub-
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by trans-activation of strong A. thaliana FLC by functionalFRI from Arabidopsis arenosa (Wang et al., 2006). Al-though most natural flowering time variation in A.thaliana seems to act through FLC, photoreceptor genessuch as CRYPTOCHROME2 and PHYTOCHROMEC have also been implicated (El-Assal et al., 2001;Balasubramanian et al., 2006). Findings from A. thali-ana have successfully been used to start to elucidatethe genetic basis of natural flowering time variation inother crucifer species (Brassica rapa: Schranz et al.,2002; Brassica nigra: Osterberg et al., 2002; Brassicaoleracea: Okazaki et al., 2007). However, despite theavailability of genomic tools, and although assessingthe generality of patterns seen in A. thaliana is clearlyimportant, there is a dearth of studies on the geneticcontrol of natural variation in flowering time in theclosest relatives of A. thaliana, such as Arabidopsis lyrataor Capsella.
Capsella bursa-pastoris L. Medik. is a predominantlyselfing, disomic tetraploid crucifer with a nearly world-wide distribution (Hurka and Neuffer, 1997). It is anannual plant species, characterized by great colonizingability. Within C. bursa-pastoris, there is considerablevariation for a range of life-history characteristics, in-cluding flowering time (Neuffer and Hurka, 1986;Paoletti et al., 1991; Ceplitis et al., 2005). As in A.thaliana, there is also variation in vernalization require-ment, with some late-flowering accessions having anobligate requirement for vernalization in order to flower(A. Ceplitis, unpublished data). Flowering time differ-ences are highly heritable (Linde et al., 2001), andcorrelation between flowering time and environmentalfactors indicates that flowering time may represent anadaptation to local climatic conditions (Neuffer andHurka, 1986; Neuffer and Bartelheim, 1989; Neuffer,1990). In C. bursa-pastoris, two to three major quantita-tive trait loci (QTL) for flowering time were found in
an F2 population derived from crosses of two NorthAmerican 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 knownabout the nature of the genetic differences underlyingthese QTL.
Changes in the balance between flowering time path-ways can result in dramatic differences in floweringtime (Lempe et al., 2005; Roux et al., 2006). To testwhether gene regulation differences in known flower-ing time genes in Arabidopsis are also responsible fornatural variation in flowering time in C. bursa-pastoris,we compare two accessions that differ widely in flower-ing time under a vernalization/nonvernalization re-gime for differences in gene expression and validatethese differences in two accessions with less extremedifferences in flowering time. This approach allowsus to both identify flowering pathways that are differ-entially regulated between C. bursa-pastoris floweringecotypes and to test whether these regulatory dif-ferences 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 timevariation in a worldwide sample of C. bursa-pastoris(Ceplitis et al., 2005), we found that there was a sig-nificant correlation between flowering time and lati-tude (Pearson P 5 0.64, P , 0.001; Fig. 1), but notbetween longitude and flowering time. This clinal var-iation could indicate that flowering time has evolvedas an adaptation to, for example, photoperiod. Al-though demographic processes can give rise to similar
Figure 1. A, Flowering time is correlated with lati-tude in C. bursa-pastoris. The number of days toflowering from germination is significantly correlatedwith latitude (Pearson P 5 0.64, P , 0.001), andlinear regression is also significant (P , 0.001, solidline). B, The distribution of flowering time of a setof natural accessions of C. bursa-pastoris. Floweringtime ranges from less than 35 d to more than 200 d(the y axis is truncated at 200 d), when plants aregrown without vernalization treatment. The blackbars indicate the flowering time of the accessionsused in this study, and their designations are givenunder the corresponding bar. Accessions PL and SE14were chosen to represent the extremes of the range ofvariation in flowering time variation, whereas acces-sions US721 and US740 were used for biologicalvalidation of gene expression differences.
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patterns (Mitchell-Olds and Schmitt, 2006), the lack ofcorrelation to longitude suggests that at least part ofthe variation in flowering time in C. bursa-pastoris isadaptive. To accommodate the full range of floweringtime variation, we chose to include two ecotypes torepresent the extremes in flowering time variation (theearly-flowering accession PL from Puli, Taiwan, andthe late-flowering accession SE14 from Harnosand,Sweden; Fig. 1). Two less extreme ecotypes, the early-flowering US721 from Shafter, CA, and the late-floweringUS740, from Reno, NV, previously used as parents in aQTL-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 bio-logical validation of gene expression differences be-tween extreme flowering ecotypes (Fig. 1).
