Transcriptome association identifies regulators of wheat …...2017/08/14 · seed s (Figure 1 A and Supplemental Figure 1) (Bonnett, 1936; Fisher, 67 1973). Like maize and rice,

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Running title: Regulators of wheat spike architecture 1

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Corresponding authors: 3

Yuling Jiao, State Key Laboratory of Plant Genomics and National Center for Plant Gene 4

Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, 5

Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China 6

Xiangfeng Wang, Department of Crop Genomics and Bioinformatics, College of Agronomy and 7

Biotechnology, National Maize Improvement Center of China, China Agricultural University, 8

Beijing, China 9

Email address: [email protected], [email protected] 10

Plant Physiology Preview. Published on August 14, 2017, as DOI:10.1104/pp.17.00694

Copyright 2017 by the American Society of Plant Biologists

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Title: Transcriptome association identifies regulators of wheat spike 11

architecture 12

Yuange Wanga,1, Haopeng Yua,b,c,1, Caihuan Tiana, Muhammad Sajjada,c, Caixia Gaod, 13

Yiping Tongd, Xiangfeng Wangb,2, Yuling Jiaoa,c,2 14

a State Key Laboratory of Plant Genomics and National Center for Plant Gene Research, 15

Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 16

100101, China 17

b Department of Crop Genomics and Bioinformatics, College of Agronomy and Biotechnology, 18

National Maize Improvement Center of China, China Agricultural University, Beijing, China 19

c University of Chinese Academy of Sciences, Beijing, 100049, China 20

d State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and 21

Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China 22

1 These authors contributed equally to this work. 23

2 Address correspondence to [email protected] or [email protected] 24

25

One-sentence Summary: Network analysis identifies wheat genes causally related to 26

spike complexity, a key determinate of crop yield potential. 27

28

29

Footnotes. Author contributions: Y.J. conceived the project. Y.J., X.W. and Y.W. 30

designed the experiments. Y.W. performed the experiments. H.Y. and Y.W. analyzed 31

the data. C.T., C.G. and Y.T. contributed reagents. Y.W., H.Y., X.W. and Y.J. wrote the 32

manuscript. 33

Funding information: This work was supported by the Chinese Academy of 34

Sciences (grant XDA08020105), the Genetically Modified Breeding Major Project of 35

China (2014ZX08010-002), the National Natural Science Foundation of China (grant 36



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31430010), the National Program for Support of Top-Notch Young Professionals, and 37

the State Key Laboratory of Plant Genomics. 38

Address correspondence to [email protected] or [email protected] 39

Keywords: branching, spike, transcriptome, wheat 40

41



4

ABSTRACT 42

The architecture of wheat inflorescence and its complexity is among the most 43

important agronomic traits that influence yield. For example, wheat spikes vary 44

considerably in the number of spikelets, which are specialized reproductive branches, 45

and the number of florets, which are spikelet branches that produce seeds. The large 46

and repetitive nature of the three homologous and highly similar subgenomes of 47

wheat has impeded attempts at using genetic approaches to uncover beneficial 48

alleles that can be utilized for yield improvement. Using a population associative 49

transcriptomic approach, we analyzed the transcriptomes of developing spikes in 90 50

wheat lines comprising 74 landrace and 16 elite varieties, and correlated expression 51

with variations in spike complexity traits. In combination with coexpression network 52

analysis, we inferred the identities of genes related to spike complexity. Importantly, 53

further experimental studies identified regulatory genes whose expression is 54

associated with, and influences spike complexity. The associative transcriptomic 55

approach utilized in this study allows rapid identification of the genetic basis of 56

important agronomic traits in crops with complex genomes. 57



5

INTRODUCTION 58

Grains of cereal crops provide a major source of human diet and nutrition. Improving 59

grain yield is a primary objective during crop domestication and a major goal of crop 60

breeding program. Inflorescence (spike) architecture dictates the capacity for seed 61

production in cereal crops, including wheat, the world’s most widely grown cereal. In 62

the archetypal bread wheat spike, the inflorescence meristem forms a limited number 63

of lateral spikelet meristems (SMs) per rachis node, and a single terminal SM at the 64

distal end. Each SM is indeterminate and typically produces two to four fertile florets 65

that produce seeds (Figure 1A and Supplemental Figure 1) (Bonnett, 1936; Fisher, 66

1973). Like maize and rice, wheat yield per plant largely depends on the number of 67

florets per spike, and thus spike architecture. The numbers of SMs (and rachises) and 68

florets per spikelet are major target traits for efforts aimed at improving wheat yield. In 69

addition, the number of SMs per rachis may be increased to increase floret number, 70

as in rare supernumerary spikelet variations. 71

In rice, maize and barley, multiple genes regulating spike development have been 72

identified (Sreenivasulu and Schnurbusch, 2012; Tanaka et al., 2013). However, our 73

understanding of wheat spike development remains rudimentary at the molecular 74

level. In wheat, increased transcription levels of Q, an AP2-like gene, was markedly 75

associated with spike compactness, suggesting that Q gene may be implicated in 76

spike development (Simons et al., 2006), although its role in spike complexity remains 77

unclear. Feng et al. (2017) reported that a durum wheat ARGONAUTE1d gene mutant 78

produced shorter spikes, and fewer grains per spike, than wild-type controls. In 79

addition, recent studies have shown the mechanisms underlying the genetic 80

regulation of rare supernumerary spikelet variations. For example, wheat 81

Photoperiod-1 (Ppd-1) was identified as a regulator of paired spikelet formation 82

(Boden et al., 2015). When Ppd-1 is mutated, a secondary spikelet initiates 83

immediately below a typical single spikelet in the same rachis node, thus forming a 84

rare supernumerary spikelet variation. In another type of variation, one or more 85

spikelets are replaced by long lateral branches, which form their own spikelets and 86



6

florets. Mutations of the WFZP-A/BHt-A1 gene, encoding an AP2/ERF transcription 87

factor, lead to such noncanonical spike-branching (Derbyshire and Byrne, 2013; 88

Dobrovolskaya et al., 2015; Poursarebani et al., 2015), which is similar to the 89

branching produced by mutating its orthologs in maize, rice and Brachypodium 90

distachyon (Chuck et al., 2002; Komatsu et al., 2003). Although these recent 91

breakthroughs shed light on the molecular mechanisms underlying rare 92

supernumerary spikelet variations, little is known about genetic factors affecting the 93

architecture of archetypal wheat spike, its complexity, and grain yield. 94

The allohexaploid common bread wheat genome is approximately 17 gigabases 95

in size and consists of three sets of subgenomes (A, B and D) derived from closely 96

related species. An enormous amount of genomic sequencing has been performed to 97

build a reference sequence for wheat (Brenchley et al., 2012; International Wheat 98

