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Temperature-dependent regulation of flowering by antagonistic FLM variants

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Abstract

The appropriate timing of flowering is crucial for plant reproductive success. It is therefore not surprising that intricate genetic networks have evolved to perceive and integrate both endogenous and environmental signals, such as carbohydrate and hormonal status, photoperiod and temperature1,2. In contrast to our detailed understanding of the vernalization pathway, little is known about how flowering time is controlled in response to changes in the ambient growth temperature. In Arabidopsis thaliana, the MADS-box transcription factor genes FLOWERING LOCUS M (FLM) and SHORT VEGETATIVE PHASE (SVP) have key roles in this process3,4. FLM is subject to temperature-dependent alternative splicing3. Here we report that the two main FLM protein splice variants, FLM-β and FLM-δ, compete for interaction with the floral repressor SVP. The SVP–FLM-β complex is predominately formed at low temperatures and prevents precocious flowering. By contrast, the competing SVP–FLM-δ complex is impaired in DNA binding and acts as a dominant-negative activator of flowering at higher temperatures. Our results show a new mechanism that controls the timing of the floral transition in response to changes in ambient temperature. A better understanding of how temperature controls the molecular mechanisms of flowering will be important to cope with current changes in global climate5,6.

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Figure 1: Temperature-dependent expression of FLM-β, FLM-δ and SVP.
Figure 2: FLM-SVP protein–protein interactions and DNA binding assays.
Figure 3: FLM and SVP are interdependent and regulate flowering time and flower morphology.

Accession codes

Accessions

Gene Expression Omnibus

Data deposits

ChIP-seq data have been deposited with NCBI Gene Expression Omnibus under accession number GSE48082.

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Acknowledgements

We thank F. Hohnstein, J. Weirich, C. Martín-Pizarro, J. Busscher-Lange, C. Lanz and J. Reixinger for technical assistance, A. Srikanth for BiFC vectors, and P. Huijser and B. Davies for seeds. Supported through the ERA-NET PLANT GENOMICS collaborative project BLOOM-NET by a grant from the Deutsche Forschungsgemeinschaft (SCHM 1560/7-1) and the MPI for Developmental Biology to M.S., a BLOOM-NET related NWO grant to R.G.H.I. and G.C.A., and a NWO/TTI-Green Genetics THERMOFLOW grant to R.G.H.I., G.C.A. and L.V.

Author information

Authors and Affiliations

Authors

Contributions

D.P. performed most experiments, L.V. performed EMSA assays, F.O. analysed ChIP-seq data, L.Y. and J.M. generated 35S:FLM-β/35S:FLM-δ constructs, L.Y. generated and phenotyped F1 Col-0 35S:FLM-β/35S:FLM-δ transgenic plants, G.C.A. contributed to the discussion, and D.P., R.G.H.I., L.Y. and M.S. supervised the work and wrote the manuscript.

Corresponding author

Correspondence to Markus Schmid.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Analysis of FLM splice variant expression in Col-0.

a, Graphic representation of the FLM-α, FLM-β, FLM-γ and FLM-δ transcripts, including exons (boxes) and introns (lines). Primers used for FLM-α (F1-R1), FLM-β (F1-R2), FLM-γ (F2-R1) and FLM-δ (F2-R2) amplification are shown. b, Semi-quantitative RT–PCR of FLM splice variants in Col-0 cDNA at different temperatures, using plasmids for each splice variant as controls (lanes 1–4).

Extended Data Figure 2 Distribution of flowering time of independent transgenic T1 lines established in this study.

ad, Flowering time of 35S:FLM-β and 35S:FLM-δ in Col-0 and flm-3 mutant background (a), flm-3 pFLM:gFLM and flm-3 pFLM:gFLM-GFP (b), flm-3 pFLM:iFLM-β, flm-3 pFLM:iFLM-δ, flm-3 pFLM:iFLM-β-GFP and flm-3 pFLM:iFLM-δ-GFP (c) and 35S:FLM-β-VP16 (d), grown under 16 °C, long-day are shown. Shaded areas mark the median and the 25% and 75% percentile of flowering time for a given genotype.

Extended Data Figure 3 Analysis of FLM co-suppression in 35S:FLM-δ.

a, Analysis of FLM-β, FLM-δ, MAF2-5, FLC and SVP in Col-0 control and 35S:FLM-δ #4. All genes except FLM-δ and MAF4 were expressed at similar levels in Col-0 and FLM-δ overexpression line. b, Flowering time of maf-4 (SALK_028506) is similar to that of Col-0 control plants, indicating that the MAF4 downregulation observed in a cannot explain the early flowering phenotype of the 35S:FLM-δ line. ce, Flowering time (c) and expression (d) of FLM-β, and expression of FLM-δ (e), as determined by qRT–PCR analysis in F1 populations from crosses between 35S:FLM-β and 35S:FLM-δ plants in both Col-0 and flm-3 backgrounds. d, FLM-β expression is not co-suppressed in response to the FLM-δ misexpression (e) in both Col-0 and flm-3 backgrounds. Rosette and cauline leaf numbers after bolting are represented in dark and light grey, respectively, in b and c. Error bars denote the s.d. of three biological replicates with three technical repetitions each in a, d and e.

Extended Data Figure 4 BiFC competition experiment.

ah, Microscope images (ad) and quantification of mCitrine-positive nuclei (eh). Increasing amounts (A600 nm; bottom) of an untagged version of one of the interactors tested were included in the assay. The number of BiFC-positive nuclei decreases with increasing amounts of the specific competitor: FLM-β–FLM-β homodimerization (a, e) and FLM-δ–FLM-β (b, f), SVP–FLM-β (c, g) and FLM-δ–SVP (d, h) heterodimerization.