Flowering Time Is Affected by Vernalization
We assessed the flowering time of ecotypes PL andSE14, with and without vernalization, using survivalanalysis, an analysis method for time-dependent de-velopmental traits (see ‘‘Materials and Methods’’) suchas flowering time. We found that the survival function(i.e. the predicted probability of not flowering) wasdifferent across the four groups (P , 0.0001), and allpairwise comparisons, including that between vernal-ization treatments for the early-flowering accessionPL, exhibited significantly different median floweringtimes (P , 0.001; Table I; Fig. 2). Thus, vernalizationhad an effect on flowering time in both extreme flower-ing ecotypes, although the effect was greater for ac-cession SE14 than accession PL (Table I).
Characterization of Gene Expression Differencesbetween Flowering Ecotypes
To test whether genes involved in regulation offlowering time in A. thaliana were differentially ex-pressed between flowering ecotypes of C. bursa-pastoris,we used A. thaliana CATMA 25k (Complete Arabidop-sis Transcriptome Microarray; Allemeersch et al., 2005;www.catma.org) microarrays to assess genome-wide
differential gene expression. Gene expression wasmeasured in 1-week-old seedlings from each of thetwo extreme ecotypes, under a vernalization/non-vernalization regime (see ‘‘Materials and Methods’’).This assay allows us to identify both genes that aredifferentially expressed between accessions and thosethat are differentially expressed as a result of vernal-ization treatment.
We assembled a list of 214 genes that have beenidentified as involved in flowering time in A. thaliana,based on Gene Ontology (GO) annotation (see ‘‘Mate-rials and Methods’’; Supplemental Appendix S2). Ofthese, 112 probes were analyzed for differential expres-sion, and 21 were significantly differentially expressed(false discovery rate [FDR] # 0.1; Table II). Interest-ingly, all significant differences were between acces-sions (Table II). Key circadian clock genes, such asthe two myb-family transcription factor genes LATEELONGATED HYPOCOTYL (LHY; At1g01060) and CIR-CADIAN CLOCK-ASSOCIATED1 (CCA1; At2g46830)and TIMING OF CAB EXPRESSION1 (TOC1; At5g61380)involved in the core feedback loop of the circadianoscillator (Schaffer et al., 1998; Wang and Tobin, 1998;Alabadi et al., 2001; Mizoguchi et al., 2002), were dif-ferentially expressed, with LHY and CCA1 up-regulatedin the late-flowering accession SE14 and TOC1 down-regulated. A casein kinase II b-subunit-encoding gene(CKB4, At2g44680), involved in regulation of circa-dian rhythm (Perales et al., 2006), was also down-regulated in accession SE14 compared to PL (Table II;Fig. 3).
The expression of several genes in the GA pathwaydiffered between accessions (Table II; Fig. 2). Twogenes involved in GA biosynthesis, GA4 encodingGA 3-b-dioxygenase/GA 3-b-hydroxylase (At1g15550;
Table I. Flowering time of extreme flowering ecotypes with andwithout vernalization
The median, the third quartile (q3), and the first quartile (q1) of thenumber of days to flowering from germination, for each of the fourgroups studied (PLNV and PLV [nonvernalized and vernalized acces-sion PL, respectively], and SENV and SEV [nonvernalized and vernal-ized accession SE14, respectively]), are shown. The test for a differenceamong medians for the four groups was significant (P , 0.0001). If onlyPLNV and PLV were compared, the median flowering time was stillsignificantly different (P , 0.001).
Group q3 Median q1
PLNV 33 32.0 30PLV 28 27.0 26SENV 125 107.5 92SEV 47 42.0 40
Figure 2. Predicted probability of survival (not flowering) for each ofthe four strata, using a nonparametric Cox proportional hazards model.The horizontal axis displays the flowering time (in days after germina-tion), and the vertical axis displays the predicted probability of notflowering (a value of 1.0 indicates none of the plants are flowering).
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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
Positive fold changes (FC) correspond to higher level of expression in accession SE14 than in accession PL. For each gene and contrast (SE14nonvernalized [SENV] versus PL nonvernalized [PLNV], and SE14 vernalized [SEV] versus PL vernalized [PLV]), the F value (F), P value, and FDR-corrected P value (FDR) are listed.