Genome Sequencing Consortium, 2014), which enhanced our understanding of the 99

wheat genome significantly. Nevertheless, the genetic complexity associated with 100

wheat hampers map-based cloning and genome-wide association studies (GWAS). 101

Although GWAS has been applied to wheat (for example Guo et al., 2017; Maccaferri 102

et al., 2015; Sun et al., 2017), it remains highly challenging to pinpoint causal genes 103

within identified genetic loci by a GWAS approach. 104

With a long history of cultivation and artificial selection in diverse ecological zones, 105

common wheat in China has a rich genetic diversity (He et al., 2001). A mini core 106

collection of 231 Chinese wheat varieties, which is estimated to represent 107

approximately 70% of the genetic diversity of the 23,090 varieties (Hao et al., 2008); 108

widely used germplasm for wheat breeding in China. Based on geographical regions 109

and allelic diversity previously reported by Hao et al. (2011), we selected 90 winter 110

wheat varieties from 142 winter varieties within the mini core collection, including 74 111

landraces and 16 elite varieties (Figure 1B, Supplemental Table 1), for transcriptome 112

association analysis (Harper et al., 2012). By quantitatively correlating trait variation 113

with variation in gene expression, we aimed to identify the gene regulatory network 114

underlying the development of spike architecture. 115



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116

RESULTS 117

Variation in Spike Complexity among Mini Core Collection Varieties 118

We planted 90 wheat varieties at the same experimental site for two years with three 119

replications. At least 60 seeds per variety per year were planted within each 120

replication. We found that the 90 varieties have flowered at slightly different times (~3 121

d). For each year, spikes were hand dissected from the main shoots of each variety at 122

the same double ridge stage, when spikelet primordia occur between bract primordia 123

at the middle part of the spike, after which we extracted mRNA for transcriptome 124

sequencing. At the double ridge stage, SMs have emerged to produce spikelets 125

(Bonnett, 1936). In addition, florets are growing out from spikelets in the middle 126

section, whereas new SMs are still being produced at the distal end at this stage 127

(Figure 1A, Supplemental Figure 1). We also counted the number of spikelets, 128

number of florets per spike, and number of seeds per spike 20 days after flowering 129

(Supplemental Tables 2-4). We observed positive correlations among the number of 130

spikelets, florets per spike, and seeds per spike across the 90 selected wheat 131

varieties (Figure 1C and D). This finding indicates that the number of spikelets, which 132

is determined during early spike development, mainly controls the numbers of florets 133

and grains per spike. Additionally, the number of spikelets and florets across the two 134

planting years showed significantly high correlations (R = 0.864 and 0.963, P = 135

6.5e-82 and 5.0e-154, respectively), whilst a moderate correlation (R = 0.566, P value 136

= 3.1e-24) was observed for the number of seeds, indicating that the latter trait is 137

more influenced by the environment (Figure 1E-G). 138

139

Spike Transcriptome Variation among Varieties 140

The RNA-Seq reads for double ridge stage spikes were mapped to the IWGSC 141

genome survey sequence (International Wheat Genome Sequencing Consortium, 142

2014). On average, 17.5 million read pairs per variety sample were uniquely mapped 143

to the genome (Figure 2A, Supplemental Tables 5-6). Saturation analysis showed that 144



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the relative transcript abundance quantification error dropped below 5% when the 145

resampling size was increased to 50%, indicating that the sequencing depth was 146

adequate (Supplemental Figure 2). Using a criterion of RPKM value ≥ 1 for gene 147

expression, 58,494 of 100,344 annotated wheat genes were expressed in at least one 148

variety, whereas 30,638 annotated wheat genes were expressed across all varieties 149

(Figure 2B, Supplemental Table 7). In addition, the number of transcripts derived from 150

each subgenome was roughly equal (19,162 (32.8%)) from subgenome A, 19,629 151

(33.7%) from subgenome B and 19,703 (33.6%) from subgenome D) (Supplemental 152

Tables 8 and 9). 153

Next, we identified subgenomic homeologs by considering both nucleotide 154

homology (70% similarity) and chromosomal linearity, after which we categorized the 155

58,494 genes that were expressed in at least one variety into 24,230 homeologous 156

groups (HGs) by following the method described by Pfeifer et al. (2014). 157

Approximately 75% of the expressed genes were categorized into HGs containing at 158

least two subgenomic counterparts (Figure 2C, Supplemental Table 10), with 18,180 159

(41.5%) genes categorized into HGs containing three subgenomic homeologs (ABD 160

type), 14,952 (31.1%) genes categorized into HGs containing two homeologs (AD, AB 161

or BD types), and 10,694 (24.4%) subgenome-unique genes (A, B or D types). Among 162

the three types of HGs, the ABD group has the highest proportion (approximately 80%) 163

of expressed genes across the selected varieties, while the expressed proportions in 164

the AB, BD, and AD groups range from 70% to 73%, and the subgenome-unique 165

genes have the lowest proportion of expressed genes (Figure 2D). Based on the 166

alignment of RNA-Seq reads against the reference genomes, we identified 1,764,048 167

single nucleotide polymorphisms (SNPs) with minor allele frequency (MAF) ≥ 3% and 168

236,849 indels in wheat transcripts (see Materials and Methods). A similarity was 169

observed between structures of phylogenetic trees based on exonic SNPs and simple 170

sequence repeat (SSR) markers, respectively (Figure 2E) (Hao et al., 2011). 171

172

Transcriptome Association with Spike Complexity 173



9

Further, we performed a transcriptome association analysis to correlate gene 174

expression signatures (GESes) with trait variation. To summarize the observed spike 175

trait values from three replicates of each variety over two years, we used the best 176

linear unbiased estimation method, in which the variety setting was set as a fixed 177

effect, whereas both year and the interaction between year and variety were set as 178

random effects (Supplemental Tables 2-4). Next, we correlated gene expression 179

levels with spike complexity traits across 90 varieties using Spearman's correlation 180

coefficient. The expression levels of 1,538 genes were significantly correlated (FDR ≤ 181

0.05) with the number of spikelets per spike, whereas the expression levels of 105 182

genes were significantly correlated (FDR ≤ 0.05) with the number of florets per spike. 183

No gene showed expression correlation with the number of seeds per spike 184

(Supplemental Table 11). In addition, correlation strength varied among the three 185

studied traits. Spikelet number showed the strongest correlations with gene 186

expression levels, followed by floret number and seed number per spike, as illustrated 187

by the distribution of correlation significance for genes along each chromosome 188

(Figure 3A, Supplemental Figure 3). As we sampled RNAs from spikes at the double 189

ridge stage, thus our data mainly reflect gene expression during spikelet formation, 190

but not during seed development. Thus, the observation that the number of spikelets 191

per spike showed the strongest correlation with expression levels was consistent with 192

our experimental design. The finding that few GESes were correlated with the number 193

of florets per spike or number of seeds per spike indicates that the transcriptomic 194

context may have dramatically changed during the progression from spikelet initiation 195

to floret initiation and seed formation. 196

To infer the functions of the 1,538 GESes found to be correlated with the number 197

of spikelets per spike, we performed a gene ontology (GO) enrichment analysis to 198

identify GO categories significantly enriched with GESes. The set of all expressed 199

genes was used as the background set. Interestingly, GESes that were positively (754 200

