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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Nat Genet. Author manuscript; available in PMC Oct 5, 2006.
Published in final edited form as:
Published online May 28, 2006. doi:  10.1038/ng1818
PMCID: PMC1592229
NIHMSID: NIHMS12489

The PHYTOCHROME C photoreceptor gene mediates natural variation in flowering and growth responses of Arabidopsis thaliana

Abstract

Light plays an important role in modulating seedling growth and flowering time1. We show that allelic variation at the PHYTOCHROME C (PHYC) photoreceptor locus affects both traits in natural populations of A. thaliana. Two functionally distinct PHYC haplotype groups are distributed in a FRIGIDA-dependent latitudinal cline that is stronger than the one reported for FLOWERING LOCUS C, which together with FRIGIDA explains a large portion of the variation in A. thaliana flowering time2. In a genome-wide scan for association of 65 loci with latitude, there was an excess of significant p-values, indicative of population structure. Nevertheless, PHYC was the most strongly associated locus across 163 strains, suggesting that PHYC alleles are under diversifying selection in A. thaliana. Our work, together with previous findings36, suggests that photoreceptor genes are major agents of natural variation in plant flowering and growth response.

Arabidopsis thaliana occurs across a wide range of latitudes in the northern hemisphere, and wild strains display vary extensively in light sensitivity and flowering responses. FRI and FLC, which mediate the effects of exposure to winter-like temperatures (vernalization), account for about 23% of the phenotypic variation in flowering time of A. thaliana in short days79. In contrast to FRI, alleles of other loci that modulate variant flowering responses tend to be rare35,10. In addition, most of the characterized natural alleles affect either seedling growth or flowering time, even though both traits are light dependent. Linkage disequilibrium mapping studies suggest that common allelic variation at the CRYPTOCHROME2 (CRY2) photoreceptor locus is associated with flowering time variation, but this has not yet been experimentally verified6.

We have recently identified an accession from Frankfurt, Germany, Fr-2, which responds only weakly to a change in photoperiods. Compared to standard laboratory strains such as Col-0, Fr-2 flowers early in short days. This phenotype shows simple segregation in an F2 population derived from a cross of Fr-2 to Col-08. By performing whole-genome scans of 140 early flowering individuals selected from 900 F2 plants, we mapped the earliness to a 1 Mb region on chromosome 5 (Supplementary Fig. 1 online). Within this interval, we found 11 genes that were differentially expressed between Col and Fr-2 (Fig. 1a), as deduced from AtGenExpress microarray data of 34 wild strains8. Because none of the other strains showed an Fr-2 like flowering behavior, and because the early flowering phenotype was recessive, we asked whether any of the 11 genes was specifically underexpressed in Fr-2. Only one gene, which encodes the red-light receptor PHYC, fulfilled this criterion (Fig. 1b).

Figure 1
Identification of a defective PHYC allele in Fr-2. (a) Expression profiles of genes that are differentially expressed between Fr-2 and Col across 34 wild strains. The genomic interval that co-segregates with early flowering of Fr-2 were analyzed for differentially ...
Supplementary Figure 1
Markers used for mapping of the early flowering phenotype in Fr- 2. About 80 early plants were genotyped for all markers and linkage of the phenotype to MSat 5.22 and SO191 was confirmed with 60 additional early plants.

PHYC knockout alleles not only cause early flowering in short days, but also confer reduced sensitivity to red light during seedling growth11,12. Consistent with a defect in PHYC, Fr-2 seedlings responded to red light across a range of fluences less strongly than Col-0 seedlings, similar to plants with a mutation in the canonical red-light receptor PHYB (Fig.1c). In an F1, Fr-2 alleles complemented the flowering phenotypes of phyA and phyB mutants induced in the Ler background, indicating that PHYA and PHYB alleles of Fr-2 are functional (not shown). In contrast, Fr-2 failed to complement the flowering phenotype of a phyC knockout allele in Col-0, confirming that the early flowering of Fr-2 is largely due to a defect in PHYC (Fig. 1d).

Sequencing of a PHYC cDNA from Fr-2 revealed a nonsense change in the first exon, which converts the K299 codon to a stop codon. The predicted Fr-2 PHYC protein therefore lacks half of the GAF domain, and the entire PHY, PAS and histidine kinase domains, all of which are typically required for phytochrome function. Premature stop codons can trigger non-sense mediated mRNA decay13, which may explain the reduced levels of PHYC mRNA detected on microarrays14.