GST ID Locus Tag Annotation
Contrast
SENV versus PLNV SEV versus PLV
F P Value FDR FC F P Value FDR FC
CATMA1a00045 AT1G01060 myb-family transcriptionfactor, contains Pfamprofile: PF00249 myb-likeDNA-binding domain;identical to cDNA LHYGI:3281845
21.0 2.73E-03 3.79E-02 4.3 18.9 3.63E-03 4.34E-02 4.0
CATMA1b14565 AT1G15550 GA 3-b-dioxygenase/GA3-b-hydroxylase (GA4),identical to GI:2160454
6.1 6.93E-02 1.85E-01 21.1 35.6 3.91E-03 4.34E-02 21.4
CATMA1a14565 AT1G15550 GA 3-b-dioxygenase/GA3-b-hydroxylase (GA4),identical to GI:2160454
38.3 3.44E-03 4.24E-02 21.2 114.2 4.30E-04 9.54E-03 21.3
CATMA1a55630 AT1G66350 GA regulatory protein(RGL1), similar toGB:CAA75492 fromA. thaliana; containsPfam profile PF03514:GRAS family transcriptionfactor; identical toGI:15777856, GI:15777857
106.1 5.34E-04 1.38E-02 2.3 122.2 4.07E-04 9.54E-03 2.4
CATMA2a16835 AT2G18170 Mitogen-activated proteinkinase, putative/MAPK,putative (MPK7), identicalto AtMPK7; A. thaliana,SWISS-PROT:Q39027;PMID:12119167
93.1 6.22E-04 1.38E-02 21.9 117.3 3.97E-04 9.54E-03 22.1
CATMA2a21060 AT2G22540 SHORT VEGETATIVE PHASEprotein (SVP), identical toSVP GI:10944319
149.2 1.71E-05 1.90E-03 22.4 75.9 1.20E-04 9.54E-03 21.9
CATMA2a43136 AT2G44680 Casein kinase II b-chain,putative, similar to CK II;A. thaliana, SWISS-PROT:O81275
41.0 4.86E-03 4.92E-02 21.6 42.1 4.65E-03 4.70E-02 21.6
CATMA2a44050 AT2G45660 MADS-box protein (AGL20) 34.6 4.87E-04 1.38E-02 22.5 39.3 3.26E-04 9.54E-03 22.6CATMA2a45275 AT2G46830 myb-related transcription
factor (CCA1), identical toGI:4090569 from A. thaliana
39.0 5.19E-03 4.92E-02 3.0 33.7 6.57E-03 6.08E-02 2.8
CATMA3a39110 AT3G46130 myb-family transcription factor(MYB48), contains Pfamprofile: PF00249 myb-likeDNA-binding domain
13.1 6.67E-03 5.51E-02 21.7 10.9 1.07E-02 7.41E-02 21.6
CATMA4a25950 AT4G24210 SLY1, F-box family protein/SLEEPY1 protein, containsPfam PF00646: F-box domain;similar to F-box protein Fbx8(GI:6164735; human)
23.7 7.86E-03 5.51E-02 1.1 8.1 4.60E-02 1.70E-01 1.1
CATMA4a26790 AT4G25100 Superoxide dismutase (iron),chloroplast (SODB)/ironsuperoxide dismutase(FSD1), identical toGI:166700: GB:AAA32791;supported by cDNA,Ceres:32935
16.8 1.06E-02 6.53E-02 3.1 15.8 1.18E-02 7.52E-02 3.0
(Table continues on following page.)
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Table II. (Continued from previous page.)
GST ID Locus Tag Annotation
Contrast
SENV versus PLNV SEV versus PLV
F P Value FDR FC F P Value FDR FC
CATMA5a01920 AT5G02840 myb-family transcriptionfactor, contains PFAMprofile: PF00249 myb-likeDNA-binding domain
20.5 5.32E-03 4.92E-02 1.6 17.5 7.45E-03 6.36E-02 1.5
CATMA5a10290 AT5G11530 EMBRYONIC FLOWER1(EMF1), identical toGI:15430697
11.9 1.38E-02 7.46E-02 1.5 12.2 1.31E-02 7.52E-02 1.6
CATMA5a14630 AT5G16320 FRL1, family member ofFRI-related genes that isrequired for the winter-annual habit
94.2 5.70E-04 3.52E-02 21.4 80.4 7.78E-04 5.12E-02 21.4
CATMA5a32570 AT5G37260 myb-family transcriptionfactor, contains Pfamprofile: PF00249 myb-likeDNA-binding domain
37.1 7.25E-03 5.51E-02 5.9 21.0 1.68E-02 8.46E-02 3.8
CATMA5a43340 AT5G47390 myb-family transcriptionfactor, contains Pfamprofile: PF00249 myb-likeDNA-binding domain
17.9 1.28E-02 7.46E-02 21.3 6.9 5.77E-02 1.91E-01 21.2
CATMA5a47752 AT5G51810 Encodes GA 20-oxidase(GA20OX2). Involvedin GA biosynthesis.Up-regulated by far-redlight in elongating petioles.Not regulated by acircadian clock.