GESes) and negatively (784 GESes) correlated with the number of spikelets per spike 201

showed enrichment in distinct functional pathways. While the “fatty acid 202



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beta-oxidation”, “DNA methylation”, and “phenol and thiamin process” categories were 203

enriched in positively correlated GESes, the “transcription and epigenetic regulation of 204

development” and “energy storage processes” (including “photosynthesis and glucose 205

biosynthesis” and “small molecule metabolic process”) categories were enriched in 206

negatively correlated GESes (Figure 3B). This pattern suggests a genome-wide 207

transcriptional switch involving up- and down-regulation of multiple molecular 208

pathways during the process of spikelet initiation. 209

210

Correlations of Phenotypic, Genotypic and Transcriptomic Signatures 211

Based on the expression patterns of 1,538 GESes, we calculated the Euclidean 212

distance matrix, and hierarchically clustered 90 wheat varieties into three distinct sets 213

by using the complete linkage method (Figure 3C). Interestingly, the three sets of 214

varieties showed different ranges of spikelet number per spike; wheat varieties in set 215

1 and 2 have the highest and lowest spikelet numbers, respectively, while those in set 216

3 have a spikelet number average to that of set 1 and 2 (Figure 3D). This pattern 217

strongly suggests that transcriptomic signatures are correlated with phenotypic 218

quantity; this relationship can be used to discover trait-related genes. 219

To assess the consistency between expression variation and sequence variation, 220

we combined transcriptomic signature-based variety sets with the transcript 221

SNP-based phylogenetic tree (Figure 2E). The grouping pattern based on sequence 222

variation was partially correlated with that based on expression variation. Varieties in 223

set 1 and 2 were grouped separately, while the varieties in set 3 were spread between 224

set 1 and 2. This clear association between transcriptional patterns and spikelet 225

number implies that a set of major regulators of spike complexity can be identified via 226

transcriptome association analysis. 227

228

Correlation between GESes and Spikelet Number per Spike 229

Among the 1,538 GESes described above, we performed further analysis on a set of 230

230 wheat HGs orthologous to rice genes related to panicle and grain traits in spike 231



11

development (Supplemental Table 12), according to the rice OGRO database 232

(Yamamoto et al., 2012). Out of 230, 60 orthologous GESes showed statistically 233

significant negative correlation with number of spikelets, while 17 showed statistically 234

significant positive correlation with the number of spikelets (P value < 0.05) (Figure 4A, 235

Supplemental Table 12). The greater number of negatively correlated GESes in our 236

results suggests that a group of negative regulators may play important roles in 237

switching off pathways that may positively influence spikelet number during spike 238

development. 239

To further screen for key regulatory genes, we performed gene coexpression 240

network (GCN) analysis (Wisecaver et al., 2017). As homeologous genes among the 241

three subgenomes tend to show similar expression patterns, this may introduce 242

redundancy while calculating correlations. For this, we followed a previously 243

published method in which the average expression values of homeologs are used to 244

perform GCN analysis (Li et al., 2014; Pfeifer et al., 2014). First, we selected 7,945 245

genes with highly variable transcript abundance across the 90 varieties selected for 246

this study (squared coefficient of variation (CV2) ≥ 0.25). A weighted GCN was 247

constructed using the WGCNA package for R (Langfelder and Horvath, 2008), 248

followed by decomposition of the network into 16 subnetwork modules. Each module 249

contains a set of genes showing significant expression correlations with each other. 250

An eigen-value was calculated to represent the overall expression trend in each 251

variety for each module. For each module, the correlations between the eigen-value 252

and the number of spikelets, florets, and seeds per spike were computed across 90 253

varieties (Figure 4B). Notably, Module 9 exhibited a significantly negative correlation 254

with all three spike traits (P value < 0.05), the strongest of which was with the spikelet 255

number per spike (Spearman's rho test, R = -0.4, P value = 5.0e-5) (Figure 4C). 256

Therefore, Module 9 may harbor major regulators influencing the entire period of 257

spike development, and thus strongly influencing wheat grain production. 258

Because transcription factors (TFs) are important direct modulators of gene 259

expression, we focused on TF-encoding genes. Seven modules, which include 76.2% 260



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(6,052 of 7,945) of highly variable HGs, were enriched in TF-encoding genes (P value 261

≤ 0.5, hypergeometric test). This observation suggests that TFs are involved in spike 262

trait variations. Among the 16 modules, Module 9 has the greatest degree of 263

enrichment of TF-encoding genes (12.9% TFs compared to 4.4% TFs genome-wide, 264

hypergeometric test, P value = 6.4e-10) (Supplemental Table 13). The intriguing 265

features of Module 9 led us to focus on this module for further analysis. Using a more 266

stringent cutoff of adjacency ≥ 0.02, we refined Module 9 to obtain a core subnetwork 267

of 125 HGs (Figure 4C, Supplemental Table 14). After manual curation, 32 of 125 268

(25.6%) core HGs were found to encode TFs, including 13 MADS-box TFs, which 269

have been reported to be important regulators of floral development. In addition, 7 270

genes belonging to the cytochrome P450 superfamily and 3 genes related to hormone 271

signaling or transport were contained in the refined module. 272

273

Experimental Validation of Candidate Spike Regulators 274

The results of the transcriptome analysis, especially the identified trait-correlated 275

GESes, provide a rich resource of genes that could be important for efforts aimed at 276

improving wheat grain production. As a proof-of-concept, we selected ten genes for 277

experimental verification (Supplemental Figure 4A). Amongst which, six genes have 278

either expression significantly correlated with spikelet number (P value ≤ 0.05), and/or 279

present in Module 9 (Supplemental Table 14). In details, TaPAP2, WFZP and TaLAX1 280

are found in both groups, TaRA3 and TaTFL1 are only correlated with spikelet number, 281

and TaVRS1 is only found in Module 9. All these six genes are annotated as 282

development regulators by GO and/or MapMan. We further selected four additional 283

genes (TaAPO1, TaLAX2, TaREV and TaVT2) annotated as development regulators 284

but have no expression correlation with spike complexity for experimental verification. 285

As all candidates are HGs, we selected the homeolog showing the strongest 286

correlation with spike traits for transgenic validation. We overexpressed the selected 287

wheat genes in KN199, an elite hexaploid wheat variety with intermediate spike 288

complexity. For each gene, we obtained more than 45 independent transformants, 289



13

which were subjected to analysis of transgene expression levels and spike trait 290

phenotypes. Overexpression of three out of the ten tested genes significantly altered 291

spike complexity. Overexpression of TaTFL1-2D increased spike complexity, while 292

overexpression of TaPAP2-5A or TaVRS1-2B reduced spike complexity (Figure 5A, 293

6A and 6D). Notably, all these genes have expression correlation with spike 294

complexity. 295

TaTFL1-2D, a member of positively correlated Module 14, encodes a putative 296

transcription cofactor sharing high protein sequence homology with Arabidopsis TFL1, 297

a regulator of inflorescence meristem identity (Bradley et al., 1997). We found that the 298