In addition to the stop codon, the Fr-2 PHYC allele is highly polymorphic with 12 nonsynonymous changes compared to Col-0. Ten of these substitutions are present in another common laboratory strain, Ler15. Many screens for seedlings with reduced light responses have been carried out in the Ler background, but phyC alleles were never recovered, suggesting that the Ler PHYC allele may have limited activity. Earlier overexpression studies of PHYC are in agreement with this hypothesis. Overexpression of Col-0 PHYC in Arabidopsis resulted in reduced hypocotyl length under red light. In contrast, while overexpression of Ler PHYB in transgenic tobacco plants also lead to shorter hypocotyls, Ler PHYC had no effect on hypocotyl length in red light16,17.

If the Ler PHYC allele is less active than that of Col-0, PHYC should at least partially explain the earlier flowering of Ler in short days. Indeed, a quantitative trait locus (QTL) that maps to the PHYC region has been detected in Ler x Col recombinant inbred lines (RILs) grown under natural conditions in fall, when days are short18. Consistent with a similar genetic basis of early flowering under short days in Fr-2 and Ler, a Ler x Fr-2 F2 population showed a near normal distribution of flowering time in short days, which contrasts with the behavior of a Col-0 x Fr-2 F2 population (Supplementary Fig. 2a online). Genotyping of F2 populations derived from Fr-2 x Col or Fr-2 x Ler revealed that the average flowering time of plants that carry Col-0 PHYC was much higher than that of plants with Fr-2 or Ler alleles (Supplementary Fig. 2b online). Furthermore, the PHYC allele of Ler failed to fully complement the phyC knockout in the Col-0 background. Fr-2 x Ler F1 plants flowered early in short days, in agreement with the limited activity of Ler PHYC (Supplementary Fig. 2c online). Finally, quantitative complementation analyses showed that phenotypic differences due to allelic variation between Fr-2 and Ler PHYC were less than those observed between Col-0 and either Fr-2 or Ler (Fig. 2).

Figure 2
Quantitative complementation analysis with different parental lines. Col-0 was used as the wild type and the null allele phyC-2 in the Col-0 background as a tester for the crosses. An ANOVA was performed with the following model: TLN~ Line + Cross + Line ...
Supplementary Figure 2
Flowering behavior of populations segregating for different PHYC alleles. (a) Distribution of flowering time in an F2 population derived from Ler x Fr-2 cross showing a continuous distribution, in contrast to a bimodal distribution observed in an F2 population ...

Since both the sequence of PHYC in Col, Ler, and Fr-2 and the genetic data suggested functionally distinct PHYC alleles, we studied the haplotype structure at the PHYC locus in more detail. We sequenced PHYC from a randomly chosen sample of 29 wild strains. Phylogenetic analysis, using the sister species A. lyrata as outgroup, indicated two major haplotype classes, with Ler in one class and Col-0 in the other (Fig. 3). A total of 40 SNPs that result in eight informative amino acid changes were in complete linkage disequilibrium among the two haplotype groups (Fig 3). In addition, the two haplotype groups were distinguished by an indel 500 bp upstream of the start codon.

Figure 3
PHYC haplotypes. A representative unrooted phylogenetic tree generated from PHYC coding sequences is shown on top. Numbers indicate bootstrap values above 60. The amino acid changes that delineate the two haplotype groups are given below. Unique changes ...

Mapping experiments have detected QTL for flowering time and hypocotyl length in many RIL populations across multiple environments. Haplotype analysis on multiple crosses has been previously used to identify candidate genes for a QTL19. In a reverse approach, we predicted that if the two PHYC haplotypes are distinguished by their activity, variation in flowering time and hypocotyl length should map to PHYC in crosses derived from parental lines with contrasting haplotypes. Using the promoter indel as a diagnostic marker for the two haplotype groups, we assessed the PHYC haplotype of more than 220 Eurasian strains (Supplementary Table 1 online), and then examined published QTL maps from six different crosses for flowering time and hypocotyl length10,18,2024. As predicted, only populations in which the parents carried different haplotypes at PHYC showed a QTL near PHYC (Table 1). The direction of the QTL effect in all crosses was consistent with the Ler-like haplotype being less active than the Col-like haplotype. In agreement with the known function of PHYC, the QTL depend on the environment, with the light sensitivity QTL being detected under red light and the flowering time QTL in short days.

Table 1
Summary of QTL in existing RIL sets.
Supplementary Table 1
PHYC haplotypes of A. thaliana strains.

We also asked whether PHYC haplotype groups could account for variation in hypocotyl lengths of 115 wild strains, which we had previously analyzed in several different light conditions5. We found that PHYC could explain 8% of the variation in white light, and that PHYC was significantly associated with hypocotyl lengths across several conditions (Table 2). All these results support the hypothesis that PHYC is responsible for the flowering time and hypocotyl length QTL that have been reported in this genomic region. We note, however, that a tightly linked locus could contribute to these results.

Table 2
Associations of PHYC with hypocotyl lengths across range of conditions.

That the less active PHYC haplotype group is quite common suggests that variation at this locus may be adaptive in the wild. We therefore asked whether the frequency of PHYC haplotypes varies with latitude, since co-variation with environmental factors can be evidence of adaptation25. Latitudinal clines in light sensitivity and flowering time are known in A. thaliana5,8,26. Indeed, we found that the more active Col-0 PHYC haplotype group was more frequent at northern latitudes (p = 0.0001, n = 221). This is particularly striking among strains that do not carry obvious lesions in FRI (Fig. 4a). In addition, the contribution of PHYC to flowering time is latitude-dependent. A PHYC x latitude interaction can explain 10% of the residual variation in short day flowering, after accounting for effects of FRI, suggesting that PHYC partially contributes to the late flowering of FRI positive strains at northern latitudes (Supplementary Table 2 online). Since latitudinal associations could also be due to population structure, we asked how often significant interaction terms were observed in 163 strains for a set of 65 SNPs that have comparable allele frequencies and that are spaced throughout the genome27. The same analysis was carried out for FLC, which has been reported to show a FRI-dependent latitudinal cline independent of population structure28. The p-values for the association of PHYC ranked first for latitude (Fig. 4b) and fourth for the FRI-dependent interaction with latitude (Fig. 4c). In addition, it always ranked ahead of the corresponding p-values for FLC. PHYC was also the highest ranking marker for association with hypocotyl length after gibberellin treatment5 (Supplementary Table 3 online). While there is an excess of significant p-values among the random loci, which is consistent with the effect of population structure, the relative ranks of the PHYC p-values are suggestive of adaptive selection29 (Supplementary Table 3 online). A similar analysis with an independent set of 69 SNP markers, even with a much smaller sample size of 56 strains, yielded comparable results (Supplementary Table 4 online).

Figure 4
Latitudinal cline of PHYC alleles. (a) Proportion of Ler-type (black) and Col-0-type (white) PHYC alleles at different latitudes among apparently FRI functional strains. The absolute numbers for each of the classes is given on top of the histograms. (b) ...
Supplementary Table 2
ANOVA of latitude by PHYC haplotype group interaction on residual variation in flowering time at 23°C in short days after accounting for FRI functionality. The significance for each of the factors is given.
Supplementary Table 3
p-values obtained from for different models testing for association with latitude (Latitude), FRI dependent interaction with latitude (Interaction), hypocotyl length under GA treatment (GAHypo) and total leaf number in short days (TLNSD) for 67 markers ...
Supplementary Table 4
p-values obtained from for two different models testing for latitudinal association with a completely independent set of markers across 56 strains. (A) Model: Latitude ~ FRI functionality + SNP + SNP * FRI functionality. The p-values for the entire model, ...

Of the informative SNPs that distinguish the PHYC haplotype groups, 20 Col-0 SNPs are shared with A. lyrata, as are 20 Ler-SNPs. Of the 8 amino acid changes that characterize the Col-0 and Ler haplotypes, four are clustered in a small region that seems to have regulatory activity in both PHYA and PHYB30,31. These changes could in principle be responsible for the functional difference between the haplotypes, although both specify unique amino acids at positions that are conserved in other phytochromes, not only in Arabidopsis, but also in land plants in general.

PHYC appears to have arisen by duplication of PHYA, which encodes a far-red light receptor. phyC knock out alleles in Col-0 and Ws backgrounds have phenotypes mostly in red and blue light11,12, but we found phenotypic associations even in far-red light. Our results could indicate differential cross talk among photoreceptors, dependent on PHYC haplotype. Alternatively, our understanding of phyC mediated light perception may have been limited by having only knock out alleles in Col-0 and Ws, which share the same haplotype.

While PHYC is mostly far-red sensing in rice, in Arabidopsis it is functionally more similar to the red-light sensing PHYB11,12,32,33. Nevertheless, loss of PHYC has similar consequences in Arabidopsis and rice, namely early flowering under non-inductive conditions12,33. This suggests that the functional divergence of PHYC from PHYA varies in different taxa. Consistent with this idea, PHYA evolves faster than PHYC in some species, but PHYC sequences are more divergent than those of other phytochrome genes in Arabidopsis, tomato and sorghum3436. PHYC has even been suggested as a target for adaptive evolution in sorghum35. In Arabidopsis, allelic variation with demonstrated functional effects appears to be more pervasive at PHYC than at other photoreceptor loci35. The functional role of allelic variation at PHYC in other species is therefore an important question for future research.