47.8 1.59E-03 2.52E-02 22.4 49.9 1.45E-03 2.30E-02 22.4
CATMA5a56800 AT5G61150 VIP4, highly hydrophilicprotein involved inpositively regulatingFLC expression; leo1-likefamily protein, weak simi-larity to SP:P38439LEO1 protein (Saccharo-myces cerevisiae); containsPfam profile PF04004:Leo1-like protein
17.6 7.95E-03 5.51E-02 1.4 1.2 3.26E-01 5.10E-01 1.1
CATMA5a57025 AT5G61380 ABI3-INTERACTINGPROTEIN1 (AIP1), identicalto pseudo-response regulator1 GI:7576354 fromA. thaliana; TOC1GI:9247019; containsPfam profile PF00072;response regulator receiverdomain
13.5 8.54E-03 5.58E-02 21.5 13.4 8.72E-03 6.91E-02 21.5
CATMA5a61115 AT5G65790 myb-family transcriptionfactor (MYB68), identicalto GI:3941493 fromA. thaliana; containsPfam profile: PF00249myb-like DNA-bindingdomain
103.2 5.56E-04 1.38E-02 1.6 69.4 1.19E-03 2.19E-02 1.5
CATMA5a63030 AT5G67580 myb-family transcriptionfactor, contains Pfamprofile: PF00249 myb-likeDNA-binding domain
47.9 1.17E-03 2.16E-02 22.6 30.9 3.00E-03 4.16E-02 22.2
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Talon et al., 1990; Chiang et al., 1995) and a gene en-coding GA 20-oxidase (GA20OX2; At5g51810; Xuet al., 1995), were down-regulated in SE14 comparedto PL, whereas genes encoding RGL1, a GA regulatoryprotein that represses GA signaling (Wen and Chang,2002), and SLY1 (At4g24210), a gene involved in reg-ulation of GA signaling (Dill et al., 2004), were up-regulated in the late-flowering accession SE14compared to PL. In addition, six myb-family transcrip-tion factors whose expression is affected by GA (Chenet al., 2006) were differentially expressed across acces-sions. Three of these were up-regulated in accessionSE14 (At5g65790, At5g02840, and At5g37260), and threewere down-regulated (At3g46130, At5g67580, andAt5g47390).
Other differentially expressed candidate genes forflowering included two genes in the vernalizationpathway: VIP4 (At5g61150) and FRL1 (At5g16320),both involved in regulation of FLC expression (Zhangand van Nocker, 2002; Michaels et al., 2004), and thefloral repressors EMF (At5g11530; Moon et al., 2003)and SVP (At2g22540; Hartmann et al., 2000; Gregiset al., 2006; Table II; Fig. 3). The floral pathway inte-grator SOC1 (AGL20; At2g45660; Lee et al., 2000; Moonet al., 2003) was also differentially expressed, with alower level of expression in accession SE14 than in PL(Table II; Fig. 3).
Microarray data for an additional 10,859 probeswere also analyzed for differential expression. Theexpression of a total of 1,642 differed significantly be-tween groups at 10% FDR. The largest difference ingene expression was found between nonvernalizedseedlings of accessions PL and SE14 (PLNV versusSENV, 1,493 genes). Fewer genes were differentiallyexpressed between vernalized seedlings of the twoaccessions (PLV versus SEV, 874 genes), and very fewgene expression differences were found between ver-nalized and nonvernalized seedlings (PLV versusPLNV, and SEV versus SENV, two genes). However,GO annotation of the 1,642 genes indicates that mostof these genes function in various biological proces-ses with no obvious relation to control of floweringtime (Supplemental Appendix S3). Genes differen-tially expressed by vernalization encode a Gly-rich,endomembrane-located protein (At4g29030) and amicrotubule-associated protein (MAP70-1) that havenot been implicated previously in the vernalizationresponse.