TaTFL1-2D transgene expression level was positively correlated with spikelet number, 299

floret number, and seed number per spike (Figure 5B and C, Supplemental Figure 4B). 300

Our analysis of spike development in the transgenic lines indicated that the double 301

ridge stage and the following floret stage were significantly extended in TaTFL1-2D 302

overexpressing lines (Figure 5D). During the double ridge stage, SMs emerge to 303

produce spikelets, while florets grow out from spikelets in the middle section (Figure 304

1A). During the floret stage, most of the florets initiate glumes and lemmas to 305

complete floret formation (Bonnett, 1936). The prolonged double ridge and floret 306

stages in the TaTFL1-2D overexpressing lines resulted in the formation of extra 307

spikelets per spike and thus more florets per spikelet (Figure 5E-L) which 308

considerably enhance spike complexity. 309

TaPAP2-5A and TaVRS1-2B, both contained in Module 9, encode a MADS family 310

transcription factor and a putative HD-ZIP family transcription factor, respectively. 311

TaPAP2-5A shares high protein sequence homology with rice PAP2, a SM identity 312

regulator (Kobayashi et al., 2010). TaVRS1-2B shares high protein sequence 313

homology with barley Vrs1, a regulator of spikelet development (Komatsuda et al., 314

2007). Overexpressing either TaPAP2-5A or TaVRS1-2B reduced the spikelet number, 315

floret number, and seed number per spike in a dosage dependent manner (Figure 6A, 316

B, D and E, Supplemental Figure 5D, E, G and H). In transgenic overexpressing lines, 317

either TaPAP2-5A or TaVRS1-2B reduces the lengths of the double ridge stage, floret 318



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stage, stamen development stage and anther development stage (Figure 6C and F). 319

As a result, these transgenic plants develop spikes with fewer spikelets per spike and 320

florets per spike in comparison with control lines transformed with empty vectors 321

(Figure 6G-R). Whereas overexpressing TaPAP2-5A inhibits SM formation, consistent 322

with the function of its rice ortholog, the effect of overexpressing TaVRS1-2B diverges 323

from that of overexpressing its barley ortholog Vrs1. Barley Vrs1 suppresses lateral 324

spikelet development to form rudimentary lateral spikelets (Komatsuda et al., 2007). 325

In contrast, wheat TaVRS1-2B inhibits SM formation, as we did not detect rudimentary 326

spikelets, but found a reduced number of spikelets per spike in TaVRS1-2B 327

overexpressing lines. In addition, wheat TaVRS1-2B also inhibits floret meristem (FM) 328

formation. 329

Taken together, the results of our transcriptome analysis reveal genes associated 330

with, and may be causally related to, spike complexity, providing a rich resource of 331

genes that could be used to improve wheat grain yield. Appropriate expression of 332

these genes can increase the number of productive spikelets, as well as the number 333

of productive florets per spike, thus enhancing spikelet complexity and increasing the 334

grain yield per plant. 335

336

DISCUSSION 337

Identifying genes conferring traits of agronomic importance is of enormous 338

significance to crop improvement. Map-based cloning and GWAS in crops with 339

relatively simple genomes, such as rice and maize, have been used to reveal 340

agronomically important genes, but such study remains challenging in polyploid crops 341

with complex genomes. In fact, our attempt to use genetic variations identified in the 342

RNA-seq data to perform GWAS analysis did not yield any significant locus. To 343

circumvent this difficulty, we used transcriptome association analysis and GCN 344

analysis to correlate gene expression with trait variation. By using the recently 345

released wheat genome sequence (International Wheat Genome Sequencing 346

Consortium, 2014), we applied this analysis method to allohexaploid common wheat 347



15

to study spike complexity. 348

Spike complexity determines the number of seeds per spike. Manipulation of 349

spike complexity is a major strategy for improving yield potential. Whereas a large 350

number of inflorescence regulators have been identified in other plant species, 351

understanding of wheat spike development is relatively poor. Our transcriptome 352

survey of a representative mini core collection identified a large number of potential 353

regulators of wheat spike development. We identified a large number of candidate 354

genes whose expression levels were positively or negatively associated with spike 355

complexity. Although we quantified three traits related to spike complexity, namely the 356

number of spikelets, florets per spike, and seeds per spike, we found the number of 357

spikelets per spike had by far larger number of positively and negatively correlated 358

genes. This may reflect less environmental contribution but more genetic contribution 359

to this trait. Thus, our coexpression analysis focused mostly on the number of 360

spikelets per spike to obtain more reliable results. 361

Notably, the number of negative regulators of spike complexity was substantially 362

greater than the number of positive regulators of spike complexity. We speculate that 363

this finding may reflect a general inhibition of branching by the reproductive 364

development program (Hagemann, 1990). As branching is the primitive condition for 365

shoot growth, the onset of flowering significantly alters this development pattern from 366

indeterminate to determinate, thus restricting branching ability and resulting in 367

reduced complexity. Spike complexity depends on the remaining branching ability of 368

the shoot apical meristem before its full termination into a flower. Most of the flowering 369

genes are negative regulators of branching and show dominant expression after the 370

floral transition, it is also conceivable that there are many negative regulators of spike 371

complexity. Indeed, we identified many genes related to flower development, including 372

those encoding MADS-box TFs in Module 9, as putative negative regulators of spike 373

complexity. 374

Importantly, our results demonstrate that overexpression of one of the three 375

candidate genes, TaTFL1-2D, TaPAP2-5A and TaVRS1-2B, affects inflorescence 376



16

development in wheat, and suggest that appropriate selection of alleles of favored 377

expression or modifications in these genes could be used to increase wheat spike 378

complexity and thus grain yield. Furthermore, our analysis identified wheat-specific 379

functions of known spike regulators. For example, TaVRS1 overexpression negatively 380

regulates spike branching by inhibiting SM initiation, whereas orthologous barley Vrs1 381

suppresses lateral spikelet outgrowth, but not SM initiation (Komatsuda et al., 2007). 382

The neofunctionalization of TaVRS1 suggests fast evolution of the gene regulatory 383

network underlying spike development in grasses. Detailed analysis of the spike 384

development program in transgenic plants indicated that the duration of SM and FM 385

formation stages are correlated with, and are likely causal to, spike complexity. This 386

observation shows that the duration of remaining branching growth (before 387

termination into flowers) is critical to the regulation of spike complexity. Our analysis 388

also suggests that the processes of SM and FM formation utilize conserved regulatory 389

modules, as all three genes that were experimentally validated in this study regulate 390

both number of spikelets and number of florets, which are results of the two major 391

branching events in wheat spike branching. 392

In addition to genetic factors, environmental adaption also has profound effects 393

on yield. In this study, we used the same growth condition for all varieties to minimize 394

environmental effects, and to focus on genetic contributions to spike complexity. To 395

this end, we selected only winter wheat varieties that can normally grow in Beijing. 396