Materials and Methods

Plant material and phenotyping

Seed stocks and growth conditions have been described8. phyC insertion lines were obtained from the Nottingham Arabidopsis Stock Centre. Typically, 120 F2 plants were used to analyze flowering time, as measured by counting total leaf number on the main shoot. For complementation studies, 30–50 F1 plants each were examined. For hypocotyl length measurements, about 20 seeds were stratified on Murashige-Skoog medium for four days at 4°C in dark, and then transferred to growth chambers at 23°C. A week later, plants were flattened and imaged on a flatbed scanner. Hypocotyls lengths were measured using NIH Image.

DNA Analyses

Genomic DNA was amplified with Pfu polymerase (Fermentas), and products pooled from two to four independent reactions were directly sequenced. See Supplementary Table 5 online for oligonucleotide primers. A 4.9 kb region covering the promoter and almost the entire coding region was obtained from 26 A. thaliana strains, the same fragment without the 1.5 kb promoter from three A. thaliana and one A. lyrata strain (GenBank accession numbers XXXX-XXXXX).

Supplementary Table 5
Oligonucleotide primers.

Phylogenetic Analysis

Sequences were aligned using Seqman (DNA Lasergene Inc, USA) and alignment was verified manually. Diversity measurements were obtained with DnaSP v4.10 (http://www.ub.es/dnasp)37. Sequence alignments were imported into PAUP38 and a heuristic search with maximum likelihood was performed using the settings for HKY model specified through Modeltest39. A maximum parsimony search was performed with ACCTRAN character state optimization. In both methods, initial trees were generated through step-wise addition. TBR branch swapping option was used. 1000 bootstrap permutations were performed using the same search settings used for parsimony search with the full heuristic search option in PAUP. Parsimony and maximum likelihood resulted in similar trees. The maximum parsimony tree is shown in Fig. 3. The same split in the tree was obtained when using coding or non-coding regions.

Statistical Analyses

Data were analyzed using JMP (version 5.1, SAS Institute), Excel (Microsoft) or R (http://www.r-project.org). For the latitudinal association studies, only Eurasian strains were used. The larger set made use of SNP genotyping information published in ref. 27. Information on the independent SNPs used in the second, smaller analysis is available at http://naturalvariation.org. A nominal logistic regression model with genotype as the response and latitude as a factor was used to compute p-values for latitudinal cline. For testing the interaction with FRI, a simple linear regression model with latitude as the response, and FRI and the genotype of PHYC or a random SNP as interaction term was used. The intrinsic multiple testing problem associated when comparing PHYC with random SNPs is somewhat mitigated by the fact that of 43 SNPs in PHYC, 40 are fixed in the two haplotype groups. For association with flowering time, residuals on regression with flowering time as the response and FRI functionality as a factor, were regressed using PHYC haplotype group and latitude as factors with interaction. PHYC x latitude interaction accounted for about 10% of the variation in the residuals. For association with hypocotyl length, a single factor ANOVA was performed with PHYC haplotype group as the factor and hypocotyl lengths as response. Previously published flowering time and hypocotyls length measurements were used for association studies5,8.

Supplementary Figure 3
Latitudinal cline of PHYC alleles. (a) Distribution of p-values of a nominal logistic regression model with latitude as a factor and genotype as response. Allele information of 69 random SNP markers with similar allele frequency as that of PHYC was available ...
Supplementary Table 6
Strains sequenced for PHYC.

Acknowledgments

We thank the Nottingham Arabidopsis Stock Center for seed stocks of SALK T-DNA lines donated by Joe Ecker and colleagues. We thank Christa Lanz for help with sequencing, and Kirsten Bomblies and Ya Long Guo for help with phylogenetic analysis. We thank Kirsten Bomblies, Vava Grbic, Yasushi Kobayashi, Janne Lempe and Stephen Russell for discussion and critical reading of the manuscript, and the anonymous reviewers for insightful comments that led to improvements of the manuscript. Supported by an EMBO Long-Term Fellowship to S.B., an NIH Postdoctoral Fellowship to T.P.M., an NIH grant (GM62932) to J.C. and D.W., and by the Max Planck Society, of which D.W. is a director. J.C. is an HHMI Investigator.

S.B., J.C and D.W. conceived the experiments; S.B., S.S., M.A., T.P.M., C.W, and J.N.M performed the experiments; S.B., S.S., J.N.M., R.C., N.W., J.C and D.W. analyzed the data and wrote the manuscript.

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