List Enrichment Analysis
We used list enrichment analysis to assess whetherthere was an overrepresentation of differentially ex-pressed genes in GO categories of relevance to flower-ing time (see ‘‘Materials and Methods’’). We found asignificant overrepresentation of significantly differ-entially expressed genes in the category ‘‘circadianrhythm’’ (20 genes in category, seven significant, two-sided P 5 2.3 3 1022, Fisher’s exact test). There wasalso a significant overrepresentation of genes involved
in GA metabolism and signaling (49 genes in cate-gory, 13 significant at FDR 0.1, two-sided P 5 4.23 3 1022,Fisher’s exact test).
Chromosomal Clustering of Differentially Expressed
Genes on Ancestral Chromosome 4
To determine whether the positions of differentiallyexpressed genes were random or clustered, we exam-ined the chromosomal position of each differentiallytranscribed probe, based on the A. thaliana genomeannotation. We found that part of A. thaliana chromo-some 2, corresponding to ancestral chromosome 4(ak4) in Capsella (Schranz et al., 2006), had a signifi-cantly higher proportion of differentially expressedgenes in the PL-SE14 comparison than overall in thegenome (0.185 of genes significant for ak4, 0.152 sig-nificant for all detected genes, x2 5 9.00, d.f. 5 1, P 52.7 3 1023). This region of A. thaliana chromosome 2constitutes an entire, separate chromosome in bothA. lyrata and Capsella rubella. In A. thaliana, it corre-sponds to approximately 10 Mb of the lower part ofchromosome 2 (delimited by the loci At2g21160 andAt2g47730) containing a total of 2,867 annotated loci.In this study, 1,235 of these were labeled ‘‘present’’ and228 were differentially expressed. In the US721-US740comparison, we found no overrepresentation of dif-ferentially expressed genes for ak4.
Verification of Differential Expression
We selected four genes for verification of the micro-array results (SUPPRESSOR OF OVEREXPRESSIONOF CONSTANS1 [SOC1], TOC1, CCA1, and FLC).Although FLC was not differentially expressed aftercorrection for multiple testing, there was some evi-dence for differential expression (P 5 0.03), and theliterature on this gene as well as the vernalizationresponse led us to include it in our panel. Real-timereverse transcription (RT)-PCR DCT values for dif-ferentially expressed candidate genes (SOC1, TOC1,CCA1) were consistent with array results (Supplemen-tal Appendix S4). Thus, we did not identify any falsepositives among the genes assessed. Analysis of real-time RT-PCR gene expression measurements indicatedthat FLC expression did not differ between accessionsprior to vernalization and was diminished after ver-nalization in both accessions, but to a greater extentin SE14.
Flowering Time Ecotypes Differ in Rhythmic Expressionof CCA1 and TOC1
Because the microarray data analysis indicated thatcircadian core genes were differentially expressed, weset up two experiments to assess differences in theexpression of circadian genes over time. The rhythmicexpression of the circadian core oscillator genes TOC1and CCA1 differed between accessions PL and SE14
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under both constant light and long-day conditions(Fig. 4).
Independent Biological Validation ofDifferential Expression
Independent biological validation of differentiallyexpressed flowering time genes was obtained in a pairof less extreme flowering ecotypes, representative ofthe average part of the flowering time distribution(Fig. 1; accessions US721 and US740). Gene expressionmicroarray analysis (see ‘‘Materials and Methods’’)indicated that a total of 97 probes for flowering timegenes, including probes for 18 of the 21 differentiallyexpressed flowering time genes in the PL-SE14 com-parison, were in common between experiments (Sup-plemental Appendix S5). As an independent biologicalvalidation, we asked whether the set of 18 floweringtime genes that were differentially expressed betweenthe extreme flowering ecotypes also had evidence fordifferential expression in the US721-US740 compari-son. Out of 33 significant contrasts between accessions
for these genes in the PL-SE14 comparison, 12 con-trasts corresponding to eight different genes were alsosignificant in the US721-US740 comparison (Table III).These genes included circadian core genes such asLHY and TOC1, as well genes involved in GA biosyn-thesis and response (e.g. GA4, RGL1, MYB48, and themyb-family transcription factor At5g02840); FRL1, agene involved in the vernalization response; and SVP,a floral repressor (Table III). Overall, this constitutesgood agreement between experiments and indicatesthat flowering ecotypes with intermediate differencesin flowering time also differ in the expression of genesregulating circadian rhythm and GA biosynthesis andresponse.