Future experiments in multiple sites would resolve the interaction between genetics 397

and environment. 398

399

CONCLUSIONS 400

Early reproductive development in wheat is essential for grain number per spike, and 401

hence the wheat yield potential. However, the allohexaploid wheat genome makes 402

genetic dissection highly challenging. Recent breakthroughs in genome sequencing 403

has enabled transcriptome analysis possible for this important food crop (Feng et al., 404

2017; Li et al., 2014). In this study, we analyzed the transcriptomes of young spikes in 405



17

90 winter wheat lines, and correlated expression with variations in spike complexity 406

traits (spikelets number per spike, florets number per spike, and seed number per 407

spike).Together with weighted gene coexpression network analysis, we inferred 408

candidate genes that may relate to spike complexity. Furthermore, experimental 409

studies identified genes whose expression is not only associated with, but also affects 410

spike complexity. The associative transcriptomic method employed in the present 411

work may allow us to identify genetic basis of agronomically important traits in 412

common wheat or other crops with complex genomes. 413

414

MATERIALS AND METHODS 415

Plant Materials and Growth Conditions 416

A total of 90 winter wheat (Triticum aestivum L.) varieties were grown at the 417

Experimental Station of Institute of Genetics and Developmental Biology, Chinese 418

Academy of Sciences, Beijing for two consecutive years (planted in September in 419

2013 and 2014) with three replications and were arranged using Randomized 420

Complete Block Design. For each replication, 60 wheat seeds per variety per year 421

were sown in two 1.5-m-long rows with 25 cm row-row distance. For each variety in 422

each replication, reproductive tissues at the early double ridge stage (see below and 423

Figure 1A) from 10 randomly selected spikes of the main shoot were collected for 424

transcriptome analysis. At the early double ridge stage, spikelet primordia occur 425

between bract primordia at the middle part of a spike. For sample collection, leaves 426

surrounding the young spike were removed by hand and the reproductive tissue 427

(without stem) was cut with a sharp blade under a stereomicroscope to confirm 428

developmental stage. In total, 30 spikes per variety per year were collected and the 429

reproductive tissues were further sampled. The reproductive tissues were frozen in 430

liquid nitrogen, after which total RNA was extracted using the RNA Miniprep Kit 431

(Axygen). Equal amounts of total RNA from each year were pooled together for each 432

variety. At least 3 µg of total RNA from each variety was used to construct a 433

sequencing library using the NEBNext Ultra RNA Library Prep Kit for Illumina (New 434



18

England Biolabs). Paired-end sequencing libraries with an insert size of approximately 435

250 bps were sequenced on an Illumina HiSeq 2500 sequencer. The number of 436

spikelets per spike, number of florets per spike, and the number of seeds per spike 437

were also investigated for 10 randomly selected plants in each replication 20 days 438

after flowering. 439

440

Evaluation of Wheat Spike Development Stages 441

The process of wheat spike development was considered as five separate stages 442

(Bonnett, 1936; Fisher, 1973). Briefly, the spike elongation stage (stage I) refers to the 443

stage when the shoot apex growing point elongates, but its outline remains smooth. 444

During the following single ridge stage (stage II), prematurely ceased foliar primordia 445

appear as ridges surrounding the stem apex. The intervening tissue between two 446

neighboring single ridges then enlarges to form an additional ridge (a SM) in 447

conjunction with the subtending single ridge, marking the double ridge stage (stage 448

III). In the following differentiation stage (stage IV), glumes, followed by lemmas, 449

initiate from SMs. Florets initiate as lateral swellings above the lemma initials. In the 450

stamen and pistil stage (stage V), three stamens and one pistil emerge in the middle 451

part of a spike, while upper spikelets develop floret primordia. 452

453

Read Alignment and Gene Expression Quantification 454

We downloaded the reference wheat genome sequence (IWGSC CSS + POPSEQ) 455

and gene annotation from the Ensembl plant database 456

(ftp://ftp.ensemblgenomes.org/pub/plants/release-28/). We also downloaded a 457

recently released wheat genome assembly and the INSDC TGACv1 gene annotation. 458

We mapped RNA-Seq reads to both of the assemblies and gene annotation, as well 459

as the pre-released IWGSC WGA v0.4 assembly that lacks gene annotation. We 460

detected more expressed genes by using IWGSC CSS + POPSEQ (Supplemental 461

Table 16), although INSDC TGACv1 and IWGSC WGA v0.4 gave slightly higher 462

mapping rates of reads (Supplemental Table 17). We also found that aligning to 463


ftp://ftp.ensemblgenomes.org/pub/plants/release-28/


19

IWGSC CSS + POPSEQ and INSDC TGAC v1 showed highly similar gene 464

expression profiles (Supplemental Figure 6). Thus, we chose IWGSC CSS + 465

POPSEQ as a reference genome and annotation, which are expected to give results 466

similar to those of other assemblies. 467

For read alignment and expression quantification, we first removed low quality 468

reads, after that, we mapped the remaining reads to the reference genome using 469

STAR version 2.4.2a (Dobin et al., 2013), allowing mismatches of 6 nts at most on the 470

paired end reads. To eliminate false discovery of split junction reads, the intron length 471

was set to 60–6000 nts. Using HTSeq version 0.6.0, we counted uniquely mapped 472

reads, normalized the read count by the trimmed mean of M values (TMM), and 473

transformed the results to reads per kilobases per million reads (RPKM) using edgeR 474

version 3.12.1 (Robinson et al., 2010). Low abundance genes with an expression 475

cutoff of RPKM ≥ 1 in at least one variety were removed from the set. 476

477

Phylogenetic Tree Construction 478

Construction of the phylogenetic tree was based on the SNPs identified from the 479

RNA-Seq data. First, we analyzed each variety using GATK version 3.3 (Van der 480

Auwera et al., 2013) (Supplemental Figure 8). As the GATK workflow failed to report 481

homozygous SNP sites before imputation, we recalled missing genotypes if they were 482

covered by RNA-Seq reads. After filtering out SNPs with MAF ≤ 3%, we imputed 483

missing genotypes again using beagle version 4.1 (Browning and Browning, 2016). To 484

obtain a set of representative SNPs for phylogenetic tree construction, we kept only 485

SNPs anchored on chromosomes with LD ≤ 0.2. We randomly selected 20,000 SNPs 486

for the construction of phylogenetic tree using maximum likelihood method implemented 487

in DNAML (Felsenstein, 1989). 488

489

Identification of Homeologous Groups (HGs) 490

To avoid misidentification of HGs because of paralogous genes within the same 491

subgenome, we considered both sequence homology and chromosomal linearity when 492



20

one subgenomic homeolog had multiple best-hit counterparts in two other subgenomes. 493

Specifically, we mapped the cDNA sequences of a wheat gene model with FKPM value 494

≥ 1 to the other two reference subgenomes using BLAT (Kent, 2002). As approximately 495

99% of annotated genes (exon + intron) have a size of less than 15,000 bps, we set the 496

maximum intron size to 15,000 bps. Hits with identity less than 70% or that did not 497

overlap with any annotated genes were filtered before identifying best-hit pairs. This 498

procedure was performed repeatedly for each subgenome against the other two 499

subgenomes. The HGs showing consistent one-to-one correspondence were retained 500

and finally classified into 7 categories; namely ABD, AB, AD, BD, A, B and D type. 501

502

Gene Function Annotation Refinement and Enrichment Analysis 503

To gain a comprehensive gene annotation, we aligned wheat protein sequences to 504

rice and Arabidopsis using BLASTP with an e-value cutoff of 1e-5. We further curated 505

a set of development regulators by referring the annotation of GO or MapMan. The 506

GO annotation was download from Ensembl Plant Biomart 507

(https://plants.ensembl.org/biomart/martview), and MapMan pathway mappings were 508

download form MapMan Store (http://mapman.gabipd.org/web/guest/mapmanstore) 509

(Usadel et al., 2009). To functionally categorize the genes positively or negatively 510

correlated with spike complexity, we performed GO enrichment analysis using BiNGO 511

version 3.0.3 (Maere et al., 2005) with hypergeometric test and considered terms with 512

an FDR below 0.05 as significant and visualized the results as a network by 513

EnrichmentMap version 2.2.1 (Merico et al., 2010). 514

515

Gene Coexpression Network Analysis 516

Scale-free coexpression network analysis was performed on log2 transformed RPKM 517

values of expressed genes using the WGCNA package (v 1.51) in R (Langfelder and 518

Horvath, 2008). An unsigned co-expression network was constructed for all pairwise 519

Spearman correlations of gene expression. To weight highly correlated genes, we set 520

the soft threshold power to 9, as determined by assessment of scale-free topology 521



21

(Supplemental Figure 7A). For network construction, we used a dynamic tree cutoff 522

0.20 to merge similar trees (Supplemental Figure 7B-C). To identify networks 523

associated with spike trait variables, we calculated the eigen-value of each module, 524

after which Spearman’s rank correlation was calculated between the eigen-value 525

(overall expression trend of the genes in each module) and trait quantity. 526

527

Construction of Overexpressing Wheat Lines 528

Winter wheat variety Kenong 199 (KN199) was used to amplify gene sequences and 529

generate transgenic wheat plants. To obtain transgenic wheat plants, the entire coding 530

regions of ten genes were inserted separately into pUbi-pAHC25, a modified vector 531

for wheat gene overexpression driven by the maize ubiquitin promoter (Wang et al., 532

2013). The resulting constructs were transformed into immature embryos of wheat 533

variety KN199 by particle bombardment (Becker et al., 1994). At least 45 independent 534

transformants were obtained and analyzed for transgene expression. 535

536

Spike Phenotype Analysis of Wheat Overexpression Lines 537

Seeds of transgenic lines (including lines transformed with an empty vector) were 538

surface-sterilized in 2% NaClO for 15 min and rinsed overnight with flowing water, 539

after which they were sown in soil and allowed to grow for 40 days in a 4C 540

environment. After 40 days, the seedlings were transferred to a greenhouse with long 541

day condition (16-h light/8-h dark photoperiod, light intensity of 350 μmol photons m- 2 542

s-1, ambient temperature of 22–25C, and relative humidity of 60–70%). Spike 543

phenotypes were recorded for 30–45 randomly selected transgenic plants 20 days 544

after flowering. 545

546

Quantitative Reverse Transcriptase PCR 547

Total RNA was extracted from young leaves of transgenic overexpressing plants using 548

the RNA Miniprep Kit (Axygen). First-strand cDNA was synthesized from 2 μg of 549

DNase I-treated total RNA using the TransScript First-Strand cDNA Synthesis 550



22

SuperMix Kit (TransGen) as recommended by the manufacturer and was stored at 551

-20°C. Quantitative reverse transcriptase PCR (RT-qPCR) analysis was performed 552

using the PrimeScript RT reagent Kit (TaKaRa) and a Bio-Rad CFX96 Real-time PCR 553

detection system. Relative gene expression levels were determined using the method 554

of Livak and Schmittgen (Livak and Schmittgen, 2001). As the nucleotide sequences 555

of the homologous genes of TaTFL1, TaPAP2, and TaVRS1 were highly similar, 556

gene-specific primers for TaTFL1-2D, TaPAP2-5A, and TaVRS1-2B were not 557

designed. In this study, the relative expression levels of TaTFL1, TaPAP2 and TaVRS1 558

reflected the transcript abundance of the homologous genes of the three genes. We 559

used β-TaTubulin mRNA as the internal control for RT-qPCR analysis. The primers 560

used for RT-qPCR are listed in Supplemental Table 15. 561

562

Scanning Electron Microscopy (SEM) 563

For SEM, young spikes from KN199 and T4 transgenic plants overexpressing 564

TaTFL1-2D, TaPAP2-5A and TaVrs1-2B at different stages were fixed overnight in 2.5% 565

glutaraldehyde at 4°C. After dehydration in a series of ethanol solutions and 566

substitution with 3-methylbutyl acetate, the samples were subjected to critical point 567

drying, coated with platinum, and observed using a Hitachi S-3000N variable pressure 568

scanning electron microscope. 569

570

Accession Numbers 571

The raw read data for this study have been submitted to the NCBI Sequence Read 572

Archive (SRA; http://www.ncbi.nlm.nih.gov/sra) under accession number SRP091625. 573

574

ACKNOWLEDGEMENTS 575

We thank Yanbao Tian (Institute of Genetics and Developmental Biology, Chinese 576

Academy of Sciences) for help with SEM. 577 https://plantphysiol.orgDownloaded on May 15, 2021. - Published by

Copyright (c) 2020 American Society of Plant Biologists. All rights reserved.


23

Supplemental Figure 1. Major early developmental stages of the wheat spike. 578

Supplemental Figure 2. Saturation analysis of transcriptomic depth. 579

Supplemental Figure 3. Transcriptome association analysis of spike complexity. 580

Supplemental Figure 4. Correlation of the expression levels of the three subgenomic 581

copies of TaTFL1, TaPAP2 and TaVRS1 with spikelet quantity. 582

Supplemental Figure 5. Analyses of spike complexity for wild-type KN199 and 583

transgenic wheat plants. 584

Supplemental Figure 6. Scatter plot of transcript abundance as determined by using 585

two genome assemblies and annotations. 586

Supplemental Figure 7. Cutoffs used for coexpression network construction. 587

Supplemental Figure 8. Analytical workflow for this study and detailed pipeline used 588

for variant calling. 589

Supplemental Table 1. Detailed information of 90 accessions used in the study. 590

Supplemental Table 2. The number of spikelets per main spike in 90 accessions. 591

Supplemental Table 3. The number of seeds per main spike in 90 accessions. 592

Supplemental Table 4. The number of florets per main spike in 90 accessions. 593

Supplemental Table 5. Summary of reads mapping. 594

Supplemental Table 6. Statistics of reads mapped to each subgenome. 595

Supplemental Table 7. Expression abundance of expressed genes in 90 accessions. 596



