More than the (human) eye can see: the regulation of floral UV absorbance

Beverley Glover

Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK.

Flowers come in many different colours, and those colours can be arranged in a wide variety of patterns. Colour itself arises from a combination of differential reflection of light of different wavelengths, combined with the ability of an animal to perceive that differential reflection. If light across the human visible spectrum (blue to red) is all reflected equally, we perceive the reflecting object as white (no colour). If light of a shorter wavelength, in the ultraviolet (UV) range, is differentially reflected then we will still perceive the object as white, but an animal with UV-sensitive photoreceptors in its eyes will perceive the object as coloured. Most insects and many other animals have UV-sensitive vision. I was once asked in a popular science talk whether it’s possible that there is a planet out there on which colour doesn’t exist, and everything is black or white. To which the answer is, there are many planets out there on which no organism has the ability to perceive differential light reflection, but I’m sure that on all planets on which light falls, there are materials which reflect different wavelengths to different extents. Whether that means those planets have colour, if nothing can see it, is a question for a philosopher to tackle!

Bev blog on UV light

Arctotis fastuosa photographed under visible (left) and UV (right) light. The human visible patterning resulting from differential anthocyanin pigmentation is very different from the insect visible patterning that results from UV absorbance by the inner ring of ray florets but UV reflectance by the outer ring.

The colour of flowers is widely considered to be part of the signalling between plants and animal pollinators, with the brightly coloured petals acting as part of a ‘sensory billboard’ (Raguso, 2004) to attract animals to the floral rewards (usually nectar or pollen). It is clear that different colours are visible to different animals, and so particular colours or colour patterns can be interpreted as part of an evolutionary trend in response to attracting particular groups of pollinators. The extent to which specific flower colours do attract specific pollinators is a source of great debate in the literature, with some authors arguing that, like a human-attracting billboard, any colour that stands out against the green vegetation will be as attractive as any other.
While there are clearly some very specific cases of colour (and scent) patterns combining to attract very specific pollinators, such as in the sexually deceptive orchids like Ophrys speculum , it is likely that most flower colours are signalling more broadly.

Ophrys speculum has a highly zygomorphic flower, with inner tepals and both whorls of reproductive organs affected

One good example of specific colours signalling to specific pollinators is found in the genus Petunia, where bee-pollinated flowers such as P. inflata are purple and reflect UV, moth-pollinated P. axillaris flowers are white and absorb UV, and hummingbird-pollinated P. exserta flowers are red and reflect UV. The evolutionary transitions between these different pollination syndromes must have involved switches in regulation of pigment production, to cause both human-visible and human-invisible colour changes. Previous studies have uncovered some of the mechanisms underpinning the human-visible shifts between purple and white flowers, which are the result of changes in the regulation of anthocyanin production. In Petunia this evolutionary transition is caused by changes to the activity of a MYB transcription factor which regulates the genes encoding the enzymes of anthocyanin biosynthesis (Hoballah et al., 2007; Quattrocchio et al., 1999). Interestingly, in various other phylogenetically unrelated examples of evolutionary transitions in anthocyanin content, MYB genes have also been implicated (eg. Yuan et al., 2013).

Recent work by Sheehan et al. (2016) has tackled the much more difficult problem of how the human-invisible UV absorbance has changed between different Petunia species. UV absorbance is the result of flavonol production – these compounds are produced by a biosynthetic pathway which branches off the main anthocyanin pathway using dihydroflavonols as substrate. Flavonols appear white to the human eye, but they absorb UV, and so to a moth with UV-sensitive vision flavonol-pigmented flowers will reflect longer wavelengths more strongly that shorter ones, and appear blue-green. Sheehan and colleagues first used accessions of P. axillaris differing in flavonol production to confirm that hawkmoths preferentially visit UV-absorbing flowers over UV-reflecting ones. They then used QTL mapping with an F2 population derived from a cross between P. exserta and P. axillaris to identify a major locus explaining 79% of the variation in flavonol levels between the 2 species. In combination with previous reports of a flavonol QTL on chromosome II when comparing P. integrifolia (UV reflecting) and P. axillaris (UV absorbing), these data led the authors to investigate the gene encoding flavonol synthase (FLS), the enzyme that catalyses the production of flavonols from dihydroflavonols. FLS had been previously mapped to chromosome II. They found that the FLS locus itself could not explain the differences between species – there were no differences in coding sequence, and although the gene was differentially expressed in different parent species, when alleles from different parents were combined in a single heterozygous plant the different alleles did not show differential expression. Sheehan and colleagues concluded that changes to a regulator of FLS was the likely explanation for the evolutionary transitions. They therefore used a transposon mutagenesis screen to search for candidates, crossing a UV-reflective line of P. hybrida with active transposons to UV-absorbing P. axillaris. The F1 generation is heterozygous for UV absorption, which is a dominant trait. Flowers were screened for UV reflecting sectors where the transposon had disrupted the target gene in the P. axillaris chromosome. This work was made much easier by the surprising realisation that UV reflecting sectors were also a deeper pink in the human-visible spectrum. Identification of the gene disrupted in these sectors revealed the key regulator to be another MYB transcription factor, named MYB-FL, which is linked to and regulates the expression of FLS. Following this molecular genetic detective work Sheehan and colleagues were able to trace the evolution of UV absorbance between their 3 Petunia species, finding that MYB-FL is not expressed in bee-pollinated P. inflata. Expression is activated in moth-pollinated P. axillaris, inducing UV absorbance, by mutation of the MYB-FL promoter region. This expression is maintained in hummingbird-pollinated P. exserta but UV absorbance is lost due to a single base pair deletion in the coding region, causing a frameshift and an early stop codon.
This dissection of the molecular basis for shifts in UV absorption is a powerful example of the use of molecular genetic tools to tackle evolutionary questions even outside the range of what is usually visible to humans. However, the interesting twist in the tale is the discovery that UV reflecting sectors were also brighter pink to the human eye. We cannot see flavonols, so the explanation proposed by Sheehan et al. is that the FLS enzyme is in competition with dihydroflavonol reductase (DFR) for dihydroflavonol substrates. As FLS activity reduced with the inactivation of MYB-FL, then so DFR could channel more dihydroflavonol into the anthocyanin pathway. This hypothesis remains to be tested, but it is certainly logical and, if proven correct, will generate a need to re-think our ideas about the independence of different pigment classes and the ability of different aspects of flower colour to evolve independently.


Hoballah M,  Gubitz T, Stuurman J, Broger L,  Barone M,  Mandel T, Dell’Olivo A, Arnold M, and Kuhlemeier C. 2007. Single gene-mediated shift in pollinator attraction in Petunia. Plant Cell 19, 779-790.

Quattrocchio F,  Wing J, van der Woude K, Souer E, de Vetten N, Mol J, and  Koes R. 1999. Molecular analysis of the anthocyanin2 gene of petunia and its role in the evolution of flower colour. Plant Cell 11, 1433–1444.

Raguso RA. 2004. Flowers as sensory billboards: progress towards an integrated understanding of floral advertisement. Current Opinion in Plant Biology 7, 434-440.

Sheehan H, Moser M, Klahre U, Esfeld K, Dell’Olivo A, Mandel T, Metzger S, Vandenbussche M, Freitas L, and Kuhlemeier C. 2016. MYB-FL controls gain and loss of floral UV absorbance, a key trait affecting pollinator preference and reproductive isolation. Nature Genetics 48, 159-166.

Yuan Y, Sagawa J, Young R, Christensen B, and Bradshaw HD Jr. 2013. Genetic dissection of a major anthocyanin QTL contributing to pollinator-mediated reproductive isolation between sister species of Mimulus. Genetics 194, 255-263.

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A database for all things flowering

by Frank Wellmer
Smurfit Institute of Genetics, Trinity College Dublin, Ireland

Three decades of research on the genetic and molecular control of flowering has led to a staggering amount of data from a multitude of different experimental approaches (summarized for example in Fornara et al., 2010; Prunet and Jack, 2014). This work has been described in several thousand publications, and even noted experts in this area will hardly be in full command of the wealth of available information. What’s more, young researchers entering the field have an almost unscalable mountain of literature to climb when they want to familiarize themselves with what is already known and what still remains to be investigated. A key question is thus how this large amount of information can be unified, simplified, displayed and brought into a format that readily allows it to be used for approaches such as data mining, meta-analysis or even mathematical modeling. Arguably one of the biggest issues here is that the available data are often of markedly different quality: while some observations are being support by evidence from multiple independent experimental approaches generated by different research groups, others are based on the results of a single study (which, in some cases, might have been poorly designed). And there are examples where the available data are, at least in part, contradictory. Also, the advent of genomics approaches, while highly influential and impactful in plant biology, has led to the generation of datasets with inherent errors, but error rates are difficult to estimate and thus are unknown in most cases. The results from these approaches in particular depend on the experimental set-up used. For example, has the function of a given floral regulator been studied through transcriptomics after its constitutive and ectopic over-expression, or have more sophisticated transgenic lines been established for its analysis resulting in conditions that are closer to the situation found in the wild type?

The points outlined above (and probably many others as well) make the indexing, the processing and the representation of the available data a truly daunting challenge. Recently, Bouché and colleagues have taken on this herculean task and set up a free Flowering-Interactive Database, FLOR-ID (Bouché et al., 2016). While primarily containing information for gene networks involved in the control of flowering time, FLOR-ID also represents knowledge on early events during flower development (Fig. 1).

Fig. 1

Fig. 1. Screenshot of one of the interactive schemes available in FLOR-ID. Floral organ identity factors (in red) and their known regulatory functions in the different types of floral organs are shown. Created by Bouché.

In total, data from almost 1,600 publications have been used to assemble the database, which covers around 300 regulatory genes. Importantly, FLOR-ID is hand-curated, using a simple but effective search strategy (UniProt and PubMed database searches combined with information taken from reviews and primary research papers). The information in FLOR-ID can be accessed either by downloading tabulated data or through a user interface, which provides beautifully designed and interactive schemes for the different processes known to be involved in flowering-time control and early flower development (Fig. 1). Notably, the results from papers using -omics approaches have been largely omitted, especially if they are only predictive and not backed by results from independent experiments. Some may find this approach too exclusive, but the authors had the good sense to design FLOR-ID to allow user input and to suggest modifications, which, importantly, will be curated and only then incorporated into the active database. Thus, FLOR-ID could become a real community effort and will certainly be a major boon in the field of flowering for years to come.


Fornara F, de Montaigu A, Coupland G. 2010. SnapShot: Control of flowering in Arabidopsis. Cell 141(3):550, 550.e1-2.

Prunet N, Jack TP. 2014. Flower development in Arabidopsis: there is more to it than learning your ABCs. Methods in Molecular Biology 1110, 3-33.

Bouché F, Lobet G, Tocquin P, Perilleux C. 2016. FLOR-ID: an interactive database of flowering-time gene networks in Arabidopsis thaliana. Nucleic Acids Research 44, D1167-1171.


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Distorted flower development and rainforest loss

How basic research in epigenetics helps to improve somatic embryogenesis technologies and contributes to protect rain forests

by Lars Hennig
Swedish University of Agricultural Sciences, Department of Plant Biology and Linnean Center for Plant Biology, Uppsala, Sweden

Not often is the relevance of basic research immediately obvious. Although history is full of examples of how human curiosity led to discoveries that changed the world, basic research finds itself very often in a defensive position. Recent work from the Martienssen lab (Ong-Abdullah et al., 2015) is a brilliant example how basic research findings from two fields – plant floral development and epigenetics – pave the way to improve crop yield and eventually alleviate the deforestation pressure on tropical rain forests. This work very clearly illustrates the great importance of knowledge about genes steering flower development and the mechanisms by which plants defend their genomes.
The paper by Ong-Abdullah and colleagues contributes to overcome one of the great disappointments of plant biotechnology – somaclonal variation. Some decades ago, in vitro regeneration and, in particular, somatic embryogenesis methods were developed to rapidly multiply elite genotypes. Such techniques can maintain favourable allele combinations, eg. in F1 hybrids, and are particularly valuable in perennial species with long juvenile phases. However, the great hopes that were connected to in vitro techniques were soon confronted by unwanted phenotypic variation in the in vitro offspring – so-called somaclonal variation.

One-year-old oil palm plant generated by somatic embryogenesis grown in vitro. (Image courtesy of Marcelinus Rocky Hatorangan, Uppsala, Sweden)

One-year-old oil palm plant generated by somatic embryogenesis grown in vitro. (Image courtesy of Marcelinus Rocky Hatorangan, Uppsala, Sweden)

The African oil palm (Elaeis guineensis) is one of the major sources for plant oil and estimates suggest that somatic embryogenesis may contribute to 20-30% yield increase in this species. This is highly relevant because rain forest is cleared to increase the land available for oil palm cultivation to meet the growing demand for plant oil. Unfortunately, oil palm suffers from severe somaclonal variation with many plants derived from somatic embryos developing abnormal flowers that result in great yield losses (Corley, 1986). These flowers have carpelloid stamens and staminodes suggesting reduced B-class floral homeotic gene function. The phenotype was called ‘mantled’ and has escaped molecular understanding for several decades. Like many somaclonal variation effects, the mantled phenotype occurs in a stochastic manner and can sometimes revert. The non-Mendelian genetics have suggested an epigenetic nature of the effect. Mantled oil palms arise stochastically after tissue culture but appear symptom-free during the long juvenile phase of several years before first flowering.
The work by Ong-Abdullah et al. (2015) shows that mantled plants are characterized by reduced DNA methylation on a transposable element (TE) related to the rice Karma element. In oil palm, the critical TE is located in a large intron of EgDEF1 – a homolog of Arabidopsis APETALA3 and snap dragon DEFICIENS, and a likely B-class floral homeotic gene. Loss of KARMA methylation affects EgDEF1 pre-RNA splicing and thereby creates an altered EgDEF1 protein that possibly could act in a dominant negative way to interfere with normal floral development. The mantled phenotype is fully consistent with reduced B-class function and the EgDEF1 gene has been studied as a candidate gene in mantled oil palms before (Adam et al., 2007; Jaligot et al., 2014).

When is Karma methylation in EgDEF1 lost? Tissue culture often reduces DNA methylation but most of it is restored during plant regeneration. In some plants, however, DNA methylation at Karma was lost completely and not restored. Because DNA methylation loss appears to be stochastic, lost Karma methylation does not correlate with other methylation changes and phenotypes. This has made it impossible so far to predict the flower morphology at a young age.

One-year-old oil palm plant generated from seed grown on soil. (Image courtesy of Marcelinus Rocky Hatorangan, Uppsala, Sweden)

One-year-old oil palm plant generated from seed grown on soil. (Image courtesy of Marcelinus Rocky Hatorangan, Uppsala, Sweden)

As a result of the work by Ong-Abdullah and colleagues, a diagnostic assay is now available to predict the flower phenotype at early age. Instead of growing affected individuals for many years until the flower defect is manifested by reduced yield, plants can now be screened at a young age. Land for oil palm cultivation can thus be used more efficiently, hopefully alleviating the pressure to increase the arable area.
Although the evidence is compelling and the diagnostic fulfils the practical need, the formal proof that the defective EgDEF1 splicing and the altered EgDEF1 protein cause the phenotype is still missing. Likewise the mechanism by which altered DNA methylation affects EgDEF1 splicing has not been identified. It will be interesting to learn whether other cases of somaclonal variation after tissue culture also alter splicing of genes for developmental regulators.
So far, EgDEF1 with Karma is the only well-documented locus connected to somaclonal variation but it is likely that locally reduced DNA methylation is involved in other cases, too. Karma was found using an association study design in which methylation states were profiled across the genome for populations of mantled and wild-type plants. The analysis landed right on EgDEF1, which was the only locus with DNA methylation consistently reduced in all mantled individuals. This approach can easily be used for other species and may in the future lead to the development of novel diagnostics, and reduce the detrimental effects of somaclonal variation in plant biotechnology. Finally, knowing the mechanisms behind DNA methylation homeostasis may allow developing cures for susceptible loci such as EgDEF1. As Ong-Abdullah and colleagues speculate in their paper, an engineered introduction of 24nt small RNAs targeted at the critical Karma region may re-establish DNA-methylation to a degree that mantled phenotypes do not develop and costly screening of plants is eliminated altogether.
Implications of research are impossible to foresee – they may be wider and more exciting than anticipated!

Corley, RHV (1986). Oil palm. In CRC Handbook of Fruit Set and Development. Monselise, S.P.Ed.; pp. 253-259. CRC Press: Boca Raton, Fl.
Adam H, Jouannic S, Orieux Y, Morcillo F, Richaud F, Duval Y and Tregear JW. (2007). Functional characterization of MADS box genes involved in the determination of oil palm flower structure. Journal of Experimental Botany 58: 1245-1259.
Jaligot E, Hooi WY, Debladis E, Richaud F, Beule T, Collin M, Agbessi MD, Sabot F, Garsmeur O, D’Hont A, Alwee SS and Rival A. (2014). DNA methylation and expression of the EgDEF1 gene and neighboring retrotransposons in mantled somaclonal variants of oil palm. PLoS ONE 9(3): e91896. doi:10.1371/journal.pone.0091896
Ong-Abdullah M, Ordway JM, Jiang N, Ooi SE, Kok SY, Sarpan N, Azimi N, Hashim AT, Ishak Z, Rosli SK, Malike FA, Bakar NA, Marjuni M, Abdullah N, Yaakub Z, Amiruddin MD, Nookiah R, Singh R and Low ET et al.. (2015). Loss of Karma transposon methylation underlies the mantled somaclonal variant of oil palm. Nature 525: 533-537.

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A tale of many variants

by Alt-Jan van Dijk

Plant Research International, Wageningen

In the quest to unravel genetic aspects of flowering time regulation, quantitative trait loci (QTLs) are important data. QTLs indicate genome regions in which variation influences a trait of interest. Typically, a relatively large region of the genome is identified, and for practical purposes in breeding it might not always be needed to zoom in to the nucleotide level. However, for a better understanding of the molecular mechanisms involved in flowering time regulation, and for example, its role in domestication, knowledge of causal genes or nucleotides is clearly required.

In a recent paper in Molecular Biology and Evolution, Liu et al. (2015) aimed to identify a causal gene underlying a flowering time QTL in sorghum. To get as close as possible to the causal variant underlying the QTL, fine-mapping was applied, using fine-spaced markers to delineate a small genome region within the original QTL. In the resulting 37-kb genome region, the authors observed that the gene HD1 (Heading Date 1) in one of the two parents of their population contains a frame-shift inducing deletion. HD1 is a very relevant gene, because homologs in rice and in Arabidopsis (CONSTANS, CO) are known to regulate flowering time. HD1/CO is a transcription factor, and transcription factors are known to be important players in domestication (see for example Meyer and Purugganan, 2013). The frameshift observed by Liu et al. lead to truncation of HD1. Such variant-of-large-effect is quite likely to lead to a dysfunctional protein. Although other types of functional changes are relevant during evolution, in particular cis-regulatory changes, mutations leading to null function appear to be the predominant type of causative change during domestication (Meyer and Purugganan, 2013).

In addition to analyzing the genetic variation in HD1 in the context of their original QTL mapping population, Liu et al. added two additional types of evolutionary comparisons (Fig. 1). The first additional analysis focused on a set of sorghum varieties. In this population, the same truncating variation in HD1 as in the QTL study was found in some individuals, and an alternative deletion in HD1 was also identified. As a second step, additional crops were analyzed, in particular, rice and foxtail millet. In both species, a syntenic region containing HD1 can be identified. This region had previously been mapped as flowering time QTL in both species. In rice, variants with a premature stop codon and with frameshifts are observed, which is somewhat reminiscent of the observed variation in HD1 in sorghum. In foxtail millet, however, the variation influences splicing leading to a deletion of 11 amino acids and again potential loss of HD1 function.

As a general note, the selection of HD1 as a putative causal gene for the QTL by Liu et al. was possible because of available knowledge on HD1/CO function. For many genes, such knowledge is not available – and in fact, genes with yet unknown functions arguably would be the most valuable novel findings. We recently published a method aimed at prioritizing candidate genes underlying QTLs (Bargsten et al., 2014), which integrates sequence and expression information in order to predict gene functions. Subsequently, these predicted gene functions are used to rank genes for their likelihood to be causal genes involved in the trait-of-interest. Using this approach on a rice QTL compendium, a set of 79 genes with so far unknown function was identified as most likely candidates underlying variation in rice flowering time (Bargsten et al., 2014).

The results presented by Liu et al. seem to indicate that variation in HD1 has been repeatedly selected for its effect on flowering time. In fact, an additional example is available: previously, QTL mapping suggested a variation in sorghum HD1 that consisted of a His to Tyr substitution (Yang et al., 2014). The same His to Tyr mutation inactivates the Arabidopsis HD1 homolog CO. Clearly, experimental proof of the hypothesized effects of the various genetic changes is needed. Nevertheless, the results presented by Liu et al. illustrate that for crop domestication, different roads lead to Rome: even though the genetic variation itself is not the same in the different species (deletion, splicing variation or coding change) in each case the effect presumably is that flowering time is affected.


 Bargsten JW, Nap JP, Sanchez-Perez GF, van Dijk ADJ. 2014. Prioritization of candidate genes in QTL regions based on associations between traits and biological processes. BMC Plant Biology 14: 330.

Liu H, Liu H, Zhou L, Zhang Z, Zhang X, Wang M, Li H, Lin Z. 2015. Parallel domestication of the Heading Date 1 gene in cereals. Molecular Biology & Evolution 32(10): 2726-37.

Meyer RS and Purugganan MD. 2013. Evolution of crop species: genetics of domestication and diversification. Nature Reviews Genetics 14: 840–852.

Yang S, Weers BD, Morishige DT and Mullet JE. 2014. CONSTANS is a photoperiod regulated activator of flowering in sorghum. BMC Plant Biology, 14: 148.

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Passing genes to future generations

by Ben Trevaskis1 and Kazuhiro Sato2

1CSIRO Division of Plant Industry, Australia.  2Institute of Plant Science and Resources, Okayama University, Japan.

Genome and transcriptome sequencing is changing plant science. Access to genome or transcriptome sequences allows genomics to be applied to biological questions directly in target species and there has been a shift of research focus from genome-sequenced model plants, such as Arabidopsis, towards non-model plants. A good example is rapidly expanding genomics research in cereal crops, where genome sequencing provides access to the genetic variation that drives crop improvement via plant breeding; variation that improves crop yield or that adapts varieties to diverse environments and disease threats. Here the challenge is to link variation in crop genomes to superior varietal performance. Amongst many polymorphisms, which ones influence crop performance? Genome sequencing alone is not enough to address this challenge and now, more than ever, genetics is the key to research advances.

There has been a strong tradition of genetics in barley research. Barley is an important crop, vital for beer and whisky production, but also a diploid model for closely related hexaploid bread wheat. Barley was an early target for mutagenesis and has long been used for gene mapping. There are now a range of barley genetic resources available for genomics research, such as mutant collections or mapping populations, and there are stock centres with a wealth of natural genetic diversity collected from cultivated or wild barleys. Barley is also transformable, via Agrobacterium co-cultivation in tissue culture. This allows reverse genetics approaches to be applied to test gene function, including the possibility for gene editing or other transgenic methods, such as RNA silencing or reporter-gene fusions. Other genetic tools include doubled-haploid production systems.

An early flowering barley from the Dairokkaku 1 isoline series developed by Professor Shozo Yasuda.

An early flowering barley from the Dairokkaku 1 isoline series developed by Professor Shozo Yasuda.

An outstanding example of the barley genetics tradition is provided by Professor Shozo Yasuda (Okayama University, Japan). From the 1950’s until the 1970’s Yasuda investigated the genetic basis for reduced vernalization-requirement in barley. Autumn sown “winter barleys” typically flower only after prolonged cold treatment (vernalization) whereas “spring types” flower rapidly without prior cold treatment. This variation in seasonal flowering behaviour drives adaptation to different climates and sowing dates. By inter-crossing winter by spring barleys, collected from diverse regions, Yasuda and co-workers identified three genetic loci that trigger flowering without vernalization – SPRING GROWTH HABIT (Sgh) 1-3. These genes are now known as VERNALIZATION (VRN) 1-3, and have all been identified and sequenced by gene cloning (reviewed by Trevaskis et al. 2007). Yasuda also provided evidence for further more subtle variation for vernalization requirement amongst winter barleys from different regions in Asia. This variation is now the focus of ongoing research (Saisho et al. 2011). All of this research was driven by classical genetic techniques, crossing many different parents, complementation assays with test lines and by performing rudimentary mapping via linkage with other visible traits (see for example Takahashi and Yasuda 1956). The hundreds of barleys that were genotyped for spring growth habit by test crossing gives a clear indication of the massive effort involved.

The research lead by Professor Yasuda continues to resonate in an era of genomics. We recently described the molecular characterisation of a series of barley isolines developed by Yasuda and co-workers (Cuesta-Marcos et al. 2015). These lines are genetically similar but differ for alleles of either VRN1, VRN2 or VRN3. The isolines were generated by many generations of recurrent backcrossing (9-11 rounds) of different spring growth-habit donors to a Japanese winter barley (cv. Dairokkaku 1). The entire process was also repeated in parallel with a second winter barley parent (cv. Hayakiso 2). This crossing was performed before gene sequences or corresponding molecular markers were available, so selection was based entirely on observations of flowering behaviour in segregating progeny. Assessment of the lines with genome-wide marker platforms shows the precision with which the lines were generated. Each contains the desired gene for spring growth habit, with nearby genes introgressed by linkage drag, but carries few other donor genes. In addition to single gene/allele contrasts the isolines set also includes combinations of the different introgressed spring growth-habit genes. For example, one line has reduced vernalization requirement via VRN1 whereas another line carries reduced vernalization requirement at both the VRN1 and VRN3 genes.

The isolines generated by Yasuda and co-workers are a powerful genetic resource for studying the physiological impacts of genes that reduce vernalization requirement; contrasting alleles for each gene and the interaction between the different genes. Our recent study demonstrates the potential experimental applications of Yasuda’s lines. The lines were used for detailed physiological assessment of how genotypes with different vernalization requirements cope with freezing stress. An important outcome of these experiments was the demonstration that activation of VRN3 compromises freezing tolerance and decreases the chances of surviving winter in cold climates. VRN3 is the barley equivalent of FLOWERING LOCUS T (FT), a gene activated by long-days in leaves to trigger flowering (Yan et al. 2006). FT-like genes trigger flowering in diverse plant species and it will be interesting to test whether activation of FT also compromises freezing tolerance in other plants.

There is also great potential to use the isolines for genomics research, gene expression analysis for example. Using microarray analyses we showed that, at early stages of development, a limited number of genes are differentially expressed between lines with different vernalization requirements (Cuesta-Marcos et al. 2015). These differentially expressed genes are likely to include those that actually trigger the transition to reproductive development. Consistent with this idea a number of known floral promoters were differentially expressed between the isolines. Interestingly the gene expression profiles were not identical between the VRN1, VRN2 and VRN3 isolines, supporting earlier suggestions that there are discrete differences in the way these genes trigger flowering. While the microarray analyses are a useful starting point, the real value of the lines for gene expression analysis will be achieved with transcriptome sequencing. By examining the transcriptome profiles at different stages of development and in different organs, the leaves and shoot apices for example, it will be possible to generate a deeper understanding of the pathways that trigger reproductive development and flowering in cereal crops. The close genetic relationships between the lines will allow cause and effect to be resolved – the genetic basis for different gene expression patterns is clear.

The work of Yasuda and co-workers shows the long-term value of applying classical genetics to crop research, both to understand trait variation and to develop research tools. The impact of this type of research, and the resulting genetic resources, will multiply as genomics tools become more powerful and as high-resolution phenotyping platforms are developed. Perhaps the greatest challenge for future researchers is to commit to the development of genetic resources over a longer time scale while also facing the challenges of an international research environment that strives for rapid outcomes.

Cuesta-MA, Muñoz-Amatriaín M, Filichkin T, Karsai I, Trevaskis B, Yasuda S, Hayes P, Sato K. (2015). The relationships between development and low temperature tolerance in barley near isogenic lines differing for flowering behavior. Plant & Cell Physiology doi: 10.1093/pcp/pcv147.
Saisho D, Ishii M, Hori K, and Sato K. (2011). Natural variation of barley vernalization requirements: implication of quantitative variation of winter growth habit as an adaptive trait in East Asia. Plant & Cell Physiology (2011) 52 (5): 775-784.
Takahashi R and Yasuda S. (1956). Genetic studies of spring and winter habit of growth in Barley. Berichte der Ohara Instituts für Landwirtschafliche Biologie 10:29-52.
Trevaskis B, Hemming MN, Dennis ES and Peacock WJ . (2007). The molecular basis for vernalization-induced flowering in cereals. Trends in Plant Science 12(8):352-357.
Yan L, Fu D, Li C, Blechl A, Tranquilli G, Bonafede M, Sanchez A, Valarik M, Yasuda S, Dubcovsky J. (2006). The wheat and barley vernalization gene VRN3 is an orthologue of FT. Proccedings of the National Academy of Science USA. 103: 19581-19586.

Posted in Barley, flowering, FLOWERING LOCUS T, genetic resources, Plant breeding, SPRING GROWTH HABIT, vernalization | Tagged | Leave a comment

Meeting report: Flowers are always a good excuse to meet!

By Cristina Ferrándiz and Francisco Madueño.
Instituto de Biología Molecular y Celular de Plantas. CSIC-UPV.Valencia, Spain

It is roughly 25 years since the ABC model of flower development was formulated, and still we find in its elegance never-ending inspiration for our work… With some “flavour” of a big anniversary, members of many groups, which were connected almost through family ties with the model, met for the latest biannual Workshop on Mechanisms Controlling Flowering. The Workshop was Presentation1 held from 7th to 11th June, in Aiguablava, a nice spot in the Northern Mediterranean coast of Spain, with the generous support from the Journal of Experimental Botany, the Company of Biologists and EMBO. More than 100 participants, who came mainly from Europe, but also from many other places around the world, like China, Korea, Japan, Australia, USA, Mexico and Brazil.

The Workshop was organized in five sessions. Altogether, 73 participants (almost ¾ of the total) reported their work in some type of oral presentation, maximizing the communication of their results to the audience. Many of the diverse fields of study relating to flowers and flowering were represented, from the mechanisms that control the initiation of flowering to those that regulate the developmental processes that occur within each floral organ type. Wide-ranging aspects of these processes were discussed, with a special focus on emerging technologies applied to uncover the molecular mechanisms underlying the transcriptional regulatory networks of morphogenesis and organ patterning.

The opening session was about Floral Transition. George Coupland talked about the latest results of his group on how plants acquired competence to flower, the role of miR156/SPLs and gibberellins, and how functional variation in this route can help us to understand the differences between annual and perennial species. Soraya Pelaz linked the TEMPRANILLO genes and their role in flowering also to GAs and to a new role in the regulation of trichome initiation in Arabidopsis. Jim Weller gave a nice overview on recent progress in flowering pathways in temperate legumes. Last invited talk of the session was from Markus Schmid, who put still more arrows pointing to FT from all corners of the flowering pathways map, including the recently incorporated sugar-mediated route. In addition, 5 oral presentations and five flash talks covered exciting advances in flowering regulation in Arabidopsis but also in other species such as tomato and rice.

The second session was on Inflorescence Architecture and Meristem Patterning. Robert Sablowski initiated this session talking on the fundamental question of how cell growth and cell cycle are coordinated to shape plant organs, presenting his last results on how the transcription factor JAGGED contributes to this process. The talk by Phil Wigge was about transcriptional networks, with a focus on regulation of flowering and plant architecture by temperature, and introducing the view that a good transcriptome prediction (tradiction) can be done based on data from a few selected genes. Paco Madueño talked about transcriptional control of TFL1, presenting data that put this important, though classically poorly connected inflorescence regulator, on the map of flowering pathways. François Parcy, last invited speaker in the session, presented novel structural insights into the mode of action of LFY, highlighting the importance of LFY oligomerization to act as a pioneer transcription factor. Four additional oral presentations and 5 flash talks completed the session, with novel exciting data on how well-known and novel regulators act, their connection with hormones, with other transcription factors, etc., to control inflorescence architecture and growth in Arabidopsis, tomato, petunia and rice.

Third session, the longest one, was on Floral Organ development. José Luis Riechmann opened the session describing his genome-wide studies on Arabidopsis, from analyses of the mode of action of CAL and CUC1 transcription factors to the contribution of sORFs to flower development. Martin Kater continued on gene regulatory networks but making a turn to floral female organs, ovules, and rice, focusing on the function of OsMADS13, the rice STK homologue. Lars Østergaard reminded us that auxin “rules us all”, explaining its role in guiding the unusual transition from bilateral to radial symmetry in the apical gynoecium, which is mediated by a newly uncovered mechanism of auxin perception by direct interaction of IAA with transcription factors. Gerco Angenent moved to tomato, other popular species in the meeting, and back to MADS, with a detailed analysis of how SlFUL1/2 proteins achieve specificity in their dual regulatory role in inflorescence and carpel development. Cristina Ferrandiz presented a different perspective on gynoecium development in Arabidopsis, talking on how different combinations of transcription factors may specify the different tissues of this complex structure. Returning to rice, Dabing Zhang talked about ABCDE-function MADS-box genes on floral organs and a newly discovered role of jasmonate in grass inflorescences. This was a popular session, so 8 more oral presentations and 13 flask talks contributed to broaden our knowledge on how floral organ shape, size and identity are defined in different species, the factors underlying stamen, carpel and ovule development, the role of plant hormones in organ patterning and even taking us to the mysteries of rose scent.

There was also a session dedicated to Evo-Devo. Charlie Scutt took a nice walk across the angiosperm phylogeny, focusing on the changes in molecular properties of a small set of transcription factors, identified by their key roles in Arabidopsis carpel development, that could be traced at different positions in the tree. Günther Theissen zoomed in to Brassicaceae fruits to explain the evolution of different seed dispersal mechanisms and the reasons to be heteromorphic. 4 more oral presentations and 3 flash talks brought petunias, Nicotianas, and even gymnosperms, mosses or algae, diving outside the floral world in the search of its origins.
The last session was focused on New Technological Advances and Modelling. Kerstin Kauffmann effectively bridging from the previous Evo-devo talks, explained how to apply recent genome-wide analyses approaches to understand the evolution of floral gene regulatory networks. Jose Davila-Valderrain followed up, bringing mathematics to build dynamic models and to understand the constrains to generate diversity that these models predict. Two oral presentations and 5 flash talks rounded up the scientific sessions by giving us tools to look at transcription factors at work, to edit genomes with CRISPR/cas9 and to learn how to translate the increasing complexity of data being generated into models that allow to see the forest through the trees.


The workshop had also many other ingredients for a great outcome. The venue, at the top of a really beautiful beach cove, was quite secluded and the participants were all staying together at the premises, maximizing the interaction between group leaders, postdocs and graduate students. The schedule of the meeting allowed enough free time for relaxed discussion outside the talks, exchange of information, arranging scientific collaborations and consortiums for students to explore postdoc opportunities for their near future and, of course, to have fun! In summary, a successful, highly interactive and very interesting Workshop that strongly helps to maintain the “Floral Development” tradition of Europe as leading actors in the field.
For more info, see:

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On flowering and grain number in cereals …

by Maria von Korff
Heinrich Heine University Düsseldorf
Max Planck Institute for Plant Breeding Research

The timing of flowering is important for fitness and reproductive success in plants. Numerous studies demonstrate that variation in flowering time correlates with yield in model and crop plants under field and control conditions.
How flowering time affects yield remains an interesting question. It is commonly argued that because flowers are sensitive to abiotic stresses, plants have to set flowers when environmental conditions are optimal for flower fertility and seed set. Flowering time genes thus indirectly affect flower fertility and yield as they determine when plants flower. In addition, the timing of floral transition decides when plants switch from producing leaves to producing flowers. Variation in the duration of vegetative and reproductive growth ultimately affects plant and spike architecture.

Barley flowering

Image courtesy of M von Korff

Monocot cereals represent good models to study the effects of flowering time regulators on spike architecture. In contrast to the model plant Arabidopsis, where floral transition and flowering take place within a short period of time, in cereal crops such as wheat and barley several weeks may pass between the initiation of the first flower primordia and flowering. This provides an opportunity to dissect the genetic and environmental control of spike development. Variation in spike development affects the number of seeds per spike as one of the major yield components in monocots cereals. Consequently, the manipulation of seed number per spike and spike architecture is important for breeding high-yielding cultivars. The flowers of cereals develop on a specialized short branch called a spikelet which carries one or more florets and forms on opposite sides of the central rachis. Genetic factors affecting the number of fertile spikelets per node have been identified in maize, rice and barley. For example, the barley specific Vrs1 locus encoding a homeodomain-leucine zipper (HDZip) I transcription factor suppresses the development of lateral spikelets and thus determines formation of two or six spikelets and seeds per rachis node (Komatsuda et al., 2007). In addition, genes controlling initiation and outgrowth of lateral branches or axillary meristems such as RAMOSA (RA2) or TEOSINTE BRANCHED 1 (TB1) affect spikelet number in barley and maize (Bortiri et al. 2006, Ramsay et al. 2011, Koppolu et al. 2013). Interestingly, a recent study in wheat reveals that the flowering time gene Photoperiod-1 (Ppd-1), a pseudo-response regulator gene that is known to control photoperiod-dependent flowering, has a major inhibitory effect on paired spikelet formation (Boden et al. 2015).

Effect of ppd1 on spike length. Image courtesy of B Trevaskis

Wheat -effect of Ppd1 on spike length. Image courtesy of B Trevaskis

Paired spikelets are characterized by the formation of a second spikelet immediately adjacent to and directly below a typical single spikelet in wheat. In temperate cereals, allelic variation in Ppd-1 influences sensitivity to long-day (LD) photoperiods; a mutation in this gene confers a delayed flowering response under LD conditions in barley, while in wheat gain-of-function insensitive alleles promote a constitutive LD response in all photoperiods (Turner et al. 2005, Beales et al. 2007). Boden et al. (2015) analysed near isogenic wheat lines (NIL) which either carried photoperiod insensitive alleles of PPD1 (flower early regardless of photoperiod) or photoperiod sensitive alleles of PPD1 (flower earlier in LD than SD photoperiods). They demonstrate that the photoperiod sensitive NIL flowered very late and produced paired spikelets under SD conditions. The authors further demonstrate that variation in the formation of paired spikelets correlates with the expression of TaFT, the wheat homolog of FLOWERING LOCUS T (FT) in Arabidopsis. In Arabidopsis, FT protein is produced in the leaf and transported to the shoot apical meristem where it triggers expression of meristem identity genes that regulate the development of the inflorescence and floral organs. In wheat, low expression of TaFT and downstream meristem identity genes such as TaSOC1 (SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1) and TaVRN1 (VERNALIZATION1, APETALA1/FRUITFUL-like) increased the formation of paired spikelets. These results suggest that the strength of a floral signal determines when inflorescence axillary meristems are converted to spikelet meristems, so that either a short branch comprising a lateral (secondary) and terminal (primary) spikelet forms or a single spikelet. These findings show that modifying the expression of flowering genes can be used to modify the number of grain producing spikelets. Future studies may investigate whether and how flowering time genes control the expression of “classical” branching genes such as VRS1, RA2 or TB1.

Boden SA, Cavanagh C, Cullis BR, Ramm K, Greenwood J, Finnegan EJ,Trevaskis B, Swain SM. 2015. Ppd-1 is a key regulator of inflorescence architecture and paired spikelet development in wheat. Nature Plants 1, 14016.
Bortiri E, Chuck G, Vollbrecht E, Rocheford T, Martienssen R, and Hake S. 2006. ramosa2 encodes a LATERAL ORGAN BOUNDARY domain protein that determines the fate of stem cells in branch meristems of maize. Plant Cell. 18, 574–585.
Beales J, Turner A, Griffiths S, Snape JW, Laurie DA. 2007. A Pseudo-Response Regulator is misexpressed in the photoperiod insensitive Ppd-D1a mutant of wheat (Triticum aestivum L.). Theoretical and Applied Genetics. 115, 721–733.
Komatsuda T. et al. 2007. Six-rowed barley originated from a mutation in a homeodomain-leucine zipper I-class homeobox gene. Proceedings of the National Academy of Science USA 104: 1424–1429.
Koppolu R, Anwar N, Sakuma S, Tagiri A, Lundqvist U, Pourkheirandish M, Rutten T, Seiler C, Himmelbach A, Ariyadasa R, Youssef HM, Stein N, Sreenivasulu N, Komatsuda T, Schnurbusch T. 2013. Six-rowed spike4 (Vrs4) controls spikelet determinacy and row-type in barley. Proceedings of the National Academy of Science USA. 110,13198-13203.
Ramsay L. et al.. 2011. INTERMEDIUM-C, a modifier of lateral spikelet fertility in barley, is an ortholog of the maize domestication gene TEOSINTE BRANCHED 1. Nature Genetics. 43, 169–172.
Turner A, Beales J, Faure S, Dunford RP, Laurie DA. 2005. The pseudo-response regulator Ppd-H1 provides adaptation to photoperiod in barley. Science. 11, 1031-1034.

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A hitchhiker guide to modelling

by  Aalt-Jan van Dijk

Wageningen University, The Netherlands

In my last few posts (‘Tell me who your sister is and I’ll tell you who you are‘ and ‘Cold kick-start for flowering‘), I discussed a couple of modelling papers. Hence, I thought it would be good to give a more general ‘travel guide’ for this type of papers. To do so, I will mostly focus on a recent paper by Seaton et al. (2015) which deals with the link between the circadian clock and flowering in response to photoperiod and temperature. The components of their model include CYCLING DOF FACTOR 1 (CDF1), FLAVIN-BINDING, KELCH REPEAT, F-BOX 1 (FKF1), GIGANTEA (GI), CONSTANS (CO) and FLOWERING LOCUS T (FT). For the purpose of this guide, I will group the results using three different stages that we can distinguish in a typical modelling approach (Fig. 1).


Fig.1.Three different modelling stages and typical examples of results obtained in these stages

(1) The first stage consists of constructing the model. One example from the Seaton paper is that the effect of GI on CDF1 protein stability is ‘sufficient to explain the lower CO transcript levels observed in the gi mutant’. This means that a model variant, in which the effect of GI on CDF1 stability is incorporated, is able to reproduce the known fact that the gi mutant has lower CO levels. The construction phase is, in most cases, when parameter values have to be obtained. Often, the strength of the interactions in quantitative models is estimated based on e.g. time course data. One then adjusts the parameter values to get as good fit as possible between predictions and data. One example of such parameter fitting in the Seaton paper is the influence of temperature on the regulation of FT by SHORT VEGETATIVE PHASE (SVP) and FLOWERING LOCUS M (FLM). According to the paper, ‘the action of these regulators can be modelled by introducing a uniform activation of FT expression at 270C’. What is done here is that when the temperature is 270C instead of 220C, three parameters are changed in the model. Importantly, given the high number of parameters, relatively noisy and/or scarce data, a good fit is not yet proof for the validity of a model or any of the claims made based on results obtained during this construction phase.

(2) The second stage of modelling deals with model predictions and their validation. Typical phrases to look for if you want to travel in that direction are ‘previously unseen data’ or ‘test data’. An example from the Seaton et al. paper is that model simulations for the response of CO and FT were compared with data ‘not used for parameter optimisation’. The way in which model predictions and data are compared can be either quantitative or qualitative. In this paper, comparison is qualitative. This makes it difficult to decide how much evidence there really lies in the fact that model and data agree to a certain extent. A qualitative match can sometimes be trivial, if in the construction stage choices were made which inevitably lead to certain model behaviour. Note that discrepancies between model predictions and experiments can be very informative. According to Seaton et al. (2015) ‘the model is unable to fully describe the dynamics of CO and FT mRNA in the cca1;lhy double mutant’. This could indicate that CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) and LATE ELONGATED HYPOCOTYL (LHY) may also regulate CO and FT transcription by additional mechanisms than those included in the model.

Various other examples of validation could be given. In my recent flowering time model (Leal Valentim et al., 2015), validation was performed by predicting changes in expression level in mutant backgrounds and comparing these predictions with independent expression data, and by comparison with predicted and experimental flowering times for several double mutants. This involved a quantitative comparison between experiments and predictions. In the model by Satake et al., (2013), parameters were fitted to data obtained in controlled experiments in the lab. The model subsequently predicted the response to temperature conditions measured in the field. Comparison of these predictions with measurements indicated a convincing ability to predict flowering.

(3) Finally, a third stage in modelling is to interpret model features or results emerging from the modelling to obtain biological insights. One more example from the Seaton paper is that coherent feed-forward networks are identified. In such network structures, a single component plays multiple reinforcing roles in the system. The concept of feed-forward or feed-back loops and related network motifs are intuitively appealing and lead to new insights into how biology works. Discussion of such types of network motifs can be found in a study by Jaeger et al. (2013). The interlocked feedback loops were claimed to be relevant in a developmental context because they enable the acquisition of a fate outcome that is stable over many cell divisions.

One general remark that I still want to make is that for modelling as well as for any other scientific experiment, it all starts with good questions. To illustrate this, consider the answer given to ‘the great Question of Life, the Universe and Everything’ by the computer in ‘The Hitchhiker’s Guide to the Galaxy’. ‘Forty-two’, said Deep Thought, with infinite majesty and calm. ‘I checked it very thoroughly, and that quite definitely is the answer. I think the problem, to be quite honest with you, is that you’ve never actually known what the question is’.

This small ‘travel guide’ is of course far from complete. Some aspects that I have not discussed include technical issues like what type of model is used – e.g. continuous vs. discrete or stochastic vs. deterministic. In addition to the molecular level discussed here, models could include various other levels such as e.g. cells or tissues. Finally, it is important to realise that results in the validation stage or the interpretation stage can sometimes be quite sensitive to choices and parameter values defined during the construction stage. Nevertheless, hopefully this ‘travel guide’ will help you to find your way when reading your next modelling paper!


Seaton DD, Smith RW, Song YH, MacGregor DR, Stewart K, Steel G, Foreman J, Penfield S, Imaizumi T, Millar AJ, Halliday KJ. 2015. Linked circadian outputs control elongation growth and flowering in response to photoperiod and temperature, Molecular Systems Biology11(1): 776. doi: 10.15252/msb.20145766.

Leal Valentim F, van Mourik S, Posé D, Kim MC, Schmid M, van Ham RCHJ, Busscher M, Sanchez-Perez GF, Molenaar J, Angenent GC, Immink RGH, van Dijk ADJ. 2015. A quantitative and dynamic model of the Arabidopsis flowering time gene regulatory network. PLoS ONE 2015 doi: 10.1371/journal.pone.0116973

Satake A, Kawagoe T, Saburi Y, Chiba Y, Sakurai G, Kudoh H. 2013. Forecasting flowering phenology under climate warming by modelling the regulatory dynamics of flowering-time genes. Nature Communications. Aug 14;4:2303.

Jaeger KE, Pullen N, Lamzin S, Morris RJ and Wigge PA. 2013 Interlocking feedback loops govern the dynamic behaviour of the floral transition in Arabidopsis. The Plant Cell. 25(3), 820-833.

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Cold kick-start for flowering

by Aalt-Jan van Dijk
Wageningen University, The Netherlands

When I started writing this commentary, I looked out of my window and decided a good way to start would be to mention that here in the Netherlands winter had finally arrived. Snow had fallen and ice was beginning to be visible on the pond in the park. However, by the time I was finishing writing this a few days later, the situation had already changed and the temperature was again above zero degrees Celsius. Hence, instead of referring to the current weather, I’ll use an old Dutch work of art (Fig.1). It was painted in a time of severe winters and illustrates the impact of winter on the landscape and on human behaviour.

Fig. 1. Painting by Hendrick Avercamp (1609).

Fig. 1. Painting by Hendrick Avercamp (1609). Rijksmuseum, Amsterdam

Winter also influences plants, and flowering is a prime example of a trait under control of environmental cues such as light and temperature. Temperature influences flowering both via small fluctuations in ambient temperature and via longer periods of winter cold. In the latter case, the promotion of flowering in response to a prolonged exposure to cold is called vernalization. In the model plant Arabidopsis thaliana, a lot is known about regulation of this process. In particular, the transcription factor FLC is known to be a central player in the vernalization response. FLC is a repressor of flowering which has to be repressed itself in order to allow vernalization. In a recent paper, Helliwell et al. (2015)  propose a hypothesis on how the influence of winter cold on flowering is initiated.
Increasingly, there is a role for computational approaches in understanding the regulation of flowering time, including vernalization (Angel et al. 2011). As recently described in a review on gene regulatory networks involved in Arabidopsis reproduction (Pajoro et al. 2014), computational models come in a variety of flavours. Examples include two models for the Arabidopsis flowering time integration network (Valentim et al. 2015), which integrate computational modelling with experimental data in order to understand the function of a gene regulatory network. Helliwell et al. (2015) made a different use of models. Here, the main purpose is to generate hypotheses which then still await experimental validation. Their hypothesis deals with the question of how repression of FLC is initiated. As mentioned above, FLC is the main player in Arabidopsis’ response to vernalization. Although a lot is known about FLC repression, it still seems unclear what is the very first step in this repression.
Their central proposal is that the initial response to temperature change is physical, in the sense that there would be reorganization of the folding or looping of the chromatin polymer which would derive from a polymer entropy effect. Such a physical response is quite a general effect, different from a response enabled by a particular gene or protein, which changes its behaviour upon temperature change. To clarify the idea of a physical response, you could look again at the scene visualized in Fig.1. Ice is clearly visible, and this is the result of the physical response of freezing. In this process, molecules themselves do not change, but their configuration change with respect to each other (Figure 2).

Fig.2. Physical responses to temperature change. (Top) Water changes structure upon the transition from liquid water (right) to ice (left). Red indicates oxygen atom, black hydrogen atom. (Bottom) Polymer phase transition from high temperature (right) to low temperature (left). Green circles indicate monomeric subunits of polymer.

Fig.2. Physical responses to temperature change. (Top) Water changes structure upon the transition from liquid water (right) to ice (left). Red indicates oxygen atom, black hydrogen atom. (Bottom) Polymer phase transition from high temperature (right) to low temperature (left). Green circles indicate monomeric subunits of polymer.

Ideas on how the physical response of chromatin to temperature would be relevant for FLC repression are provided by Helliwell et al.(2015) referring polymer physics
models. These are described from a mathematical perspective as well as more visually by a video recording of the relaxation of a rubber band. One aspect of this hypothesis, which I think is beautiful, is how it connects different ‘levels’. This is actually an important aspect of computational models: on which scale or level do they operate? Models can, for instance, describe a whole plant, they can be tissue-based, or at the other extreme, models can be molecule-based such as the above-mentioned gene regulatory network models. Modelling chromatin via polymer physics models involves molecules but is clearly at a higher level than the scale of individual proteins or genes. In fact, the exact nature of the DNA and proteins involved in chromatin is not considered. These components are aggregated simply as ‘polymer’. The polymer scale is then connected to the molecular and cellular scale via its proposed influence on FLC expression and finally to the macroscopic level as it would influence flowering time upon vernalization.
What still puzzles me is how such a physical response could specifically influence FLC and not a lot of genes all over the genome. What would be special about FLC related chromatin? Helliwell et al. (2015) mentioned that ‘it is likely that the combination of DNA sequence and chromatin topology of the FLC locus make this locus uniquely responsive to changes in temperature’, but to me it is not clear how that could be accomplished for such a general physical response. Of course, any type of hypothesis or model should ultimately be tested with experiments. They also discussed a few examples of how analysis of kinetics of gene expression would enable this testing to be done. This would involve analysis of the kinetics of FLC repression after transfer to different low temperatures, and the kinetics of repression of genes neighbouring FLC. It will be interesting to see how data obtained in this way will validate or falsify the hypothesis on the importance of the physical response of chromatin to temperature for vernalization.
From my perspective here in the Netherlands, it is very timely to read Helliwell et al.(2015) paper. Even if we are not aware of it, plants around us are already preparing for the time after winter. The question what is the initial kick-start by which cold enables this response is clearly of fundamental importance to understand the regulation of flowering– and this paper generates fresh ideas on this issue.

Helliwell CA, Anderssen RS, Robertson M, Finnegan EJ
. 2015. How is FLC repression initiated by cold? Trends in Plant Sciences. 20(2) 76-82.
Angel A, Song J, Dean C, Howard M. 2011. A Polycomb-based switch underlying quantitative epigenetic memory. Nature 476(7358):105-108.
Pajoro A, Biewers S, Dougali E et al. 2014. The (r)evolution of gene regulatory networks controlling Arabidopsis plant reproduction: a two-decade history. Journal of Experimental Botany. 65, 4731-4745.
Jaeger KE, Pullen N, Lamzin S, Morris RJ and Wigge PA. 2013 Interlocking feedback loops govern the dynamic behaviour of the floral transition in Arabidopsis. The Plant Cell. 25(3), 820-833.
Valentim FL, van Mourik S, Posé D, Kim MC, Schmid M, van Ham RCHJ, Busscher M, Sanchez-Perez GF, Molenaar J, Angenent GC, Immink RGH, van Dijk ADJ. 2015. A quantitative and dynamic model of the Arabidopsis flowering time gene regulatory network. PLoS ONE 10(2):e0116973.


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Flowers under attack by phytoplasmas

by Frank Wellmer

Smurfit Institute of Genetics, Trinity College Dublin, Ireland

Phytoplasmas are cell wall-less bacteria that infect plants when they are transferred via sap-feeding insects such as leafhoppers. Plants infected with phytoplasmas can show a range of defects. One of them is the well-known Witches’ Broom, a massive overproliferation of shoots, often seen in nature on trees and shrubs. Another common defect of phytoplasma infection is termed phyllody, which is a conversion of floral organs into leaf-like structures (Figure 1).  This phenomenon is interesting from an evolutionary point of view as floral organs are thought to be modified leaves (Pelaz et al., 2001; von Goethe, 1790). Thus, phytoplasmas convert floral organs into something that resembles their developmental ground state.

MacLean AM et al. (2014). Phytoplasma Effector SAP54 Hijacks Plant Reproduction by Degrading MADS-box Proteins and Promotes Insect Colonization in a RAD23-Dependent Manner. PLoS Biol 12(4): e1001835.

Figure 1. From MacLean AM, et al. (2014). PLoS Biol 12(4): e1001835.

The molecular basis of phyllody is largely unknown but a recent paper by MacLean et al. (2014) has begun to shed light on the underlying mechanism. The authors elucidated how one effector of phytoplasma, a protein called SAP54 (MacLean et al., 2011), induces phyllody in Arabidopsis thaliana. Using a yeast two-hybrid approach, they found that SAP54 physically interacts with several transcription factors of the MADS-domain family, which contains many key floral regulators. Among them are the APETALA1 (AP1) and SEPALLATA1 to 4 (SEP1-4) proteins that have important functions during the onset of flower development and/or the specification of floral organ identity, respectively (O’Maoileidigh et al., 2014; Sablowski, 2010).

How does the interaction of SAP54 with floral regulators of the MADS-domain family lead to phyllody? Given that plants with mutations in genes coding for certain MADS-domain proteins exhibit transformations of floral organs into leaves it appeared likely the SAP54 might render these transcription factors inactive. In fact, it was found that the MADS-domain proteins AP1 and SEP3 are less abundant in plants infected with phytoplasmas than in the wild type. This reduced protein accumulation appears to be a consequence of a higher rate of protein turnover because addition of a proteasome inhibitor resulted in a restoration of MADS-domain protein levels.

This finding brought the authors back to the results of their yeast two-hybrid screen, in which they had also identified two RAD23 proteins as interacting with SAP54. RAD23 proteins are thought to play a role in directing ubiquitinated proteins for degradation by the 26S proteasome. Thus, SAP54 may bridge between components of the ubiquitin-proteasome system and floral regulators.

That RAD23 proteins are indeed important for phyllody was shown through experiments in which SAP54 was ectopically expressed in plants. While SAP54 over-expression leads to a strong degree of phyllody in the wild type, the expression of phyllody is suppressed in mutants in which several RAD23 genes are disrupted. Similarly, infection of higher-order rad23 mutants with phytoplasma does not lead to phyllody nor to a degradation of MADS-domain proteins, albeit other aspects of phytoplasma infection, such as the formation of Witches’ Broom, are unaffected. Taken together, these data provide convincing evidence that SAP54 induces phyllody by targeting MADS-domain transcription factors for degradation through interactions with RAD23 proteins.

What benefit does inducing phyllody in their host plants have for phytoplasmas? When male and female leafhoppers were given the choice between plants with normal flowers and plants that exhibited phyllody, they produced significantly more progeny on the latter. While the exact reason for this preference is currently unclear, it appears from these results that phytoplasmas modify flower architecture primarily to attract their insect vector to infected plants and thus ensure the spread of the pathogen.


Figure 1: An Arabidopsis wild-type flower (left) and a flower from a plant after phytoplasma infection showing phyllody (right). Figure taken from (MacLean et al., 2014).


MacLean AM, Sugio A, Makarova OV, Findlay KC, Grieve VM, Toth R, Nicolaisen M, Hogenhout SA. 2011. Phytoplasma effector SAP54 induces indeterminate leaf-like flower development in Arabidopsis plants. Plant Physiol 157, 831-841.

MacLean AM, Orlovskis Z, Kowitwanich K, Zdziarska AM, Angenent GC, Immink RG, Hogenhout SA. 2014. Phytoplasma effector SAP54 hijacks plant reproduction by degrading MADS-box proteins and promotes insect colonization in a RAD23-dependent manner. PLoS Biol 12, e1001835.

O’Maoileidigh DS, Graciet E, Wellmer F. 2014. Gene networks controlling Arabidopsis thaliana flower development. New Phytol 201, 16-30.

Pelaz S, Tapia-Lopez R, Alvarez-Buylla ER, Yanofsky MF. 2001. Conversion of leaves into petals in Arabidopsis. Curr Biol 11, 182-184.

Sablowski R. 2010. Genes and functions controlled by floral organ identity genes. Semin Cell Dev Biol 21, 94-99.

von Goethe JW. 1790. Versuch die Metamorphose der Pflanzen zu erklären. Ettinger, Gotha, Germany.


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