DISCUSSION
In this study we have characterized differential geneexpression between flowering ecotypes of C. bursa-pastoris, to test whether gene regulation differences inknown flowering time genes in Arabidopsis are also
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. Genesthat were up-regulated in SE14 compared to PL are marked in green, and genes down-regulated in SE14 are marked in magenta.
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responsible for natural variation in flowering time inC. bursa-pastoris. In the close relative A. thaliana, amajor part of natural flowering time variation is due tomultiple independent mutations in the FRI gene, thefunction of which is to induce FLC expression that inturn represses the transition to flowering (Johansonet al., 2000). In our experiment, quantitative RT-PCRanalysis of FLC expression showed that FLC was in-deed down-regulated in both early- and late-floweringaccessions as a result of vernalization, but did notdiffer significantly in expression between accessionsbefore vernalization. Thus, although it seems likelythat the function of FLC as an important mediatorof the vernalization response is conserved acrossA. thaliana and C. bursa-pastoris, our data shows thatsimilar mutations as those found in A. thaliana FRIhave not been important in generating natural flower-ing time variation in the C. bursa-pastoris accessions wehave studied. Other pathways and genes are morelikely responsible for natural variation in floweringtime in this species. Microarray analysis of differentialexpression between early- and late-flowering C. bursa-pastoris offered some insight as to which pathwaysthese may be. Indeed, we found a significant enrich-ment of differentially expressed genes in two of themain A. thaliana flowering time pathways, the GApathway and the photoperiodic pathway, and, morespecifically, circadian clock-related genes in the latter.The fact that different pathways seem responsible fornatural flowering time variation in A. thaliana and thestudied accessions of C. bursa-pastoris could suggestthat these species have experienced different selectiveconstraints on flowering time, or that genetic variationat flowering time genes differed between species, pro-viding different avenues to variation in flowering time.
In A. thaliana, variation in circadian rhythm amongnatural accessions contributes to fitness (Dodd et al.,2005), is correlated with latitude of origin (Michaelet al., 2003), and can cause variation in flowering time(Imaizumi and Kay, 2006). Because differences in geneexpression, especially for genes with circadian expres-sion, may be difficult to interpret based on data from asingle 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 circadiangenes, CCA1 and TOC1. Both of these genes differed indiurnal expression between the early-flowering PLand the late-flowering SE14 ecotype. In A. thaliana,changes in rhythmic expression of CCA1 or TOC1 haveeffects on flowering time (Strayer et al., 2000; Alabadiet al., 2001; Mizoguchi et al., 2002). Interestingly, in ourmicroarray experiment, CKB4, which encodes a regu-latory subunit of casein kinase II and leads to changesin circadian period and phase in A. thaliana whenoverexpressed (Perales et al., 2006), was up-regulatedin the early-flowering accession PL. Circadian rhythmis a crucial component in the now generally acceptedexternal coincidence model (Bunning, 1936). In a mo-lecular version of this model, the circadian clock gen-erates daily oscillation of CONSTANS (CO) mRNA. Asprotein stability of CO is controlled by light, the coin-cidence of light and high CO expression that onlyoccurs in long days induce the pathway integrator FTand thereby flowering (Valverde et al., 2004; Corbesieret al., 2007). Thus, alterations in genes controlling thecircadian clock are attractive candidates for the evo-lution of flowering time differences in C. bursa-pastoris.
The GA pathway was also enriched for differentiallyexpressed genes among the early-flowering accessionPL and the late-flowering accession SE14. In A. thaliana,
Figure 4. Normalized expressionlevels (CTtarget 2 CTreference) for CCA1and TOC1 at 12 time points fol-lowing entrainment. Mean expres-sion levels are indicated by blacksymbols for accession PL and whitesymbols for accession SE14. Errorbars indicate SE of the mean. Thetop pair of plots shows CCA1 andTOC1 expression in constant light,and the bottom pair shows theirexpression under long-day condi-tions.