24

Supplemental Table 8. Statistics of the number of genes expressed in each variety. 597

Supplemental Table 9. Statistics of the number of expressed genes in each 598

chromosome. 599

Supplemental Table 10. Statistics of the number of each type of HGs. 600

Supplemental Table 11. List of genes significantly correlated witch spikelet number. 601

Supplemental Table 12. Wheat genes homologous to rice spike development-related 602

genes. 603

Supplemental Table 13. Transcription factor enrichment within each module 604

classified by WGCNA. 605

Supplemental Table 14. Lists of genes in Module 9 core subnetwork. 606

Supplemental Table 15. Primers used to generate transgenic wheat plants. 607

Supplemental Table 16. Statistics of reads mapped to different version of genome 608

sequences. 609

Supplemental Table 17. Statistics of the number of expressed genes assessed using 610

different version of genome sequences. 611

612

613

FIGURE LEGENDS 614

Figure 1. Spike complexity of 90 selected varieties from the Chinese mini core 615

collection. 616

(A) Schematic diagram of main spike development in wheat. See Supplemental 617



25

Figure 1 for scanning election micrographs. (B) Geographical distribution of 90 618

selected winter wheat varieties in China. (C) Scatter plot of spikelet number per spike 619

against floret number per spike. (D) Scatter plot of floret number per spike against 620

seed number per spike. (E-G) Scatter plot of average spikelet (B), floret (C) and seed 621

(D) number per main spike from 2014 against the corresponding values from 2015. 622

Each color denotes one variety, and the shape denotes the replicate. The gray line 623

represents the regression trend calculated by the general linear model of each trait. 624

625

Figure 2. Transcriptome analysis of 90 selected wheat varieties. 626

(A) Percentages of read pairs aligned to the reference genome sequence. (B) The 627

three subgenomes contribute roughly equal proportions of transcripts to the whole 628

transcriptome. (C) Proportion of genes classified to each type of homeologous group 629

(HG). HGs were categorized based on the number of homeologous copies from the 630

three subgenomes. (D) Boxplot of the expression ratios of different types of HGs. (E) 631

Phylogenetic tree of 90 selected wheat varieties. The branches labeled by orange 632

belongs to variety Set 1, which was grouped by expression pattern of GESes, and the 633

purple ones belongs to Set 2. See Figure 3C for details. 634

635

Figure 3. Transcriptome association analysis for spike complexity. 636

(A) Manhattan plot of the association significance between spike traits and gene 637

expression abundance along chromosome 3B. The gray line shows the genome-wide 638

significance level (FDR ≤ 0.05). (B) Function enrichment network for gene expression 639

signatures (GESes) significantly associated with spikelet quantity (FDR ≤ 0.05). 640

Enriched gene ontology (GO) terms for negatively correlated genes are represented 641

by nodes with green edges, and terms for positively correlated genes are represented 642

by nodes with blue edges. (C) 1,538 GESes can be grouped into three distinct sets 643

based on expression abundance. (D) Boxplot of the spikelet numbers of the three sets 644

of wheat varieties. 645

646



26

Figure 4. Coexpression network analysis of GESes associated with spikelet 647

number. 648

(A) Distribution of Spearman’s correlations for wheat genes homologous to rice genes 649

previously reported as related to grain traits. (B) Correlation heatmap between 650

subnetwork modules and spike traits. An eigen-value of each module was computed 651

by the WGCNA package to assess correlations between overall expression trends 652

and spike traits. The color bar represents the scale of the Spearman’s correlation. (C) 653

Network of the red module (Module 9) showing a significant negative correlation to all 654

assessed spike traits. 655

656

Figure 5. Functional validation of TaTFL1-2D in the transgenic KN199 wheat line. 657

(A) Comparison of the spike complexity of KN199 and T2 transgenic TaTFL1-2D 658

wheat plants. Scale bars, 1 cm. Positive correlation between spikelet number (B) and 659

floret number (C) per main spike and TaTFL1 expression levels in T2 transgenic 660

plants. (D) Comparison of developmental duration between KN199 and T4 transgenic 661

TaTFL1-2D lines (OE1 and OE2) at each stage. II, III, IV, and V indicate stage II, stage 662

III, stage IV, and stage V, respectively. Data are the mean ± s.d. of 30 plants for each 663

line. Student’s t-test, *P < 0.05. DAS, days after a single ridge appearance. (E)-(H), 664

Scanning electron micrographs of young spikes from KN199 plants at 3, 10, 15, and 665

21 days after a single ridge appearance. (I)-(L), Scanning electron micrographs of 666

young spikes from transgenic TaTFL1-2D plants at 3, 10, 15, and 21 days after a 667

single ridge appearance. Scale bars, 200 μm (E, I), 300 μm (F, J), 500 μm (K) and 1 668

mm (G, H, L). FM, floret meristem; GP, glume primordium; LP, lemma primordium. The 669

asterisks indicate spikelets. The number at the bottom represents the spikelet number 670

per spike. 671

672

Figure 6. Functional validation of TaPAP2-5A and TaVRS1-2B in the KN199 673

transgenic wheat line. 674

Comparison of the spike complexity of KN199 plants with that of T2 transgenic 675



27

TaPAP2-5A (A) and TaVRS1-2B (D) plants. Scale bars, 1 cm. Negative correlation 676

between spikelet number per main spike and TaPAP2 (B) and TaVRS1-2B (E) 677

expression levels in T2 transgenic plants. Comparison of developmental duration 678

between KN199 plants and T4 transgenic TaTFL1-2D (c) and TaVRS1-2B (F) lines 679

(OE1 and OE2) at each stage. I. II, III, IV, and V indicate stage II, stage III, stage IV, 680

and stage V, respectively. Data are the mean ± s.d. of 30 plants for each line. DAS, 681

days after a single ridge appearance. (G)-(J), Scanning electron micrographs of 682

young spikes from KN199 plants at 2, 9, 15, and 22 days after a single ridge 683

appearance. (K)-(N), Scanning electron micrographs of young spikes from transgenic 684

TaPAP2-5A plants at 2, 9, 15, and 22 days after a single ridge appearance. (O)-(R), 685

Scanning electron micrographs of young spikes from transgenic TaVRS1-2B plants at 686

2, 9, 15, and 22 days after a single ridge appearance. Scale bars, 200 μm (G, K, O), 687

300 μm (H, L, P), 500 μm (M, Q), 1 mm (I, J) and 3 mm (N, R). GP, glume primordium; 688

LP, lemma primordium; An, awn. Student’s t-test, *P < 0.05. The asterisks indicate 689

spikelets. The number at the bottom represents the spikelet number per spike. 690

