Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Temperature-dependent regulation of flowering by antagonistic FLM variants

Nature volume 503, pages 414–417 (2013)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

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.

References

  1. Srikanth, A. & Schmid, M. Regulation of flowering time: all roads lead to Rome. Cell. Mol. Life Sci. 68, 2013–2037 (2011)

    Article  CAS  Google Scholar 

  2. Posé, D., Yant, L. & Schmid, M. The end of innocence: flowering networks explode in complexity. Curr. Opin. Plant Biol. 15, 45–50 (2012)

    Article  Google Scholar 

  3. Balasubramanian, S., Sureshkumar, S., Lempe, J. & Weigel, D. Potent induction of Arabidopsis thaliana flowering by elevated growth temperature. PLoS Genet. 2, e106 (2006)

    Article  Google Scholar 

  4. Lee, J. H. et al. Role of SVP in the control of flowering time by ambient temperature in Arabidopsis. Genes Dev. 21, 397–402 (2007)

    Article  CAS  Google Scholar 

  5. Marcott, S. A., Shakun, J. D., Clark, P. U. & Mix, A. C. A reconstruction of regional and global temperature for the past 11,300 years. Science 339, 1198–1201 (2013)

    Article  ADS  CAS  Google Scholar 

  6. Mann, M. E. et al. Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia. Proc. Natl Acad. Sci. USA 105, 13252–13257 (2008)

    Article  ADS  CAS  Google Scholar 

  7. Chouard, P. Vernalization and its relations to dormancy. Annu. Rev. Plant Physiol. Plant Mol. Biol. 11, 191–238 (1960)

    Article  CAS  Google Scholar 

  8. Sheldon, C. C. et al. The FLF MADS box gene: a repressor of flowering in Arabidopsis regulated by vernalization and methylation. Plant Cell 11, 445–458 (1999)

    Article  CAS  Google Scholar 

  9. Michaels, S. D. & Amasino, R. M. FLOWERING LOCUS C encodes a novel MADS domain protein that acts as a repressor of flowering. Plant Cell 11, 949–956 (1999)

    Article  CAS  Google Scholar 

  10. Kumar, S. V. & Wigge, P. A. H2A.Z-containing nucleosomes mediate the thermosensory response in Arabidopsis. Cell 140, 136–147 (2010)

    Article  CAS  Google Scholar 

  11. Kumar, S. V. et al. Transcription factor PIF4 controls the thermosensory activation of flowering. Nature 484, 242–245 (2012)

    Article  ADS  CAS  Google Scholar 

  12. Franklin, K. A. et al. Phytochrome-interacting factor 4 (PIF4) regulates auxin biosynthesis at high temperature. Proc. Natl Acad. Sci. USA 108, 20231–20235 (2011)

    Article  ADS  CAS  Google Scholar 

  13. Rosloski, S. M. et al. Functional analysis of splice variant expression of MADS AFFECTING FLOWERING 2 of Arabidopsis thaliana. Plant Mol. Biol. 81, 57–69 (2013)

    Article  CAS  Google Scholar 

  14. Gu, X. et al. Arabidopsis FLC clade members form flowering-repressor complexes coordinating responses to endogenous and environmental cues. Nature Commun. 4, 1947 (2013)

    Article  ADS  Google Scholar 

  15. Werner, J. D. et al. Quantitative trait locus mapping and DNA array hybridization indentify an FLM deletion as a causefor natural flowering-time variation. Proc. Natl Acad. Sci. USA 102, 2460–2465 (2005)

    Article  ADS  CAS  Google Scholar 

  16. Méndez-Vigo, B., Martinez-Zapater, J. M. & Alonso-Blanco, C. The flowering repressor SVP underlies a novel Arabidopsis thaliana QTL interacting with the genetic background. PLoS Genet. 9, e1003289 (2013)

    Article  Google Scholar 

  17. Scortecci, K., Michaels, S. D. & Amasino, R. M. Genetic interactions between FLM and other flowering-time genes in Arabidopsis thaliana. Plant Mol. Biol. 52, 915–922 (2003)

    Article  CAS  Google Scholar 

  18. Scortecci, K. C., Michaels, S. D. & Amasino, R. M. Identification of a MADS-box gene, FLOWERING LOCUS M, that represses flowering. Plant J. 26, 229–236 (2001)

    Article  CAS  Google Scholar 

  19. Jiao, Y. & Meyerowitz, E. M. Cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control. Mol. Syst. Biol. 6, 419 (2010)

    Article  Google Scholar 

  20. Riechmann, J. L., Krizek, B. A. & Meyerowitz, E. M. Dimerization specificity of Arabidopsis MADS domain homeotic proteins APETALA1, APETALA3, PISTILLATA, and AGAMOUS. Proc. Natl Acad. Sci. USA 93, 4793–4798 (1996)