Extended Data Figure 5 Flowering time of the agl74N T-DNA mutant.

Flowering time of Col-0, flm-3, and a homozygous agl74N T-DNA insertion line (SALK_016446).

Extended Data Figure 6 Graphic representation of iFLM-β/δ-(GFP) constructs, gBrowse traces of mapped ChIP-seq reads and validation of FLM targets.

a, iFLM-β/δ-(GFP) constructs representation including exons (boxes), introns included (black flat line) and introns missing (grey lines). Dashed boxes indicate presence only in the mGFP6-tagged constructs. be, Local enrichment of FLM, iFLM-β and iFLM-δ binding in ATC (b), RVE2 (c), SHP2 (d) and TEM2 (e). Chromosomal position (TAIR10) and models of the genes close to the peaks are given at the top of the panels. Each panel shows a 5-kb window. Forward reads are mapped above each line and reverse reads below. fh, qRT–PCR expression analysis of SEP3 (f), SOC1 (g) and TEM2 (h) in flm-3 mutant, Col-0 wild-type and a 35S:FLM-β transgenic line show how increasing levels of FLM-β downregulate SEP3 and SOC1 expression, but induce TEM2. Error bars in fh denote the s.d. of three biological replicates with three technical repetitions each.

Extended Data Figure 7 Venn diagram showing the number and overlap of FLM and SVP targets.

a, Overlap of loci bound in gFLM-GFP and iFLM-β-GFP ChIP-seq experiments with a false discovery rate (FDR) < 0.1 in all biological replicates. At this FDR, the high quality iFLM-β-GFP data set identifies 460 targets that are missing from the gFLM-GFP data set, which includes a replicate (#2) that contains substantial fewer uniquely mappable reads than the other replicates (see Supplementary Table 2). b, Overlap of loci bound in gFLM-GFP and iFLM-β-GFP ChIP-seq (FDR < 0.1) and SVP (FDR < 0.05) ChIP-chip assays21.

Extended Data Figure 8 EMSA assays and FLM-β/SVP BiFC competition experiment.

ac, EMSA assay with three sequences identified as binding-sites for SVP21 and FLM (this work) by ChIP-chip and ChIP-seq, respectively. SEP3 (a), SOC1 (b) and ATC (c) promoter probes that include two (a, b) or one (c) CArG motif(s) were used in EMSA. Different order complexes are represented by black arrowheads and asterisks for homo- or heterotetramers, respectively, and with grey arrowheads and asterisks for homo- or heterodimers, respectively. Orange and blue ellipses represent SVP and FLM-β proteins, respectively. d, e, Microscope images (d) and quantification (e) of mCitrine-positive nuclei. Increasing amounts (A600 nm; bottom) of untagged 35S:FLM-δ were added to FLM-β and SVP mCitrine-tagged vectors. A reduction in the number of BiFC-positive nuclei is observed with increasing amounts of competitor.

Extended Data Figure 9 Models of SVP–FLM complex function.

a, Temperature-dependent FLM splicing and genetic interactions of SVP–FLM-β heterocomplex in the flowering pathway. Strong binding of FLM to FT was observed in only one ChIP-seq replicate. Hence we propose that FLM–SVP downregulates FT expression in leaves indirectly through the induction of floral repressors transcription factors such as TEM2 and the AP2-like TOE3. The FLM–SVP complex contributes to the repression of floral transition by directly downregulating SOC1 and SEP3 expression, where SOC1 is a major floral activator. Arrows and block lines denote activation and repression, respectively. Dotted lines indicate a putative direct regulation. Rounded rectangles indicate proteins. b, Model of the temperature-dependent SVP–FLM complex function. Although SVP expression level is constant, FLM-β and FLM-δ levels are regulated in an antagonistic manner, with the former being the prevalent protein at low temperature and the latter dominating at high temperatures. At low temperatures SVP and FLM-β can interact, forming both homo- or heterocomplexes. The SVP-containing complexes are able to bind to the CArG boxes in the cis elements of important flowering related genes such as SEP3, SOC1, ATC, TEM2 and TOE3 and regulate their expression. When temperature increases, alternative splicing of FLM occurs, making FLM-δ the predominant splice variant. FLM-δ proteins compete with the remaining FLM-β and SVP proteins for complex formation. This results in the formation of non-functional SVP–FLM-δ complexes, which are impaired in their DNA-binding capability. The temperature-dependent splicing regulation of FLM occurs within 24 h, allowing the plant to quickly sense and respond to changes in ambient temperature, ensuring the switch between the non-flowering and flowering phase of development.

Extended Data Table 1 Mutants and transgenic lines used in this study.

Supplementary information

Supplementary Table 1

This file contains a yeast two-hybrid analysis of FLM-β and FLM-δ interactions with a collection of Arabidopsis MADS domain transcription factors. (XLSX 51 kb)

Supplementary Table 2

This table contains the read numbers for pFLM:gFLM-GFP. (XLSX 91 kb)

Supplementary Table 3

This table contains the read numbers for pFLM:iFLM-β-GFP. (XLSX 286 kb)

Supplementary Table 4

This file contains 232 common targets. Please note that pFLM:gFLM-GFP and pFLM:iFLM-β-GFP are applicable to this study. (XLSX 178 kb)

Supplementary Table 5

This file contains the Oligonucleotides used in this study. (XLSX 37 kb)

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Posé, D., Verhage, L., Ott, F. et al. Temperature-dependent regulation of flowering by antagonistic FLM variants. Nature 503, 414–417 (2013). https://doi.org/10.1038/nature12633

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