Differential Expression and Adaptation in Capsella
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the GA pathway is generally considered as a defaultpathway acting mainly when flowering is not inducedby long days. Although gene expression differencesfor genes in the GA pathway might be important forflowering time variation in C. bursa-pastoris, an attrac-tive alternative hypothesis is that these expressiondifferences are a secondary effect of altered circadianclock function, and that this altered clock functionaffects flowering time mainly through other pathways(e.g. through CO and FT). In this study, two GAbiosynthesis genes displayed a higher expression inearly flowering accession PL as compared to SE14,which might well be an effect of altered clock function(Blazquez et al., 2002). Blazquez et al. (2002) furtherconcluded that GA contribution is not quantitativelyimportant in the determination of flowering time bythe photoperiod pathway in A. thaliana. Rather, theincrease in GA concentration induced by long daysmight be relevant for cell expansion required duringstem elongation, rather than the determination offlowering time.
Differential expression of flowering time genes wasbiologically validated in a pair of less extreme flower-ing ecotypes from North America. The good agree-ment of flowering time gene expression differencesbetween both pairs of accessions could indicate thatthe genetic basis of expression differences is shared bycommon ancestry, or that similar regulatory differ-ences have evolved in parallel. Although the two pairsof accessions were sampled in widely different geo-graphical regions (the extreme flowering ecotypes PLand SE14 from Taiwan and Sweden, respectively, and
the less extreme flowering accessions US721 andUS740 from the United States), a shared genetic back-ground is not unlikely, as the species has apparentlyattained its present distribution recently (Ceplitiset al., 2005). Indeed, both early- and late-floweringC. bursa-pastoris accessions were introduced into NorthAmerica by European settlers (Neuffer and Hurka,1999). To resolve the genetic basis of gene expressiondifferences, a natural extension of this study is to mapgene expression as a quantitative trait, as has been donee.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 acrossaccessions, and not as a result of the vernalizationtreatment, although vernalization had an effect onflowering time. This could indicate that vernalizationaffected the expression of very few genes, or that theeffect on gene expression was generally small so thatwe had limited power to detect these differences.Similar results have been obtained in other species,for example, in Lolium perenne, where cDNA micro-array analysis identified only a handful of genes dif-ferentially expressed as a result of vernalizationtreatment (Ciannamea et al., 2006). In A. thaliana, sev-eral known components of the vernalization pathwayare not themselves regulated by vernalization (VRN1,VRN2) or regain their normal level of expression uponreturn to warmer temperatures (VIN3; Levy et al., 2002;Wood et al., 2006). Indeed, localized modification ofFLC chromatin may be the main underlying mechanismfor vernalization response in A. thaliana (Bastow et al.,
Table III. Independent biological validation of differentially expressed flowering time genes
The table shows the contrast P values for the independent biological validation of differentially expressedflowering time genes, with contrasts significant in the PL-SE14 comparison written in italics. The groupdesignations are as follows: nonvernalized accession US721 (721NV), nonvernalized accession US740(740NV), vernalized accession US721 (721V), and vernalized accession US740 (740V).
Locus Tag Gene ProductContrast P Value
721NV versus 740NV 721V versus 740V
AT1G01060 LHY 0.0494 0.2340AT1G15550 GA4 0.0137a 0.0379AT1G66350 RGL1 0.0399 0.188AT2G18170 AtMPK7 0.929 0.142AT2G22540 SVP 0.0117a 0.00344a
AT2G44680 CKB4 0.0805 0.704AT2G45660 SOC1 (AGL20) 0.795 0.469AT2G46830 CCA1 0.204 0.585AT3G46130 MYB48 0.00496a 0.000931a
AT4G24210 SLY1 0.693 0.0723AT5G02840 myb-family transcription factor 0.0272 0.494AT5G11530 EMF1 0.448 0.650AT5G16320 FRL1 0.0561 0.0308AT5G47390 myb-family transcription factor 0.778 0.274AT5G61150 VIP4 0.280 0.849AT5G61380 TOC1 0.000423a 0.00419a
AT5G65790 MYB68 0.681 0.330AT5G67580 myb-family transcription factor 0.724 0.491
aP values that meet a 10% FDR criterion.
Slotte et al.
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Copyright © 2007 American Society of Plant Biologists. All rights reserved.
2004; He et al., 2004; Sung and Amasino, 2004; Shindoet al., 2006; Swiezewski et al., 2007). Interestingly, weidentified 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 expressionchanges are involved in vernalization is unclear, butthey could be related to cold acclimatization becausechanges in membrane composition and cytoskeletal or-ganization are both believed to play a role in this pro-cess (Browse and Xin, 2001).