    Article  ADS  CAS  Google Scholar 

  21. Tao, Z. et al. Genome-wide identification of SOC1 and SVP targets during the floral transition in Arabidopsis. Plant J. 70, 549–561 (2012)

    Article  CAS  Google Scholar 

  22. James, A. B. et al. Alternative splicing mediates responses of the Arabidopsis circadian clock to temperatures changes. Plant Cell 24, 961–981 (2012)

    Article  CAS  Google Scholar 

  23. Wang, X. et al. SKIP is a component of the spliceosome linking alternative splicing and the circadian clock in Arabidopsis. Plant Cell 24, 3278–3295 (2012)

    Article  CAS  Google Scholar 

  24. Jones, M. A. et al. Mutation of Arabidopsis spliceosomal timekeeper locus1 causes circadian clock defects. Plant Cell 24, 4066–4082 (2012)

    Article  CAS  Google Scholar 

  25. Curtis, M. D. & Grossniklaus, U. A gateway cloning vector set for high-throughput functional analysis of genes in planta. Plant Physiol. 133, 462–469 (2003)

    Article  CAS  Google Scholar 

  26. Clough, S. J. & Bent, A. F. Floral dip: a simplified method for Agrobacterium-mediated transformation of Arabidopsis thaliana. Plant J. 16, 735–743 (1998)

    Article  CAS  Google Scholar 

  27. Immink, R. G. et al. Characterization of SOC1’s central role in flowering by the identification of its upstream and downstream regulators. Plant Physiol. 160, 433–449 (2012)

    Article  CAS  Google Scholar 

  28. Yant, L. et al. Orchestration of the floral transition and floral development in Arabidopsis by the bifunctional transcription Factor APETALA2. Plant Cell 22, 2156–2170 (2010)

    Article  CAS  Google Scholar 

  29. Moyroud, E. et al. Prediction of Regulatory Interactions from Genome Sequences Using a Biophysical Model for the Arabidopsis LEAFY Transcription Factor. Plant Cell 23, 1293–1306 (2011)

    Article  CAS  Google Scholar 

  30. James, P., Halladay, J. & Craig, E. A. Genomic libraries and a host strain designed for highly efficient two-hybrid selection in yeast. Genetics 144, 1425–1436 (1996)

    CAS  PubMed  PubMed Central  Google Scholar 

  31. de Folter, S. et al. Comprehensive interaction map of the Arabidopsis MADS Box transcription factors. Plant Cell 17, 1424–1433 (2005)

    Article  CAS  Google Scholar 

  32. Severing, E. I. et al. Predicting the impact of alternative splicing on plant MADS domain protein function. PLoS ONE 7, e30524 (2012)

    Article  ADS  CAS  Google Scholar 

  33. van Dijk, A. D. et al. Sequence motifs in MADS transcription factors responsible for specificity and diversification of protein-protein interaction. PLOS Comput. Biol. 6, e1001017 (2010)

    Article  Google Scholar 

  34. de Felippes, F. F. & Weigel, D. Transient assays for the analysis of miRNA processing and function. Methods Mol. Biol. 592, 255–264 (2010)

    Article  CAS  Google Scholar 

  35. Masiero, S. et al. INCOMPOSITA: a MADS-box gene controlling prophyll development and floral meristem identity in Antirrhinum. Development 131, 5981–5990 (2004)

    Article  CAS  Google Scholar 

Download references

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

Author notes

  1. David Posé, Levi Yant & Johannes Mathieu

    Present address: Present addresses: Instituto de Hortofruticultura Subtropical y Mediterránea, Universidad de Málaga–Consejo Superior de Investigaciones Científicas, Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, 29071 Málaga, Spain (D.P.); Department of Organismic and Evolutionary Biology, Harvard University, 16 Divinity Avenue, Cambridge, Massachusetts 02138, USA (L.Y.); Boyce Thompson Institute for Plant Research, Tower Road, Ithaca, New York 14853-1801, USA (J.M.).,

Authors and Affiliations

  1. Department of Molecular Biology, Max Planck Institute for Developmental Biology, Spemannstr. 35, 72076 Tübingen, Germany,

    David Posé, Felix Ott, Levi Yant, Johannes Mathieu & Markus Schmid

  2. Plant Research International, Bioscience, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands,

    Leonie Verhage, Gerco C. Angenent & Richard G. H. Immink

  3. Laboratory of Molecular Biology, Wageningen University, 6708 PB Wageningen, The Netherlands,

    Leonie Verhage & Gerco C. Angenent

Authors
  1. David Posé
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leonie Verhage
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Felix Ott
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Levi Yant
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Johannes Mathieu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gerco C. Angenent
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Richard G. H. Immink
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Markus Schmid
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Ethics declarations

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.

a–d, 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. c–e, 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.

a–h, Microscope images (a–d) and quantification of mCitrine-positive nuclei (e–h). 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. b–e, 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. f–h, 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 f–h 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.

a–c, 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.
Full size table

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)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature12633

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing