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LARGE-SCALE BIOLOGY ARTICLE 1

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Differences in DNA-binding specificity of floral homeotic protein 3

complexes predict organ-specific target genes 4

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Cezary Smaczniak1,2,3,#

, Jose M. Muiño3,4,#

, Dijun Chen2,3

, Gerco C. Angenent1,5

, Kerstin 6

Kaufmann2,3,*

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8 1

Laboratory of Molecular Biology Wageningen University, Wageningen, 6708PB, The 9

Netherlands 10 2 Institute for Biochemistry and Biology, Potsdam University, Potsdam, 14476, Germany 11

3 Present Address: Institute of Biology, Humboldt-Universität zu Berlin, Berlin, 10115, Germany 12

4 Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, 14195, 13

Germany 14 5 Bioscience, Wageningen Plant Research, Wageningen, 6708PB, The Netherlands 15

16 # These authors contributed equally to this work 17

18 * To whom correspondence should be addressed: [email protected] 19

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Short Title: Specificities of MADS-domain homeotic complexes 21

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One-Sentence Summary: DNA-binding specificity of floral MADS-domain protein complexes 23

depends on protein complex composition and provides clues to the mechanisms underlying target 24

gene specificity. 25

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The author responsible for distribution of materials integral to the findings presented in this 27

article in accordance with the policy described in the Instructions for Authors 28

(www.plantcell.org) is: Kerstin Kaufmann ([email protected]). 29

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ABSTRACT 32

Floral organ identities in plants are specified by the combinatorial action of homeotic master 33

regulatory transcription factors. How these factors achieve their regulatory specificities is 34

however still largely unclear. Genome-wide in vivo DNA binding data show that homeotic 35

MADS-domain proteins recognize partly distinct genomic regions, suggesting that DNA binding 36

specificity contributes to functional differences of homeotic protein complexes. We used in vitro 37

systematic evolution of ligands by exponential enrichment followed by high throughput DNA 38

sequencing (SELEX-seq) on several floral MADS-domain protein homo- and heterodimers to 39

measure their DNA-binding specificities. We show that specification of reproductive organs is 40

associated with distinct binding preferences of a complex formed by SEPALLATA3 (SEP3) and 41

AGAMOUS (AG). Binding specificity is further modulated by different binding site spacing 42

preferences. Combination of SELEX-seq and genome-wide DNA binding data allows to 43

differentiate between targets in specification of reproductive versus perianth organs in the 44

flower. We validate the importance of DNA-binding specificity for organ-specific gene 45

regulation by modulating promoter activity through targeted mutagenesis. Our study shows that 46

Plant Cell Advance Publication. Published on July 21, 2017, doi:10.1105/tpc.17.00145

©2017 American Society of Plant Biologists. All Rights Reserved

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intrafamily protein interactions affect DNA-binding specificity of floral MADS-domain proteins. 47

Differential DNA-binding of MADS-domain protein complexes plays a role in the specificity of 48

target gene regulation. 49

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INTRODUCTION 51

The exact molecular mechanisms of how most transcription factors (TFs) achieve their DNA-52

binding specificity are largely unknown. Most intriguing are the questions how closely related 53

TFs control distinct biological processes, and how heteromeric complex formation affects 54

functional specificity. DNA binding specificity of proteins stems from primary DNA sequence 55

and its structural properties (Rohs et al., 2009). Another aspect that contributes to functional 56

specificity comes from the ability of TFs to form higher-order protein complexes. The protein 57

interactions potentially modify the DNA-binding affinity of individual members of the complex. 58

For example, interactions of TFs from the same family with a common cofactor can evoke latent 59

differences in DNA-binding specificities (Slattery et al., 2011). Moreover, the formation of 60

heterodimeric complexes between TFs of the same or different families results in the recognition 61

of novel, composite DNA TF binding sites (TFBSs) (Jolma et al., 2013; Jolma et al., 2015; 62

Bemer et al., 2017). The interplay of these molecular mechanisms could influence the DNA-63

binding specificity of plant MADS-domain TFs that are known to form a complex intra-family 64

protein interaction network. 65

MADS-domain TFs are present in all major eukaryotic lineages. Especially in plants, they 66

form a large family, e.g. of more than 100 members in the flowering plant Arabidopsis thaliana 67

(Parenicova et al., 2003). MADS-domain TFs have important roles in the regulation of many 68

developmental processes (Smaczniak et al., 2012a). Remarkably, some MADS-domain proteins 69

acquired several distinct functions in different organs related with their ability to interact with 70

other family members in a combinatorial manner. To explain the variety of regulatory functions 71

of MADS-domain proteins, we need to understand how the formation of heteromeric protein 72

complexes affect their DNA-binding specificity and target gene regulation. Previous studies 73

showed that MADS-domain proteins bind DNA sequence elements called CArG-boxes 74

(consensus CC[A/T]6GG) (Pollock and Treisman, 1990; Schwarz-Sommer et al., 1992; Huang et 75

al., 1993; Riechmann et al., 1996b; Riechmann et al., 1996a) by means of their highly conserved, 76

56 amino acid N-terminal DNA binding MADS-domain (Schwarz-Sommer et al., 1990). 77

Thousands of CArG-boxes are present in the genome of Arabidopsis thaliana, many of 78

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which seem not to be bound by MADS-domain TFs (de Folter and Angenent, 2006; Kaufmann et 79

al., 2009; Muino et al., 2014). Besides that, a large fraction of genomic regions bound in vivo do 80

not contain consensus CArG-box sequences (Kaufmann et al., 2009; Zheng et al., 2009; 81

Kaufmann et al., 2010; Deng et al., 2011). A good example how combinatorial protein 82

interactions determine regulatory specificity of plant MADS-domain proteins is flower 83

development. According to the ‘floral quartet’ model, each type of floral organ is specified by a 84

distinct combination of MADS-domain proteins that form quaternary protein complexes and 85

bind two CArG-boxes (a protein dimer contacts a single TFBS in such tetrameric complex) in 86

the regulatory regions of target genes (Theissen and Saedler, 2001). The interactions of 87

Arabidopsis MADS-domain proteins suggested in the ‘floral quartet’ model as well as 88

interactions with other TFs and chromatin-associated proteins were characterized in vitro and in 89

vivo (Honma and Goto, 2001; Melzer and Theissen, 2009; Melzer et al., 2009; Smaczniak et al., 90

2012b). Therefore, part of the functional specificity may come from the ability of MADS-91

domain TFs to form homo- and heteromeric protein complexes, which has been proposed before 92

(Krizek and Meyerowitz, 1996; Riechmann et al., 1996b). The functional analysis of chimeric 93

proteins, where MADS domain swaps between plant and human MADS-domain TFs are able to 94

rescue floral homeotic mutants, suggests, somewhat controversially, that their functional 95

specificity is independent of DNA-binding specificity (Riechmann and Meyerowitz, 1997; 96

Krizek et al., 1999). However, recent genome-wide in vivo DNA binding for homeotic MADS-97

domain proteins (Wuest et al., 2012; Ó’Maoiléidigh et al., 2013; Pajoro et al., 2014) show 98

between 30% and 60% non-overlapping TFBSs (Yan et al., 2016). Also, the DNA looping may 99

affect binding preferences, as shown in vitro for SEPALLATA homotetrameric complexes (Jetha 100

et al., 2014). Together, these results indicate a complex recognition mechanism that depends on 101

the DNA-binding specificities of MADS-domain TF dimers and their higher-order interactions. 102

Using systematic evolution of ligands by exponential enrichment followed by high-103

throughput sequencing (SELEX-seq), we determined DNA-binding specificities of selected 104

MADS-domain protein complexes in vitro. Here we show that MADS-domain protein 105

homodimers SEPALLATA3 (SEP3)–SEP3, AGAMOUS (AG)–AG, APETALA1 (AP1)–AP1 106

and organ-specific heterodimers SEP3–AG (reproductive organs), SEP3–AP1 (perianth) bind 107

DNA sequences with different specificities and affinities. In particular, the identity specification 108

of reproductive organs is linked with unique DNA-binding preferences of SEP3–AG dimers, 109

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resulting in a set of target genes not bound by other MADS-domain proteins. More generally, we 110

show that differences in DNA-binding specificities can discriminate between complex-specific in 111

vivo DNA TFBSs (as identified by ChIP-seq experiments). This approach allows to modify the 112

specificity of the native DNA TFBSs of MADS-domain proteins. As a proof of concept, we 113

modified TFBSs in the APETALA3 (AP3) promoter to modulate organ-specific gene expression 114

in vivo. 115

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RESULTS 117

DNA-Binding Specificities of Floral MADS-Domain TF Complexes 118

To determine DNA-binding specificities of individual MADS-domain TF complexes we 119

followed a SELEX-seq approach (Figure 1), in which we made use of in vitro 120

transcribed/translated MADS-domain protein complexes and double-stranded DNA (dsDNA) 121

libraries containing a 20 bp region of randomized nucleotides (Jolma et al., 2010). Starting with 122

this random pool of dsDNA, we isolated DNA sequences bound by MADS-domain TF 123

complexes by immunoprecipitation with immobilized, protein-specific antibodies (Figure 1A). 124

For each MADS-domain TF dimer combination we performed at least three rounds of SELEX 125

and characterized the evolved pools of sequences after each round by high-throughput DNA 126

sequencing (Supplemental Table 1) and electrophoretic mobility shift assays (EMSAs) (Figure 127

1B). Heteromeric complexes were not separated from homomeric complexes during our SELEX 128

experiment. However, our earlier data showed that SEP3 and AG or AP1 proteins, when 129

incubated with the DNA, form predominantly heteromeric rather than homomeric complexes 130

(Smaczniak et al., 2012b). This was confirmed by EMSA experiments using the SELEX-131

enriched DNA and AP1 or AG protein complexes with a truncated SEP3 protein (Supplemental 132

Figure 1). The predominant formation of heteromeric complexes with specific DNA-binding 133

preferences is also confirmed here by high reproducibility of the SELEX-seq-derived affinities of 134

the SEP3–AG complex when we used either the SEP3 or the AG antibody (Supplemental Figure 135

2). Furthermore, we observed a specific enrichment pattern of sequences for SEP3–AG 136

heterodimers that is neither strong for the SEP3 homodimer nor for the AG homodimer (Figure 137

2). 138

To estimate affinities of MADS-domain TF dimers to the DNA fragments we used 139

sequencing data of the initial randomized dsDNA libraries (Round 0, R0) and calculated the 140

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relative enrichments between R1–R3 of SELEX and R0 (Slattery et al., 2011). High-throughput 141

sequencing of the first three rounds of SELEX (R1–R3) for the various MADS-domain protein 142

combinations showed enrichment of the generic MADS-domain TFBS consensus in the evolved 143

pools of sequences (Figure 1C). The enrichment of a generic CArG-box in the SELEX-seq for 144

the AP1 homodimer was lower compared to any other studied MADS-domain protein complex 145

(Figure 1C). This suggests that either the AP1 homodimer binds more weakly to DNA compared 146

to other MADS-domain protein complexes, as was observed before in EMSA experiments on 147

individual DNA probes (Smaczniak et al., 2012b), or AP1 homodimers are formed with lower 148

efficiency. In comparison, we did not see any enrichment of randomly permutated CArG-box 149

sequence fragments in the enriched DNA sequence pools, confirming DNA-binding specificity 150

of MADS-domain proteins (Figure 1D). 151

152

SELEX-seq Analyses Reveal DNA-Binding Preferences of Floral Homeotic Protein 153

Complexes 154

To determine the DNA-binding preferences for MADS-domain TF complexes independently of 155

the presumed consensus CArG-box, we follow the methodology proposed by Slattery et al. 156

(Slattery et al., 2011). We estimated the optimal length of the k-mer (subsequence of a length k) 157

from which we can accurately predict the relative DNA-binding affinities in the SELEX-seq 158

data. We based our analysis on k-mers and not on the full length 20 bp sequences, because, in 159

theory, there are more than 1012

possible combinations of unique 20 bp sequences, and our 160

sequencing libraries contain on average 2.2 M reads (Supplemental Table 1). Depending on the 161

library, the optimal length of the k-mer varied between 9 and 12 bp (Supplemental Figure 3). For 162

further analyses, we chose k-mer length of 12 bp that had the highest information gain score in 163

most of the libraries. Then, we calculated the relative DNA-binding affinities using the ratio 164

between frequencies of k-mers in R3 and R0 of SELEX. Owing to a large diversity of sequences 165

in the randomized initial libraries (R0), their k-mer frequencies are low and, therefore, difficult to 166

estimate directly from the sequencing results. For this reason, we used a Markov Model to 167

estimate the best frequency of R0. This workflow allowed us to calculate relative DNA-binding 168

affinities of MADS-domain protein complexes to k-mers of length 12. We clustered those 169

relative affinities in a heatmap, highlighting diverse and overlapping binding preferences (Figure 170

2A). This analysis could differentiate the complexes based on their DNA-binding preferences. 171

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Sequences in cluster 1 were specific for SEP3–AG, sequences in cluster 2 were specific for AG 172

alone, while sequences in cluster 3 had mixed specificity mainly for SEP3, SEP3–AP1 and AP1 173

that could be further divided into three additional clusters 3a–3c (Supplemental Figure 4). In 174

detail, sequences in cluster 3a were not enriched for SEP3; sequences in cluster 3b were not 175

enriched for SEP3–AG while sequences in cluster 3c were specific to SEP3 alone. Remarkably, 176

more k-mers were specifically enriched for SEP3–AG or for AG than for the other proteins and 177

protein combinations. Also the SEP3–AG complex seemed to bind a wide range of sequences 178

including, but not limited to, the ones that were also bound by SEP3–AP1 or SEP3 complexes. 179

This shows that MADS-domain protein complexes bind overlapping sets of TFBSs and suggests 180

a presence of active competition for those common sites when multiple proteins are expressed in 181

the same tissues. However, SEP3–AG and AG complexes showed additional specific DNA-182

binding capacities, which may suggest that reproductive organ specification by AG and SEP3 183

involves in vivo binding to specific target genes not bound by other MADS-domain proteins. 184

To validate SELEX-seq derived relative affinities for 12-mer sequences represented in the 185

heatmap, we performed quantitative multiple fluorescence relative affinity (quMFRA) assays 186

(Man and Stormo, 2001). For these experiments, we used 20 bp fragments (plus barcodes and 187

flanking regions with labelled primers) obtained from sequenced SELEX libraries that contained 188

a particular 12-mer sequence (Supplemental Tables 2 and 3). Our SELEX and quMFRA 189

approaches gave very similar results with a correlation coefficient (R2) between 0.6–0.8 190

(Supplemental Figure 5 and Supplemental Data Set 1). Additionally, for five selected 20 bp 191

SELEX fragments and three protein complexes, we performed absolute protein-DNA binding 192

affinity estimation by EMSA. We found that four out of five measured dissociation constants 193

(Kd) were following relative affinity values observed in the SELEX-seq: high SELEX-seq 194

relative affinity should relate to low Kd (Supplemental Figure 6 and Supplemental Data Set 2). 195

Differences could be caused by the fact that SELEX-seq derived relative affinities are based on 196

the 12-mer sequences, while quMFRA and Kd estimations by EMSA were performed with the 197

full length 20 bp fragments containing a particular 12-mer and its flanking regions. 198

To identify consensus TFBSs of MADS-domain protein complexes in each cluster, we 199

extracted all full-length 20 bp fragments from the R3 sequencing data that contained specific 12-200

mer sequences and performed DNA motif discovery using the GADEM algorithm (Li, 2009). 201

Comparison between different motifs revealed differences in nucleotide composition and number 202

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of consecutive nucleotides between different MADS-domain TFBSs mainly in the central, A/T-203

rich region (Figure 2B). We observed that the motif for AG has the A/T-rich region length 204

between 3-5 bp (cluster 2), while the A/T-rich region for SEP3, AP1 and SEP3-AP1 is longer, 6–205

8 bp (cluster 3). Especially long A/T-rich regions were found for sequences in the cluster 3c, 206

which are specific for SEP3 alone (Supplemental Figure 4). The SEP3–AG dimer showed a 207

binding preference (cluster 1) intermediate between SEP3–SEP3 and AG–AG dimers. The 208

analysis of the sequence enrichment for all 64 possible variations of the consensus CArG-box 209

allowing one nucleotide change each in the A/T-rich region of the CC[A/T]6GG sequence, 210

confirmed that different MADS-domain protein complexes have different binding preferences, 211

even in the case of the generic CArG box (Supplemental Figure 7). 212

Presence of additional nucleotides flanking the CArG-box region in the consensus motifs also 213

constitutes important information for TFBS characterization. TTN- and -NAA nucleotide 214

sequences were the prevalent flanking sites of the CArG-box motif. Moreover, the analysis of the 215

12-mer fragment enrichment revealed that MADS-domain protein complexes bound non-CArG-216

box-like sequences. Examples of these sequences are listed in Supplemental Table 2 (confirmed 217

by EMSA-based, quMFRA experiments provided in Supplemental Data Set 1) and detailed 218

tables with relative affinities to all k-mers are available from the Gene Expression Omnibus 219

database. Sequences with high relative affinity to AG complexes showed the strongest deviation 220

from the ‘generic’ CArG-box consensus, e.g. seq1 for AG, or seq5 for the SEP3–AG complex. 221

On the other hand, sequences with high relative affinity to AP1 complexes, e.g. seq15 for SEP3–222

AP1 or seq12 for AP1–AP1, showed similarity to the generic CArG-box. This shows that TFBSs 223

for AG and its heteromeric complexes differ strongly from the consensus CArG-box. 224

Because we found previously an association of DNA binding affinity of MADS-domain TFs 225

with structural parameters (e.g. narrower minor DNA groove width) (Muino et al., 2014), we 226

used the DNAshape tool to predict DNA structural features (Zhou et al., 2013) for our SELEX-227

seq data. This allowed us, for example, to compare DNA minor groove width (MGW) between 228

sequence clusters bound by different protein complexes (Figure 2C). We observed narrowing of 229

the DNA minor groove for sequences more specific to SEP3 or SEP3–AP1 complexes (cluster 230

3), while the MGW for sequences specific for AG alone was the widest. Moreover the roll and 231

helix twist are the two DNA shape properties that mainly differed between studied sequences in 232

clusters 1–3 (Supplemental Figure 8). Sequences with the longer A/T-rich region specific for 233

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SEP3 and SEP3-AP1 complexes have narrower minor groove and higher roll values comparing 234

to sequences with the shorter A/T-rich region specific for AG (p < 0.05; t-test). These parameters 235

determine intrinsic bending of the DNA (Dickerson, 1998; Rohs et al., 2009), which supports the 236

idea that the bend level of the DNA might play a role in the DNA sequence selection by MADS-237

domain TFs. This phenomenon was observed before for other types of TFs (Nelson and 238

Laughon, 1990; Kneidl et al., 1995; Stella et al., 2010). The increased degree of bending of the 239

pre-bent DNA sequence towards the minor groove was observed in the crystal structure of the 240

mammalian MADS-domain protein SRF bound to DNA (Pellegrini et al., 1995) and in in vitro 241

experiments on Arabidopsis MADS-domain TFs (Riechmann et al., 1996a; West et al., 1997; 242

West et al., 1998). Our results suggest that these parameters not only influence DNA recognition 243

by plant MADS-domain TFs in general, but also contribute to DNA-binding specificity. 244

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Combining SELEX-seq and ChIP-seq to Predict Organ-Specific Targets of Floral 246

Homeotic Protein Complexes 247

Since SELEX-seq data can separate the DNA-binding specificities of different MADS-domain 248

protein complexes, we used this information to classify SEP3 binding events obtained by ChIP-249

seq (flower development stage 4–5 for SEP3, 4 days after induction) (Pajoro et al., 2014) 250

depending on whether they were bound by SEP3-AP1 or SEP3-AG complexes (Figure 3). We 251

chose these two dimers because they distinguish regulatory programs controlling the formation 252

of perianth (sepals, and in combination with AP3/PI petals) and reproductive organs (carpels, 253

and in combination with AP3/PI stamens), respectively. Among sequences bound by SEP3–AG 254

and SEP3–AP1 complexes in SELEX-seq we could distinguish the ones that are either more 255

SEP3–AG or SEP3–AP1 specific (Figure 3A) based on the identified consensus sequences from 256

Figure 2B. This prompted us to build a classifier to identify SEP3–AG or SEP3–AP1 specific 257

SEP3 in vivo DNA TFBSs. 258

To classify SEP3 binding events obtained by ChIP-seq depending on their specificity, we 259

defined a score function (see Methods) to annotate positions in the Arabidopsis genome based on 260

the SELEX-seq-inferred DNA-binding specificities. Comparison of available ChIP-seq data of 261

AP1 and AG (flower development stages 4–5 for AP1 and 5–6 for AG, 4 days and 5 days after 262

induction, respectively) (Ó’Maoiléidigh et al., 2013; Pajoro et al., 2014) within 1500 most 263

enriched SEP3 ChIP-seq peaks revealed that the score ratio for SELEX-seq derived peaks within 264

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those SEP3 binding regions predicts complex specific TFBSs (Figure 3B and Supplemental Data 265

Set 3). In this manner, we were able to discriminate TFBSs that were either more SEP3–AP1 or 266

more SEP3–AG specifically bound in the Arabidopsis genome. We also showed that the SEP3–267

AG specific motif of cluster 1 (Figure 2B) overlaps very well with the AG-specific ChIP-seq 268

regions (Figure 3C). In addition, SEP3–AP1 highly bound sequences from cluster 3 and had 269

good overlap with AP1-specific ChIP-seq bound regions. Moreover, the AG-specific motif from 270

cluster 2 overlaps very well with the AG-specific ChIP-seq TFBSs. In general, the number of 271

specifically bound TFBSs is higher for AG than for AP1, which is linked to the presence of 272

specific DNA-binding motifs (Figure 3C). This analysis showed that in vivo DNA-binding 273

specificities of reproductive organ-specific complexes can be derived from the SELEX-seq data. 274

Inferring protein complexes that bind to specific genomic regions is difficult based on ChIP-275

seq data alone, since standard ChIP pulldowns usually detect all genomic TFBSs of a TF, 276

independent of its interaction partners. Combining ChIP-seq experiments of different TFs can 277

identify which TFs bind to the same regions, but not necessarily together in a complex. However, 278

SELEX-seq data allowed us to identify genomic regions that are directly bound by either SEP3–279

AG or by SEP3–AP1. To test whether protein complex-specific TFBSs drive expression in 280

specific floral whorls, we integrated the information from SELEX-seq data for SEP3–AG and 281

SEP3–AP1 with whorl-specific expression data (TRAP-seq) (Jiao and Meyerowitz, 2010). We 282

used genome aligned SEP3–AP1 and SEP3–AG SELEX-seq data to classify the 1500 most 283

enriched SEP3 ChIP-seq peaks. This analysis allows us to predict genomic regions that are 284

bound directly by a specific TF complex. As a result, the SELEX-based classification of SEP3–285

bound genomic regions correlated better with whorl-specific expression data, than a 286

classification based on SEP3, AP1 and AG ChIP-seq data (Figure 3D, Supplemental Figure 9). 287

To validate TFBSs obtained with SELEX-seq we focused on specific examples of regulatory 288

regions that are bound by MADS-domain proteins. The upstream promoter regions of SEP3 and 289

AP3 were predicted by our SELEX-seq approach as binding regions of several MADS-domain 290

proteins (Figure 4). The position of the SELEX-seq peaks is in very good agreement with the 291

position of ChIP-seq peaks for all MADS-domain homeotic TFs within those two regulatory 292

regions. One of these genomic regions, positioned 4.1 kb upstream in the promoter of SEP3, has 293

two TFBSs in close proximity that show differential affinity (represented by the SELEX peak 294

height) and binding specificity (not all heteromeric complexes bound to both TFBSs). These 295

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results are in agreement with our previous in vitro EMSA studies of the same regulatory 296

fragment (Smaczniak et al., 2012b) where these two TFBSs showed variable binding efficiencies 297

for different MADS-domain protein complexes with one site being superior in importance to the 298

other. Also in the AP3 promoter, two previously characterized TFBSs (Tilly et al., 1998; Jetha et 299

al., 2014), positioned close to the transcriptional start site showed differential binding affinity. In 300

the same region, a third potential CArG-box has been identified (Tilly et al., 1998), but it is not 301

predicted to be bound by MADS-domain complexes in SELEX-seq, in agreement with other in 302

vitro data (Tilly et al., 1998; Jetha et al., 2014). 303

The SELEX-seq data mapped to the genome (Figure 4) allowed us to predict TFBSs at much 304

higher resolution compared to the ChIP-seq data alone. Because of the high resolution we 305

estimated a preferred distance between located TFBSs. For all analyzed complexes we showed 306

that the preferred distance was around 62 bp (6 helical turns, visualized as the first peak from the 307

center and a red vertical line) (Figure 5). Moreover, we could see that for SEP3–AG this was the 308

only distance observed, while for SEP3–SEP3 complex TFBSs were also separated by longer 309

DNA stretch of around 210 bp. For SEP3–AP1, no strong preferences between more distal 310

TFBSs were clearly defined. This suggests that SEP3–AG and SEP3–AP1 complexes prefer to 311

act in different DNA loop size configurations, with one complex being stricter in the loop size 312

than the other is. 313

314

Activity of the AP3 Promoter with Altered DNA TFBSs 315

Combining information from SELEX-seq and ChIP-seq experiments to predict complex-specific 316

TFBSs in the Arabidopsis genome suggested that TFBSs could be potentially engineered in vivo 317

to modulate gene regulation by MADS-domain factors. To test this hypothesis we altered the 318

AP3 promoter (pAP3_wt) in a reporter construct that contained two CArG-boxes (Figure 6). We 319

chose the AP3 promoter for our in vivo and in vitro experiments as it is expressed in in a floral 320

whorl-specific manner (whorl 2 and whorl 3). The AP3 promoter is relatively short (0.9 kb) and 321

well characterized (Tilly et al., 1998; Jetha et al., 2014), with two identified MADS TFBSs. The 322

genomic region harboring the two functional CArG boxes is bound by MADS-domain proteins 323

in vivo (Figure 4). These properties of the AP3 promoter make it ideal for mutagenesis studies 324

because the compensation effects coming from other potential TFBSs would be minimized. The 325

activity of the AP3 promoter, when the two CArG-boxes were completely mutated (pAP3_mut), 326

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did not respond to the presence of the effectors (SEP3–AP1 or SEP3–AG protein complex). 327

Modifying the two CArG-boxes in the AP3 promoter based on the SELEX-seq data as having 328

more affinity towards SEP3–AG (pAP3_(SEP3-AG)) protein complex moderately altered the 329

activity of the AP3 promoter in response to the effectors (Figure 6A and 6B). The activity of the 330

pAP3_(SEP3-AG) promoter comparing to the pAP3_wt promoter increased when SEP3-AG 331

effector was used and decreased when SEP3–AP1 effector was present. The in vitro binding 332

strength of corresponding MADS-domain protein complexes to modified AP3 promoters was 333

stronger comparing to wt AP3 promoter in case of the SEP3–AG complex and weaker in case of 334

the SEP3–AP1 complex (Figure 6C). 335

The importance of these two TFBSs in the AP3 promoter was visualized by dual luciferase 336

activity assays in protoplasts and in in planta fluorescent reporter assays. These two approaches 337

showed that the expression of the mutated AP3 promoter without any functional CArG-box is 338

higher (Figure 6D) and not restricted to whorl 2 and 3 – it is detected throughout early stages of 339

flower development and the inflorescence meristem (Figure 6E). This is in agreement with the 340

previously reported GUS expression patterns (Tilly et al., 1998), and suggests that the CArG-341

boxes have also repressive roles, restricting AP3 expression to the developing flower. Thus, 342

those CArG-boxes are likely bound by other MADS-domain TF complexes as well, such as 343

those that are expressed in the inflorescence meristem. Indeed, studies have shown that SOC1 344

and SVP (Gregis et al., 2009; Immink et al., 2012) directly repress AP3 expression in the 345

inflorescence meristem and early floral meristem, providing an explanation of these results. 346

Moreover, the activity of the promoter with TFBSs more specific to the SEP3–AG complex 347

showed a spatial enhancement of the fluorescent signal in whorl 4 of the flower (Figure 6E). 348

Taken together, these results indicate that MADS-domain protein heteromeric complexes have 349

partly different DNA-binding specificities and consequently, this affects their target gene 350

specificity. 351

352

DISCUSSION 353

The variety of functions of MADS-domain TFs in the life cycle of Arabidopsis thaliana suggests 354

that they may regulate different sets of target genes. How exactly MADS-domain TFs achieve 355

their functional specificity is not yet fully understood. Here, we showed that part of the 356

functional specificity can relate to DNA binding. By making use of the SELEX-seq approach, we 357

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were able to distinguish common and specific TFBSs for several key floral homeotic MADS-358

domain TF complexes. Moreover, we presented here that differential binding of MADS-domain 359

protein complexes to their TFBSs plays a role in specificity of target gene regulation. 360

361

DNA-Binding Specificities of Floral Homeotic Proteins Predict Organ-Specific Targets 362

Recent high throughput approaches applied to large number of plant TFs aimed to identify their 363

in vitro DNA-binding specificities by using protein binding microarrays (Franco-Zorrilla et al., 364

2014) or a modification of the SELEX, DNA affinity purification sequencing (O'Malley et al., 365

2016), did not focus on MADS-domain proteins. Moreover, these studies did not take into 366

consideration the impact of TF heterodimerization on DNA TFBS selection. SELEX-seq allows 367

the systematic and sensitive characterization of DNA-binding properties of TFs. Previously, 368

‘classical’ SELEX followed by Sanger sequencing was used to study DNA-binding properties of 369

several plant MADS-domain TFs (Huang et al., 1993; Huang et al., 1995; Huang et al., 1996; 370

Tang and Perry, 2003). Some features of the consensus sequences found in those studies are 371

common to our SELEX-seq derived consensus. For example, for most MADS-domain TFs the 372

consensus motif resembles the putative CArG-box similar to our SELEX-seq motifs, and they 373

also contain specific nucleotides in the flanking regions. Moreover, we identified additional 374

TFBSs that strongly deviate from the consensus sequence. Although position weight matrices or 375

consensus sequences give substantial information on the DNA sequence characteristics that 376

determine TF binding, they fail, for example, to visualize the dependencies between nucleotide 377

positions or, most importantly, the affinities to particular DNA structures. Our analysis shows 378

that there are differences in DNA binding between selected MADS-domain homo- and hetero-379

complexes and support the concept that different MADS-domain protein complexes could bind 380

overlapping sets of TFBSs, although with different affinities, which would allow for active 381

competition between different MADS-domain protein complexes for the same target genes in 382

vivo. Floral homeotic complexes containing the AG protein, responsible for stamen and carpel 383

specification, have distinct DNA-binding preferences that result in the regulation of a specific set 384

of target genes. On the other hand, other target genes are commonly bound by different MADS-385

domain proteins but may be antagonistically regulated in perianth and reproductive organs in the 386

flower (Yan et al., 2016). 387

388

13

DNA Structure Affects MADS TF Binding Specificity 389

MADS-domain TFs bind DNA through interactions of the N-terminal part of the MADS domain 390

with the CArG-box A/T-rich region of the minor groove (Pellegrini et al., 1995) causing 391

substantial bending of the DNA. Although full crystal structures for plant MADS-domains are 392

not available, it was shown that plant MADS-domain proteins – similar to MADS proteins from 393

animals and yeast – bend the DNA (Riechmann et al., 1996a; Melzer et al., 2009). Additionally, 394

it was reported that DNA-bending by MADS-domain complexes could be DNA-sequence 395

specific (West et al., 1997; West et al., 1998), which supports the importance of the DNA 396

sequence in the regulation of a protein-DNA complex structure. The determinants of this 397

characteristic DNA-binding are not well understood. Muiño et al. showed that there is an 398

enrichment of a particular DNA structural pattern in regions bound by MADS-domain TFs 399

(Muino et al., 2014). Recently, large scale DNA structure predictions for human and plant 400

MADS-domain ChIP-seq TF binding data showed that combining DNA sequence information 401

with DNA shape features improves prediction of TF-bound genomic regions in vivo (Mathelier 402

et al., 2016). In their analysis, authors captured the importance of the MGW and Propeller Twist 403

(ProT) in selection of TFBSs of MADS-domain proteins. Our DNA-shape predictions of the 404

SELEX-seq sequences show that two ProT maxima are present at the both ends of the CArG-box 405

motif corresponding to CC and GG flanks. 406

Intrinsic ‘shape’ properties of the DNA can influence DNA binding, especially of TFs that 407

mainly recognize the DNA via interactions with the minor groove (Rohs et al., 2009). The A/T-408

rich region of the CArG-box can be considered as the “A-tract” (Muino et al., 2014). It was 409

previously reported that A-tracts facilitate MADS-domain TF DNA binding when located inside 410

the CArG-box motif and periodically distributed around it (Muino et al., 2014). Based on our 411

SELEX-seq sequence motifs we can infer that differences in the length of the A/T-rich regions 412

influence TF binding specificity. For example, AG alone usually prefers to bind sequences with 413

shorter A/T-rich region while the SEP3 alone binds sequences with a long A/T-region. The 414

SEP3-AG heterodimer binds sequences with an intermediate A/T-region. This, and the predicted 415

structural characteristics of the DNA-binding motifs of MADS-domain TF dimers, suggests 416

DNA structure, especially the width of the CArG-box minor groove, plays a role in determining 417

DNA-binding specificity. Since DNA bending (Haran and Mohanty, 2009) induced by the TF 418

14

dynamically changes conformation of promoters and juxtaposition of regulatory regions, this can 419

play a prominent role in MADS-DNA binding and gene regulation. 420

421

The Role of TF Multimerization in MADS DNA-Binding Specificity 422

According to the floral quartet model (Theissen and Saedler, 2001), quaternary MADS-domain 423

TF complexes bend the DNA in order to bind simultaneously two different TFBSs. The exact 424

nature of this binding and a possible regulation by formation of DNA regulatory loops by TFs is 425

not fully explored. Recent in vitro studies on homotetrameric complexes by SEP1, SEP2, SEP3 426

or SEP4 proteins from Arabidopsis shows their ability to bind cooperatively to DNA as tetramers 427

and loop the DNA, with different preferences for TFBS spacing (Jetha et al., 2014). The role of 428

TFBS spacing in DNA-binding specificity of MADS protein complexes is supported by the 429

distribution of TFBSs predicted by SELEX-seq within genomic regions bound in vivo. We 430

observed that spacing between SEP3/AG-specific TFBSs is more defined compared to that of 431

SEP3-AP1 TFBSs. This might suggest that some of the functional specificity comes from the 432

coordinated, simultaneous interaction of MADS-domain proteins with more than one cis-433

regulatory element in the genome. Since MADS-domain protein complexes can induce bending 434

to a different degree (Pellegrini et al., 1995; Riechmann et al., 1996a; West et al., 1997; Huang et 435

al., 2000) it also might be that stronger bending induced by SEP3-AG evokes interaction 436

between two TFBSs at shorter distances when the floral quartet is formed while a potentially 437

mild bending induced by the SEP3-AP1 protein complex does not impose such a restriction. In 438

summary, the commonly observed presence of more than one TFBS identified by SELEX-seq 439

within a single ChIP-seq peak suggest a role of these TFBSs in recruitment of specific higher-440

order complexes and robust regulation of the target gene expression. 441

442

Towards Understanding the Specificity of Floral Homeotic Protein Complexes 443

Since different MADS-domain proteins bind overlapping but not identical sets of genomic 444

regions, so part of the functional specificity of MADS-domain TFs is attributed to DNA-binding 445

specificity (Yan et al., 2016). High throughput in vivo DNA binding experiments showed that 446

MADS-domain proteins bind the DNA in places that lack canonical CArG-box (or CArG-box-447

like) sequences (Kaufmann et al., 2009; Zheng et al., 2009; Kaufmann et al., 2010; Deng et al., 448

2011). AG and AG-SEP3 bind DNA-sequence elements that resemble – if at all – CArG boxes 449

15

with very short A/T rich regions in the center. Comparing SELEX-seq with ChIP-seq binding 450

profiles, we were able to unravel part of the cis-regulatory code for specification of perianth vs. 451

reproductive organs. We showed that AG and AG–SEP3 complexes have unique DNA-binding 452

preferences that can be linked to a specific set of genomic regions bound in vivo, for example: 453

HECATE 2 (HEC2), which plays an important role in the gynoecium development (Gremski et 454

al., 2007) or CAPRICE (CPC) (Schellmann et al., 2002) and GLABRA3 (GL3) (Payne et al., 455

2000) that regulate trichome development, a process that is repressed by AG in carpels 456

(Ó’Maoiléidigh et al., 2013). In line with these specific functions, only 12.4% of the AG-bound 457

genomic regions are also bound by MADS-domain TFs that act during vegetative development 458

or floral transition (FLC, SVP, SOC1), while ~17% of the AP1-bound genomic regions show an 459

overlap with these factors (D. Chen, personal communications). 460

Targeted in vivo immunoprecipitation experiments of several MADS-domain TFs and other 461

studies revealed that MADS-domain proteins can form larger complexes with other 462

transcriptional regulators (Brambilla et al., 2007; Simonini et al., 2012; Smaczniak et al., 2012b), 463

and as such could bind the DNA. Whether the presence of other cofactors or other TFs that 464

interact with the MADS-domain TFs can modulate DNA-binding specificity remains an 465

important question to be resolved in future studies. Similarly, the chromatin status may have an 466

impact on recruitment of specific complexes. 467

Our in vivo AP3 promoter studies suggest that the expression of MADS-domain TF target 468

genes could be modulated by single nucleotide changes in their regulatory regions. In agreement 469

with these results, it was shown that differences between the spatio-temporal level of expression 470

of AP1 and CAULIFLOWER (CAL), two recent Arabidopsis duplicated genes, was determined 471

by the presence or absence of individual, functionally important TFBSs in regulatory regions (Ye 472

et al., 2016). Together, the findings suggest that changes in individual MADS-domain TFBSs 473

contribute to regulating spatio-temporal levels of target gene expression, but do not provide a 474

strict on/off regulation of target gene expression. 475

476

METHODS 477

SELEX-seq 478

The dsDNA libraries were made from the ssDNA sequences by single-cycle PCR amplification 479

with a complementary primer essentially as described before (Jolma et al., 2010). The dsDNA 480

16

libraries contained 20 random nucleotide fragment flanked by specific barcodes that allowed for 481

later characterization when multiplexed in high-throughput sequencing. The dsDNA libraries 482

contained all necessary features required for direct sequencing with an Illumina platform (Jolma 483

et al., 2010). 484

Protein dimers were synthesized using TNT SP6 Quick Coupled Transcription/Translation 485

System (Promega) following the manufacturer’s instructions in a total volume of 20 µl and 486

equimolar expression plasmid concentrations. The binding reaction mix was prepared essentially 487

as described previously for EMSA experiments (Egea-Cortines et al., 1999; Smaczniak et al., 488

2012b) and contained 20 µl of in vitro-synthesized proteins and 50–100 ng of dsDNA library in a 489

total volume of 120 µl. The binding reaction was incubated on ice for 1 h followed by 1 h 490

immunoprecipitation with protein specific antibodies (affinity-purified, polyclonal peptide 491

antibodies, Eurogentec) coupled to magnetic beads (MyOne, Invitrogen) in a thermomixer at 4 492

°C with constant mixing at 700 rpm. Antibodies were raised against the protein-specific C-493

terminal domain (the C-domain) or last part of the K-domain of MADS-domain proteins. These 494

domain parts are not responsible for protein-DNA (the M-domain is) or protein-protein 495

interactions (the I- and first part of K-domain are), rather they are assumed to stabilize higher-496

order protein complex formation (Kaufmann et al., 2005). Therefore we expected no influence of 497

antibody-protein interaction on studied protein-DNA interactions. The following synthetic 498

peptides were used for immunizations: LNPNQEEVDHYGRHH for SEP3; 499

EQWDQQNQGHNMPPPLPPQQ for AP1; PPQTQSQPFDSRNYFC and 500

QPNNHHYSSAGRQDQT for AG. The SEP3 antibodies were previously used in ChIP-seq 501

experiments (Kaufmann et al., 2009; Pajoro et al., 2014). Magnetic beads with attached 502

antibodies where prepared in advance according to the manufacturer’s instructions (MyOne, 503

Invitrogen) with purified antibodies resuspended in 1X PBS (~1 mg/ml); 20 µg of antibodies and 504

0.5 mg of beads were used for a single binding reaction. After immunoprecipitation, beads were 505

washed 5 times with 150 µl of binding buffer without salmon-sperm DNA and bound DNA was 506

eluted with 50 µl 1X TE in a thermomixer at 90 °C with full mixing speed. Afterwards, magnetic 507

beads were immobilized and the supernatant was transferred to a 1.5-ml tube. DNA fragments 508

were amplified with 10 to 15 cycles of PCR with SELEX round-specific primers (Jolma et al., 509

2010) and the total amplicon was used in the subsequent SELEX round. The amplification 510

efficiency was checked on the agarose gel by comparing to a known concentration of a standard 511

17

probe. Samples for sequencing, after amplification, were cut out from agarose gel and purified 512

using MinElute Gel Extraction Kit (Qiagen). Different libraries were multiplexed by mixing in 513

equimolar amounts with 40% PhiX Control (Illumina) in the Elution Buffer (Qiagen) and 514

sequencing was performed on the HiSeq 2000 or GAII sequencers (Illumina). 515

516

Obtaining Relative Affinities from the SELEX-seq Data. 517

Sequence reads that did not pass the filter quality of CASAVA 1.8 or mapped with no 518

mismatches to the phix174 genome were eliminated. The remaining sequences in fasta format 519

were extracted and grouped according to library specific barcodes, allowing no mismatches. 520

Barcodes were removed leading to 20 bp sequence libraries used in the data analysis. The 20 bp 521

sequences that were present in libraries in an unexpected high number (>1000) were eliminated, 522

as well as 20 bp reads containing the sequence “TCGTATGCCG” which is part of the Illumina 523

adapter sequence used for sequencing. 524

Data analysis was essentially performed as described before (Slattery et al., 2011). Because 525

the number of sequenced reads in each SELEX round (Supplemental Table 1) is much smaller 526

than the 1012

possible different 20-mer sequences, we calculated which k-mer length should be 527

used to obtain the maximum gain in information. For this, we computed the information gain 528

(the Kullback–Leibler divergence of Round 3 relative to Round 0) for each k-mer length. For 529

most of libraries the length 12 bp was the most optimal (Supplemental Figure 3), therefore, we 530

based the further analysis on the 12-mer sequences. 531

Frequencies of 12-mer sequences in each round except Round 0 was calculated directly from 532

the data using the function oligonucleotideFrequency from the Bioconductor R package: 533

Biostrings. The 12-mer sequences that were present in libraries in an unexpected high number 534

(>1,000) were eliminated at this step. 535

Sequences in Round 0 represent a set of randomly synthetized oligonucleotides and their 536

complexity did not allow for the direct calculation of 12-mer frequencies. Therefore, the 537

sequence frequency in Round 0 was estimated by the sixth-order Monte Carlo model, as 538

proposed before (Slattery et al., 2011). We chose the sixth-order Monte Carlo model, because 539

when the model was trained using 75% of the sequencing data, it resulted in the highest 540

prediction value as measured by the Pearson correlation coefficient between the predicted and 541

observed frequencies in the other 25% of the sequencing data. 542

18

Relative affinity for each possible 12-mer was calculated as the ratio between the frequencies 543

of 12-mers in Round 3 to Round 0, and normalized to 1 by dividing for the highest affinity-544

predicted 12-mer. These affinities can be download in Excel file format from the GEO omnibus 545

submission GSE95730. 546

547

Combining SEP3 ChIP-seq and SELEX-seq Data to Predict MADS-domain Dimer TFBSs 548

To in silico predict genomic regions bound by a given MADS-domain dimer based on our 549

SELEX-seq experiments, we obtained the affinity value for each k-mer of length 13 bp. For each 550

studied MADS-domain protein complex, we estimated the average affinity value to a particular 551

13-mer sequence and its reverse complementary sequence. We chose 13-mers instead of 12-mers 552

for technical reasons: the mapping software ‘soapv2’ (Li et al., 2009) was only able to map 553

sequences with a minimum length of 13 bp. We created fasta files for each library containing 13-554

mer sequences in a number equal to their estimated relative affinity multiplied by 100 and 555

rounded up to the closest integer (e.g. a sequence with the relative affinity of 0.98 was present in 556

98 copies in the fasta file). Next, we mapped these fasta files to the TAIR10 genome with 557

‘soapv2’ (Li et al., 2009) allowing no mismatches. Later, the function mappedReads2Nhits from 558

the peak caller CSAR (Muino et al., 2011) was used to identify enriched regions and their 559

associated score, extending the 13 bp reads and using no control since the relative affinities were 560

already corrected by the Round 0 enrichment. This resulted in a genomic SELEX-seq score 561

proportional to the affinity of the sum of 13-mers located on the particular genomic region. 562

To relate ChIP-seq scores with the SELEX-seq scores obtained by CSAR, we reanalyzed the 563

SEP3 (4 day after induction, stage 4-5 of flower development) (Pajoro et al., 2014), AP1 (4 day 564

after induction, stage 4-5 of flower development) (Pajoro et al., 2014), and AG (5 day after 565

induction, stage 5–6 of flower development) (Ó’Maoiléidigh et al., 2013) publically available 566

ChIP-seq data using CSAR (default parameters expect the backg that was set to 3). Next, we 567

linked the ChIP-seq and SELEX-seq peak to the SEP3 ChIP-seq when their distance was shorter 568

than 500 bp. Only the top 1500 SEP3 ChIP-seq peaks were considered. Quantile normalization 569

was used to normalize the SELEX-seq peaks scores, and independently to normalize the ChIP-570

seq peak scores aligned to the SEP3 ChIP-seq peaks. When it was needed, a TFBS was 571

associated to a gene when this was located 3 kb upstream or 1 kb downstream of a gene. The 572

19

method to classify SEP3 TFBSs showed good precision/recall ratios compared to the random 573

classifier (Supplemental Figure 10). 574

575

Other Bioinformatic Analyses 576

Position weight matrices were calculated based on the extracted 20N sequences containing 12-577

mers analyzed in the heatmap with the GADEM algorithm (Li, 2009) and DNA sequence logos 578

were built with the ‘seqLogo’ R script. 579

We used the Find Individual Motif Occurrences tool (‘FIMO’; version 4.10.2) (Grant et al., 580

2011) to scan the promoter region (TSS +3000 bp ~ -1000 bp) of genes for TFBSs using the 581

binding motifs generated from SELEX-seq data (with a p-value threshold of 10e-5 and defaults 582

for other parameters). A potential interaction was assigned if there was at least one TFBS in the 583

promoter of a gene. Significant enrichment of specific and common ChIP-seq target genes for 584

AP1 (Pajoro et al., 2014) and AG (Ó’Maoiléidigh et al., 2013) that overlapped with the different 585

SELEX-seq clusters was evaluated by a hypergeometric test (using the “phyper” function in R). 586

587

EMSA, Kd Estimation and QuMFRA 588

SELEX-derived sequences for EMSAs were produced by PCR with biotin-labeled or IR-589

fluorophore-labeled primers and purified from 2% agarose gel. Electrophoretic mobility shift 590

assays were performed essentially as described before (Smaczniak et al., 2012b). The detection 591

was performed with the Odyssey Scanner (LI-COR) for IR-labeled fragments or with the 592

Chemiluminescent Nucleic Acid Detection Module Kit (Pierce) for biotin-labeled fragments. 593

Absolute dissociation constants (Kd) for various protein complex-DNA interactions was 594

performed with the EMSA-based method, essentially as described before (Riechmann et al., 595

1996a). The constant amount of the protein complex was used with the various, known 596

concentrations of the IR-fluorophore-labeled DNA. 597

QuMFRA between selected 20 bp SELEX-seq-derived DNA fragments was performed as in 598

(Muino et al., 2014). DNA fragments were labeled with different fluorophores (Cy3/Cy5 or 599

Dy682/Dy782) and binding quantification was recorder with the Molecular Imager (Bio-Rad) 600

and Odyssey Scanner (Li-Cor). 601

602

Plant Materials and Growth Conditions 603

20

In our in vivo assays for AP3 promoter activity by fluorescent protein reporter, we used 991 bp 604

AP3 promoter region and we modified the known MADS-domain TFBSs according to the 605

SELEX-seq-inferred DNA sequences. Modifications were done following the highest relative 606

affinity towards SEP3-AG protein complex (pAP3_(SEP3-AG)) and by mutating the CArG-607

boxes altogether (pAP3_mut). Modifications to the AP3_wt promoter were introduced by PCR 608

and the promoter constructs were cloned into the Gateway entry vector pCR8/GW/TOPO 609

(Invitrogen) followed by a sub-cloning via Gateway LR reaction into the destination vector 610

pGREEN:GW:NLS-GFP (Horstman et al., 2015). The pAP3_x:GFP constructs were transformed 611

into Col-0 plants using floral dip method (Clough and Bent, 1998). Plants were grown under 612

long day conditions (16 h of light/8 h of dark at 20-22°C) on soil/vermiculite mix (3:1; vol:vol) 613

in a greenhouse. Plant inflorescences were studied by confocal microscopy (Zeiss LSM 510). 614

615

AP3 Promoter Activity Assays 616

In our in vivo AP3 promoter activity experiments by dual-LUC assays (Dual-Luciferase Reporter 617

Assay System from Promega), the same promoter region of the AP3 gene was used and the same 618

modifications were studied as in the fluorescent protein reporter assays. Promoters were inserted 619

in the front of the firefly luciferase CDS and together with the control Renilla luciferase CDS 620

and corresponding effector protein complex CDS, transfected into Arabidopsis thaliana 621

protoplasts cells (Diaz-Trivino et al., 2017). Protoplasts were isolated from young leaves 622

essentially as reported before (Yoo et al., 2007). Relative value of the signal between firefly 623

LUC and Renilla LUC was calculated and normalized to the relative signal of the sample without 624

the effector protein complex. The resulting value corresponds to the activity of the promoter in 625

the Arabidopsis protoplasts cells in arbitrary units. 626

627

Accession Numbers 628

Nucleotide sequence data are available from the NCBI/GenBank data library under the following 629

accession numbers: NM_102272 (SEP3), NM_105581 (AP1), NM_118013 (AG), NM_115294 630

(AP3), NM_122031 (PI). Accession numbers used in the Supplemental Data Sets are from the 631

TAIR10 annotation of the Arabidopsis genome (www.arabidopsis.org). SELEX-seq raw 632

sequencing data are available from the Gene Expression Omnibus database under accession 633

number GSE95730. 634

21

635

Supplemental Data 636

Supplemental Figure 1. EMSA analysis of homo- and heteromeric complexes bound to the 637

SELEX DNA from round 5. 638

Supplemental Figure 2. SELEX-seq reproducibility. 639

Supplemental Figure 3. Optimal lengths of randomized fragments that sufficiently predict the 640

specificity of bound MADS-domain complexes. 641

Supplemental Figure 4. DNA-binding specificities of MADS-domain TF complexes in cluster 642

3. 643

Supplemental Figure 5. Comparison of relative binding affinities for MADS-domain protein 644

complexes obtained by different methods. 645

Supplemental Figure 6. Comparison of the absolute binding affinity (dissociation constants, 646

Kd) by EMSA with the relative binding affinity by SELEX-seq. 647

Supplemental Figure 7. Enrichment of individual consensus CArG-boxes by MADS-domain 648

TF complexes. 649

Supplemental Figure 8. DNA structure predictions of the motifs from clusters 1-3 and 650

subclusters 3a-c depicting an average value of the four DNA structural properties at each 651

dinucleotide step in the sequences. 652

Supplemental Figure 9. Comparison of the SELEX-seq and ChIP-seq data with the whorl 653

specific expression data. 654

Supplemental Figure 10. Evaluation of the AP1/AG classifier. 655

Supplemental Table 1. Summary of the high-throughput sequencing analysis. 656

Supplemental Table 2. Sequences of selected 12-mers and their corresponding 20N most 657

representative dsDNA SELEX library sequences. 658

Supplemental Table 3. The experimental DNA sequence setup and the number of observations 659

for quMFRA experiments. 660

Supplemental Table 4. Wt and modified sequences of the AP3 promoter used in EMSA and 661

quMFRA. 662

Supplemental Data Set 1. Results of the EMSA-based quMRFA experiments. 663

Supplemental Data Set 2. Measurement of DNA-binding affinity of selected protein complexes 664

for selected SELEX-seq DNA probes. 665

22

Supplemental Data Set 3. Comparison of the top 1500 SEP3 ChIP-seq TFBSs compared to the 666

AG and AP1 ChIP-seq data and the SELEX-seq data for SEP3-AG vs. SEP3-AP1 dimer. 667

Supplemental Data Set 4. The output of the ‘FIMO’ and ‘phyper’ analysis for the prediction of 668

specific and common ChIP-seq target genes for AG and AP1, based on the SELEX-seq complex-669

specific motifs. 670

671

AUTHOR CONTRIBUTIONS 672

CS, JMM, GCA, and KK designed the research. CS performed the experiments. JMM and DC 673

analyzed the data. GCA and KK supervised the project. CS, JMM, and KK wrote the manuscript. 674

All authors discussed the results and commented on the manuscript. 675

676

ACKNOWLEDGMENTS 677

We would like to thank Arttu Jolma and Jussi Taipale for providing barcoded ssDNA libraries 678

and sharing details on the dsDNA library preparation and amplification; Markus Schmid, and 679

Elio Schijlen for generating the Illumina GAII and HiSeq data; Anneke Horstman for providing 680

pGREEN:GW:NLS-GFP reporter vector; and Ikram Blilou for providing dual-LUC reporter 681

vectors. This work was supported by an NWO-VIDI grant to KK. JMM was supported by a DFG 682

grant (TRR175). 683

684

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885

FIGURE LEGENDS 886

Figure 1. SELEX-seq for MADS-domain protein complexes. 887

(A) Overview of the experimental setup for the SELEX-seq approach performed in this study. 888

(B) EMSA analysis of the DNA libraries obtained in different rounds of SELEX for the SEP3–889

AG complex. 890

(C) Enrichment of the putative CArG-box consensus sequence (CC[A/T]6GG) in the SELEX-seq 891

rounds (log scale). Frequencies (in %) at Round 3 are: 14.2%, 14.1%, 6.4%, 4.5%, 3.6%, and 892

0.3% for SEP3–AG e1, SEP3–AG e2, SEP3–AP1, SEP3–SEP3, AG–AG and AP1–AP1 893

respectively. 894

(D) Enrichment of randomly permutated CArG-box sequences in the SELEX-seq rounds (log 895

scale). SEP3–AG e1 and SEP3–AG e2 indicate two independent experiments where different 896

antibodies were used for immunoprecipitation, SEP3 antibodies (e1) and AG antibodies (e2). 897

898

Figure 2. DNA-binding specificities of MADS-domain TF complexes. 899

(A) Relative affinity heatmap based on 12-mer sequences enriched in the 3rd

round of SELEX for 900

all studied MADS-domain TF complexes. Each line in the heatmap corresponds to a single 12-901

mer DNA fragment. High relative affinities for a particular sequence are marked in yellow, low 902

relative affinities are in blue. 903

(B) Sequence logos corresponding to the three main clusters of sequences in the heatmap built 904

from the position weight matrices for all 20N sequences containing group specific 12-mers. 905

(C) Minimal DNA minor groove width predictions of the sequences from clusters 1–3. The mean 906

value differences of the minor groove width are significant with p < 0.05 (t-test) for all pairwise 907

30

comparisons. Number of sequences used in predictions (sample size) are 1298340, 416077 and 908

705199 for cluster 1, 2 and 3 respectively. 909

910

Figure 3. Comparison between in vitro SELEX-seq and in vivo ChIP-seq. 911

(A) DNA specificity plots comparing the relative binding affinities of SELEX-seq sequences 912

selected by SEP3–AG (y-axis) and SEP3–AP1 (x-axis). Each point represents a unique sequence 913

that contains a color-coded motif. Black points represent all sequences. 914

(B) Association between SELEX-seq normalized score ratios (SEP3–AG/SEP3–AP1) and ChIP-915

seq normalized score ratios (AG/AP1) for TFBSs within the top 1500 SEP3 ChIP-seq peaks. The 916

plot represents a moving average of overlapping windows of size 1 over the SELEX-seq log2 917

score ratio. Windows with less than 5 elements were not considered. 918

(C) Prediction of specific and common ChIP-seq target genes for AG and AP1 based on the 919

SELEX-seq complex-specific motifs. The heatmap shows significance (hypergeometric test) of 920

the enrichment of SELEX-seq cluster motifs obtained in Figure 2C in specific and common 921

ChIP-seq binding regions compared previously by Yan et al. (Yan et al., 2016); numerical values 922

represent gene numbers. For the raw data of this figure see Supplemental Data Set 4. 923

(D) Comparison of the TRAP-seq expression data (Jiao and Meyerowitz, 2010) with the SELEX-924

seq and the ChIP-seq data (Ó’Maoiléidigh et al., 2013; Pajoro et al., 2014). The violin plot 925

visualizes the distribution of AP1-/AG-domain expression ratios of genes containing TFBSs with 926

a certain SEP3–AG/SEP3–AP1 SELEX-seq score ratio (orange) or with a certain AG/AP1 ChIP-927

seq score ratio (blue). 928

929

Figure 4. Examples of SELEX-seq TFBSs and ChIP-seq profiles mapped to the genome of 930

Arabidopsis. (Top) AP3 genomic locus. (Bottom) SEP3 genomic locus. 931

932

Figure 5. Characteristics of SELEX-seq peaks within ChIP-seq peaks. Distribution of complex 933

specific SELEX-seq TFBSs within ChIP-seq TFBSs of SEP3 (top), AP1 (middle) and AG 934

(bottom). Shown is the frequency of distances normalized by the background frequency 935

distances outside of the ChIP-seq peaks, plotted based on the calculated p-values using the 936

hypergeometric test. A distance of zero results when the position of compared TFBSs is the 937

same. 938

31

939

Figure 6. Comparison of activities and protein-DNA binding between various MADS-domain 940

protein complexes to modified AP3 promoters. 941

(A) and (B) Promoter activity quantification in protoplasts using dual luciferase reporter assay 942

with specific protein effector complexes SEP3–AP1 (A) and SEP3–AG (B). Error bars represent 943

standard deviation. Numbers above the boxes represent t-test p-value of the difference between 944

wt and modified promoters. 945

(C) Protein–DNA relative binding intensities of various MADS-domain protein complexes 946

between modified and wt AP3 promoters studied by quMFRA. The quMFRA was performed 947

using two sets of infrared (IR)-labelled DNA sequences (Dy682 and Dy782) where IR 948

fluorophores were reciprocally exchanged between AP3 promoter sequences. Error bars 949

represent standard deviation. Sequences used in EMSA are indicated in Supplemental Table 4. 950

(D) Basal promoter activity quantification in protoplasts using dual luciferase reporter assay. 951

Error bars represent standard deviation. 952

(E) Confocal pictures of the fluorescent reporter expression patterns of the wt and modified AP3 953

promoters. Red arrows indicate the most pronounced changes in spatial GFP expression in 954

modified promoter signal compared to the wt signal. Scale bars = 50 µm. 955

washelution

amplification

binding

immunoprecipitation

re-cycle

ssDNA library

protein-DNA mix

target protein complex

anti-targetantibodies

unbound DNA

eluted DNA

immunoprecipitatedprotein-DNA complexes

SELEXrounds

evolved DNAlibrary

sequencing dsDNA library generation

A

DC

0 1 2 3

0.001

0.01

0.1

Round of SELEX

Freq

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y of

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CAr

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SEP3−SEP3SEP3−AG e1SEP3−AP1SEP3−AG e2AG−AGAP1−AP1

B Protein target: SEP3-AG

protein-DNA

complex

DNAalone

0 1 2 3

0.0002

0.0004

0.0006

0.0008

Med

ian

frequ

ency

of r

eads

with

pe

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ated

Car

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SEP3−SEP3SEP3−AG e1SEP3−AP1SEP3−AG e2AG−AGAP1−AP1

Round of SELEX

0 1 2 3

Round of SELEX

4 5 6

Figure 1. SELEX-seq for MADS-domain protein complexes. (A) Overview of the experimental setup for the SELEX-seq approach performed in this study. (B) EMSA analysis of the DNA libraries obtained in different rounds of SELEX for the SEP3-AG complex. (C) Enrichment of the putative CArG-box consensus sequence (CC[A/T]6GG) in the SELEX-seq rounds (log scale). Frequencies (in %) at Round 3 are: 14.2%, 14.1%, 6.4%, 4.5%, 3.6%, and 0.3% for SEP3–AG e1, SEP3–AG e2, SEP3–AP1, SEP3–SEP3, AG–AG and AP1–AP1 respectively. (D) Enrichment of randomly permutated CArG-box sequences in the SELEX-seq rounds (log scale). SEP3–AG e1 and SEP3–AG e2 indicate two independent experiments where different antibodies were used for immunoprecipitation, SEP3 antibodies (e1) and AG antibodies (e2).

AG−A

G

SEP3

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AP1−AP

1

SEP3

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SEP3

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SEP3

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Color Keyand Histogram

Cou

nt

1

2

3

A

Relative affinity

Position

012

012

1

2

3

1 2 3

3.03.54.04.55.05.56.0

Min

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(Å)

B

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Info

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250

Info

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ion

cont

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Info

rmat

ion

cont

ent

1 3 5 7 9 11 13 152 4 6 8 10 12 14

1 3 5 7 9 11 132 4 6 8 10 12 14

1 3 5 7 9 11 13 152 4 6 8 10 12 14

Position

Cluster

Cluster

Figure 2. DNA-binding specificities of MADS-domain TF complexes. (A) Relative affinity heatmap based on 12-mer sequences enriched in the 3rd round of SELEX for all studied MADS-domain TF complexes. Each line in the heatmap corresponds to a single 12-mer DNA fragment. High relative affinities for a particular sequence are marked in yellow, low relative affinities are in blue. (B) Sequence logos corresponding to the three main clusters of sequences in the heatmap built from the position weight matrices for all 20N sequences containing group specific 12-mers. (C) Minimal DNA minor groove width predictions of the sequences from clusters 1–3. The mean value differences of the minor groove width are significant with p < 0.05 (t-test) for all pairwise comparisons. Number of sequences used in predictions (sample size) are 1298340, 416077 and 705199 for cluster 1, 2 and 3 respectively.

A

0.2 0.4 0.6 0.8 1.0

0.2

0.4

0.6

0.8

1.0

SEP3−AP1 relative affinity

SEP3

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rela

tive

affin

ity

SEP3−AP1 specific (CyATAAATrG)SEP3−AG specific (CyAwwTnrGG)

B

Clu

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1

Clu

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2

Clu

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3

AP1−specific (555)

common (548)

AG−specific (914)3

10

30

100

-log(p-value)

SELEX-seq

ChIP-seqtarget genes

152

249

421

79

158

282

129

157

225

C D

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p < 0.02

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p < 0.33

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log2 Score ratio

−4 −2 0 2 4

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−0.4

−0.2

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log2 SELEX-seq score ratio(SEP3−AG/SEP3−AP1)

Aver

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ore

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)

SEP3-AGSEP3-AP1

AP1

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SELEX-seq score

ChI

P-se

q sc

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Figure 3. Comparison between in vitro SELEX-seq and in vivo ChIP-seq. (A) DNA specificity plots comparing the relative binding affinities of SELEX-seq sequences selected by SEP3–AG (y-axis) and SEP3–AP1 (x-axis). Each point represents a unique sequence that contains a color-coded motif. Black points represent all sequences. (B) Association between SELEX-seq normalized score ratios (SEP3–AG/SEP3–AP1) and ChIP-seq normalized score ratios (AG/AP1) for TFBSs within the top 1500 SEP3 ChIP-seq peaks. The plot represents a moving average of overlapping windows of size 1 over the SELEX-seq log2 score ratio. Windows with less than 5 elements were not considered. (C) Prediction of specific and common ChIP-seq target genes for AG and AP1 based on the SELEX-seq complex-specific motifs. The heatmap shows significance (hypergeometric test) of the enrichment of SELEX-seq cluster motifs obtained in Figure 2C in specific and common ChIP-seq binding regions compared previously by Yan et al. (Yan et al., 2016); numerical values represent gene numbers. For the raw data of this figure see Supplemental Data Set 4. (D) Comparison of the TRAP-seq expression data (Jiao and Meyerowitz, 2010) with the SELEX-seq and the ChIP-seq data (Ó’Maoiléidigh et al., 2013; Pajoro et al., 2014). The violin plot visualizes the distribution of AP1-/AG-domain expression ratios of genes containing TFBSs with a certain SEP3–AG/SEP3–AP1 SELEX-seq score ratio (orange) or with a certain AG/AP1 ChIP-seq score ratio (blue).

AP3

SEP3

ChIP SEP3

ChIP AP1

ChIP AG

SELEX SEP3-SEP3

SELEX SEP3-AG

SELEX AG-AG

SELEX SEP3-AP1

SELEX AP1-AP1

TAIR10 mRNA (+)

Coordinates

TAIR10 mRNA (−)

ChIP SEP3

ChIP AP1

ChIP AG

SELEX SEP3-SEP3

SELEX SEP3-AG

SELEX AG-AG

SELEX SEP3-AP1

SELEX AP1-AP1

TAIR10 mRNA (+)

Coordinates

TAIR10 mRNA (−)

Figure 4. Examples of SELEX-seq TFBSs and ChIP-seq profiles mapped to the genome of Arabidopsis. (Top) AP3 genomic locus. (Bottom) SEP3 genomic locus.