Most of the differentially expressed genes werescattered across different chromosomal regions. How-ever, the proportion of significant genes (out of alldetected genes) was higher than expected for ancestralchromosome 4, which corresponds to the lower partof A. thaliana chromosome 2 (Schranz et al., 2006). Noclear signs of amplification or deletion of specific chro-mosomal regions were observed, with approximatelyequal numbers of genes up- and down-regulated ineach flowering ecotype. Chromosome-scale transcrip-tional profiling in rice (Oryza sativa) and Arabidopsishas identified variation in transcriptional activity acrosschromosomes (Li et al., 2005; Schmid et al., 2005). Suchvariation has been shown to be correlated with tissueand developmental stage as well as external factorssuch as cold stress (Yamada et al., 2003). A recentstudy on gene expression diversity among genotypesin A. thaliana (Kliebenstein et al., 2006) also reported acorrelated variation of DNA sequence divergence andexpression variation along chromosomes. In C. bursa-pastoris, increased localized sequence divergence be-tween extreme flowering ecotypes or differences inchromatin structure between these accessions couldexplain the observed clustering of differentially ex-pressed genes.
In this study we have characterized gene expressiondifferences between early- and late-flowering acces-sions of C. bursa-pastoris. Flowering time variation mayhave evolved rapidly in this species and is probably ofadaptive importance (Ceplitis et al., 2005). We haveshown that natural variation in the C. bursa-pastorisflowering time ecotypes we have studied is likely notcaused by variation at the FRI gene, as in A. thaliana.Instead, the evolution of flowering time variationappears to have involved changes in the expressionof genes regulating the circadian rhythm, and possiblyalso regulatory changes in the GA pathway. Whilefurther study is needed to elucidate the full pathwayand mechanisms involved, genes involved in regula-tion of the circadian clock, such as CCA1 and TOC1,clearly constitute strong candidates for adaptive evo-lution 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. Approx-
imately 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 treat-
ment 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 mmol
m22 s21 and block 2 had a lower average light intensity of 180 mmol m22 s21).
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 mmol m22 s21), 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. Flower-
ing 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 sam-
pled 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 biolog-
ical replicates of each treatment accession combination. Sampling took place at
midday, 7 h after dawn. Sampling occurred in the same order as the ran-
domized block design and, therefore, the order of sampling was random with
respect to vernalization-treatment and accession. We measured gene expres-
sion in seedlings because previous studies have shown that several key flower-
ing 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), accord-
ing to the manufacturer’s instructions. Protocols for RNA amplification,
labeling, and hybridization were modified from those used by Wellmer et al.
Differential Expression and Adaptation in Capsella
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Copyright © 2007 American Society of Plant Biologists. All rights reserved.
(2004), and a detailed description is found in Supplemental Appendix S1.
Briefly, first-strand cDNA was synthesized using 5 mg of total RNA as
template, 0.5 mg 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 mL 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 mg) was reverse transcribed using random hexamer primers (In-
vitrogen) 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
53 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. Hybridiza-
tions were carried out at 42�C for a minimum of 60 h. Following posthybrid-
ization 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 poten-
tially 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 micro-
array 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 Yijkl 5 m 1
di 1 gj 1 rkl 1 eijkl 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 r with e is the random error. The mean over all
observations for a particular probe is m. We used the Shapiro-Wilk’s statistic
to test for deviation from normality of the residuals. Four pairwise con-
trasts 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 signif-
icant. Microarray data are deposited in ArrayExpress under accession num-
bers 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 re-
lated (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 path-
ways 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 nor-
malization, 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 mg of total RNA was reverse transcribed to cDNA using
random hexamer primers (Invitrogen) and SuperScript III reverse transcrip-
tase (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.
Slotte et al.
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Copyright © 2007 American Society of Plant Biologists. All rights reserved.
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 trans-
cription 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 refer-
ence and target genes (DCT) was calculated for each accession and treatment
combination.
Real-Time RT-PCR Assay for Time-Series Analysisof 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 inten-
sity of 52 mmol m22 s21 for 7 d, before release into either constant light (52 mmol
m22 s21) or continued long-day (52 mmol m22 s21) 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 de-
scribed 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 differ-
ential expression for flowering time genes.
ACKNOWLEDGMENT
We thank Mattias Myrenas and Myriam Heuertz for experimental assis-
tance.
Received May 23, 2007; accepted July 10, 2007; published July 13, 2007.
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