−600 −400 −200 0 200 400 6000

2

4

6

8SEP3-AP1

Distance

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p-v

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−600 −400 −200 0 200 400 60002468

12SEP3-SEP3

Distance−l

og p

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ue 10

−600 −400 −200 0 200 400 600

05

10

20

SEP3-AG

Distance

−log

p-v

alue

15

25

Figure 5. Characteristics of SELEX-seq peaks within ChIP-seq peaks. Distribution of complex specific SELEX-seq TFBSs within ChIP-seq TFBSs of SEP3 (top), AP1 (middle) and AG (bottom). Shown is the frequency of distances normalized by the background frequency distances outside of the ChIP-seq peaks, plotted based on the calculated p-values using the hypergeometric test. A distance of zero results when the position of compared TFBSs is the same.

pAP3_(SEP3−AG)

pAP3_mut

pAP3_wt

02

04

06

08

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1.53e−11

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0.03125

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Figure 6. Comparison of activities and protein-DNA binding between various MADS-domain protein complexes to modified AP3 promoters. (A) and (B) Promoter activity quantification in protoplasts using dual luciferase reporter assay with specific protein effector complexes SEP3–AP1 (A) and SEP3–AG (B). Error bars represent standard deviation. Numbers above the boxes represent t-test p-value of the difference between wt and modified promoters. (C) Protein–DNA relative binding intensities of various MADS-domain protein complexes between modified and wt AP3 promoters studied by quMFRA. The quMFRA was performed using two sets of infrared (IR)-labelled DNA sequences (Dy682 and Dy782) where IR fluorophores were reciprocally exchanged between AP3 promoter sequences. Error bars represent standard deviation. Sequences used in EMSA are indicated in Supplemental Table 4. (D) Basal promoter activity quantification in protoplasts using dual luciferase reporter assay. Error bars represent standard deviation. (E) Confocal pictures of the fluorescent reporter expression patterns of the wt and modified AP3 promoters. Red arrows indicate the most pronounced changes in spatial GFP expression in modified promoter signal compared to the wt signal. Scale bars = 50 µm.

Parsed CitationsBemer, M., van Dijk, A.D., Immink, R.G., and Angenent, G.C. (2017). Cross-Family Transcription Factor Interactions: An AdditionalLayer of Gene Regulation. Trends Plant Sci 22, 66-80.

Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title

Brambilla, V., Battaglia, R., Colombo, M., Masiero, S., Bencivenga, S., Kater, M.M., and Colombo, L. (2007). Genetic and molecularinteractions between BELL1 and MADS box factors support ovule development in Arabidopsis. Plant Cell 19, 2544-2556.


Clough, S.J., and Bent, A.F. (1998). Floral dip: a simplified method for Agrobacterium-mediated transformation of Arabidopsisthaliana. Plant J 16, 735-743.


de Folter, S., and Angenent, G.C. (2006). trans meets cis in MADS science. Trends Plant Sci 11, 224-231.Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title

Deng, W., Ying, H., Helliwell, C.A., Taylor, J.M., Peacock, W.J., and Dennis, E.S. (2011). FLOWERING LOCUS C (FLC) regulatesdevelopment pathways throughout the life cycle of Arabidopsis. Proc Natl Acad Sci USA 108, 6680-6685.


Diaz-Trivino, S., Long, Y., Scheres, B., and Blilou, I. (2017). Analysis of a Plant Transcriptional Regulatory Network Using TransientExpression Systems. Methods Mol Biol 1629, 83-103.


Dickerson, R.E. (1998). DNA bending: the prevalence of kinkiness and the virtues of normality. Nucleic Acids Res 26, 1906-1926.Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title

Egea-Cortines, M., Saedler, H., and Sommer, H. (1999). Ternary complex formation between the MADS-box proteins SQUAMOSA,DEFICIENS and GLOBOSA is involved in the control of floral architecture in Antirrhinum majus. EMBO J 18, 5370-5379.


Franco-Zorrilla, J.M., Lopez-Vidriero, I., Carrasco, J.L., Godoy, M., Vera, P., and Solano, R. (2014). DNA-binding specificities of planttranscription factors and their potential to define target genes. Proc Natl Acad Sci USA 111, 2367-2372.


Grant, C.E., Bailey, T.L., and Noble, W.S. (2011). FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017-1018.Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title

Gregis, V., Sessa, A., Dorca-Fornell, C., and Kater, M.M. (2009). The Arabidopsis floral meristem identity genes AP1, AGL24 and SVPdirectly repress class B and C floral homeotic genes. The Plant Journal 60, 626-637.


Gremski, K., Ditta, G., and Yanofsky, M.F. (2007). The HECATE genes regulate female reproductive tract development inArabidopsis thaliana. Development 134, 3593-3601.


Haran, T.E., and Mohanty, U. (2009). The unique structure of A-tracts and intrinsic DNA bending. Q Rev Biophys 42, 41-81.Pubmed: Author and TitleCrossRef: Author and TitleGoogle Scholar: Author Only Title Only Author and Title

Honma, T., and Goto, K. (2001). Complexes of MADS-box proteins are sufficient to convert leaves into floral organs. Nature 409,525-529.

Pubmed: Author and TitleCrossRef: Author and Title

http://www.ncbi.nlm.nih.gov/pubmed?term=Bemer%2C M%2E%2C van Dijk%2C A%2ED%2E%2C Immink%2C R%2EG%2E%2C and Angenent%2C G%2EC%2E %282017%29%2E Cross%2DFamily Transcription Factor Interactions%3A An Additional Layer of Gene Regulation%2E Trends Plant Sci 22%2C 66%2D80%2E&dopt=abstract

http://search.crossref.org/?page=1&rows=2&q=Bemer, M., van Dijk, A.D., Immink, R.G., and Angenent, G.C. (2017). Cross-Family Transcription Factor Interactions: An Additional Layer of Gene Regulation. Trends Plant Sci 22, 66-80.

http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Bemer,&hl=en&c2coff=1

http://scholar.google.com/scholar?as_q=Cross-Family Transcription Factor Interactions: An Additional Layer of Gene Regulation&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

http://scholar.google.com/scholar?as_q=Cross-Family Transcription Factor Interactions: An Additional Layer of Gene Regulation&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Bemer,&as_ylo=2017&as_allsubj=all&hl=en&c2coff=1

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http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Brambilla,&hl=en&c2coff=1

http://scholar.google.com/scholar?as_q=Genetic and molecular interactions between BELL1 and MADS box factors support ovule development in Arabidopsis&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

http://scholar.google.com/scholar?as_q=Genetic and molecular interactions between BELL1 and MADS box factors support ovule development in Arabidopsis&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Brambilla,&as_ylo=2007&as_allsubj=all&hl=en&c2coff=1

http://www.ncbi.nlm.nih.gov/pubmed?term=Clough%2C S%2EJ%2E%2C and Bent%2C A%2EF%2E %281998%29%2E Floral dip%3A a simplified method for Agrobacterium%2Dmediated transformation of Arabidopsis thaliana%2E Plant J 16%2C 735%2D743%2E&dopt=abstract

http://search.crossref.org/?page=1&rows=2&q=Clough, S.J., and Bent, A.F. (1998). Floral dip: a simplified method for Agrobacterium-mediated transformation of Arabidopsis thaliana. Plant J 16, 735-743.

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http://scholar.google.com/scholar?as_q=Floral dip: a simplified method for Agrobacterium-mediated transformation of Arabidopsis thaliana&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

http://scholar.google.com/scholar?as_q=Floral dip: a simplified method for Agrobacterium-mediated transformation of Arabidopsis thaliana&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Clough,&as_ylo=1998&as_allsubj=all&hl=en&c2coff=1

http://www.ncbi.nlm.nih.gov/pubmed?term=de Folter%2C S%2E%2C and Angenent%2C G%2EC%2E %282006%29%2E trans meets cis in MADS science%2E Trends Plant Sci 11%2C 224%2D231%2E&dopt=abstract

http://search.crossref.org/?page=1&rows=2&q=de Folter, S., and Angenent, G.C. (2006). trans meets cis in MADS science. Trends Plant Sci 11, 224-231.

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http://www.ncbi.nlm.nih.gov/pubmed?term=Deng%2C W%2E%2C Ying%2C H%2E%2C Helliwell%2C C%2EA%2E%2C Taylor%2C J%2EM%2E%2C Peacock%2C W%2EJ%2E%2C and Dennis%2C E%2ES%2E %282011%29%2E FLOWERING LOCUS C %28FLC%29 regulates development pathways throughout the life cycle of Arabidopsis%2E Proc Natl Acad Sci USA 108%2C 6680%2D6685%2E&dopt=abstract

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http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Deng,&hl=en&c2coff=1

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http://www.ncbi.nlm.nih.gov/pubmed?term=Dickerson%2C R%2EE%2E %281998%29%2E DNA bending%3A the prevalence of kinkiness and the virtues of normality%2E Nucleic Acids Res 26%2C 1906%2D1926%2E&dopt=abstract

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http://www.ncbi.nlm.nih.gov/pubmed?term=Egea%2DCortines%2C M%2E%2C Saedler%2C H%2E%2C and Sommer%2C H%2E %281999%29%2E Ternary complex formation between the MADS%2Dbox proteins SQUAMOSA%2C DEFICIENS and GLOBOSA is involved in the control of floral architecture in Antirrhinum majus%2E EMBO J 18%2C 5370%2D5379%2E&dopt=abstract

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http://www.ncbi.nlm.nih.gov/pubmed?term=Franco%2DZorrilla%2C J%2EM%2E%2C Lopez%2DVidriero%2C I%2E%2C Carrasco%2C J%2EL%2E%2C Godoy%2C M%2E%2C Vera%2C P%2E%2C and Solano%2C R%2E %282014%29%2E DNA%2Dbinding specificities of plant transcription factors and their potential to define target genes%2E Proc Natl Acad Sci USA 111%2C 2367%2D2372%2E&dopt=abstract

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DOI 10.1105/tpc.17.00145; originally published online July 21, 2017;Plant Cell

Cezary Smaczniak, Jose M. Muiño, Dijun Chen, Gerco C. Angenent and Kerstin Kaufmanntarget genes

Differences in DNA-binding specificity of floral homeotic protein complexes predict organ-specific

This information is current as of June 8, 2020

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