691

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https://www.ncbi.nlm.nih.gov/pubmed/?term=Simons%20KJ%5BAuthor%5D&cauthor=true&cauthor_uid=16172507

https://www.ncbi.nlm.nih.gov/pubmed/?term=Fellers%20JP%5BAuthor%5D&cauthor=true&cauthor_uid=16172507

https://www.ncbi.nlm.nih.gov/pubmed/?term=Trick%20HN%5BAuthor%5D&cauthor=true&cauthor_uid=16172507

https://www.ncbi.nlm.nih.gov/pubmed/?term=Zhang%20Z%5BAuthor%5D&cauthor=true&cauthor_uid=16172507

https://www.ncbi.nlm.nih.gov/pubmed/?term=Tai%20YS%5BAuthor%5D&cauthor=true&cauthor_uid=16172507

https://www.ncbi.nlm.nih.gov/pubmed/?term=Gill%20BS%5BAuthor%5D&cauthor=true&cauthor_uid=16172507

https://www.ncbi.nlm.nih.gov/pubmed/?term=Faris%20JD%5BAuthor%5D&cauthor=true&cauthor_uid=16172507

https://www.ncbi.nlm.nih.gov/pubmed/16172507


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Wang, J., Sun, J., Miao, J., Guo, J., Shi, Z., He, M., Chen, Y., Zhao, X., Li, B., Han, F., et al. 801

(2013). A phosphate starvation response regulator Ta-PHR1 is involved in phosphate 802

signalling and increases grain yield in wheat. Ann Bot 111:1139-1153. 803

Wisecaver, J.H., Borowsky, A.T., Tzin, V., Jander, G., Kliebenstein, D.J., and Rokas, A. (2017). 804

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Yamamoto, E., Yonemaru, J., Yamamoto, T., and Yano, M. (2012). OGRO: The Overview of 807

functionally characterized Genes in Rice online database. Rice (N Y) 5:26. 808

809



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Figure 1. Spike complexity of 90 selected varieties from the Chinese mini core collection.(A) Schematic diagram of main spike development in wheat. See Supplemental Figure 1 for scan-ning election micrographs. (B) Geographical distribution of 90 selected winter wheat varieties in China. (C) Scatter plot of spikelet number per spike against floret number per spike. (D) Scatter plot of floret number per spike against seed number per spike. (E-G) Scatter plot of average spikelet (B), floret (C) and seed (D) number per main spike from 2014 against the corresponding values from 2015. Each color denotes one variety, and the shape denotes the replicate. The gray line represents the regression trend calculated by the general linear model of each trait.



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Figure 2. Transcriptome analysis of 90 selected wheat varieties. (A) Percentages of read pairs aligned to the reference genome sequence. (B) The three subgenomes contribute roughly equal proportions of transcripts to the whole transcriptome. (C) Proportion of genes classified to each type of homeologous group (HG). HGs were categorized based on the number of homeologous copies from the three subgenomes. (D) Boxplot of the expression ratios of different types of HGs. (E) Phylogenetic tree of 90 selected wheat varieties. The branches labeled by orange belongs to variety Set 1, which was grouped by expression pattern of GESes, and the purple ones belongs to Set 2. See Figure 3C for details.



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Figure 3. Transcriptome association analysis for spike complexity. (A) Manhattan plot of the association significance between spike traits and gene expression abundance along chromosome 3B. The gray line shows the genome-wide significance level (FDR ≤ 0.05). (B) Function enrichment network for gene expression signatures (GESes) significantly associated with spikelet quantity (FDR ≤ 0.05). Enriched gene ontology (GO) terms for nega-tively correlated genes are represented by nodes with green edges, and terms for positively correlated genes are represented by nodes with blue edges. (C) 1,538 GESes can be grouped into three distinct sets based on expression abundance. (D) Boxplot of the spikelet numbers of the three sets of wheat varieties. https://plantphysiol.orgDownloaded on May 15, 2021. - Published by



-|-|Traes_7DL_C3F07D103

-|-|Traes_7DL_3111E3A31

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Figure 4. Coexpression network analysis of GESes associated with spikelet number. (A) Distribution of Spearman’s correlations for wheat genes homologous to rice genes previous-ly reported as related to grain traits. (B) Correla-tion heatmap between subnetwork modules and spike traits. An eigen-value of each module was computed by the WGCNA package to assess correlations between overall expression trends and spike traits. The color bar represents the scale of the Spearman’s correlation. (C) Network of the red module (Module 9) showing a signifi-cant negative correlation to all assessed spike traits.



Figure 5. Functional validation of TaTFL1-2D in the transgenic KN199

wheat line. (A) Comparison of the spike complexity of KN199 and T2

transgenic TaTFL1-2D wheat plants. Scale bars, 1 cm. Positive correlation

between spikelet number (B) and floret number (C) per main spike and TaTFL1

expression levels in T2 transgenic plants. (D) Comparison of developmental

duration between KN199 and T4 transgenic TaTFL1-2D lines (OE1 and OE2) at

each stage. II, III, IV, and V indicate stage II, stage III, stage IV, and stage V,

respectively. Data are the mean ± s.d. of 30 plants for each line. Student’s t-

test, *P < 0.05. DAS, days after a single ridge appearance. (E)-(H), Scanning

electron micrographs of young spikes from KN199 plants at 3, 10, 15, and 21

days after a single ridge appearance. (I)-(L), Scanning electron micrographs of

young spikes from transgenic TaTFL1-2D plants at 3, 10, 15, and 21 days after

a single ridge appearance. Scale bars, 200 μm (E, I), 300 μm (F, J), 500 μm (K)

and 1 mm (G, H, L). FM, floret meristem; GP, glume primordium; LP, lemma

primordium. The asterisks indicate spikelets. The number at the bottom

represents the spikelet number per spike.



Figure 6. Functional validation of TaPAP2-5A and TaVRS1-2B in the KN199 transgenic wheat line. Comparison of the spike complexity of KN199 plants with that of T2 transgenic TaPAP2-5A (A) and TaVRS1-2B (D) plants. Scale bars, 1 cm. Negative correlation between spikelet number per main spike and TaPAP2 (B) and TaVRS1-2B (E) expression levels in T2 transgenic plants. Comparison of developmental duration between KN199 plants and T4 transgenic TaTFL1-2D (c) and TaVRS1-2B (F) lines (OE1 and OE2) at each stage. I. II, III, IV, and V indicate stage II, stage III, stage IV, and stage V, respectively. Data are the mean ± s.d. of 30 plants for each line. DAS, days after a single ridge appearance. (G)-(J), Scanning electron micrographs of young spikes from KN199 plants at 2, 9, 15, and 22 days after a single ridge appearance. (K)-(N), Scanning electron micrographs of young spikes from transgenic TaPAP2-5A plants at 2, 9, 15, and 22 days after a single ridge appearance. (O)-(R), Scanning electron micrographs of young spikes from transgenic TaVRS1-2B plants at 2, 9, 15, and 22 days after a single ridge appearance. Scale bars, 200 μm (G, K, O), 300 μm (H, L, P), 500 μm (M, Q), 1 mm (I, J) and 3 mm (N, R). GP, glume primordium; LP, lemma primordium; An, awn. Student’s t-test, *P < 0.05. The asterisks indicate spikelets. The number at the bottom represents the spikelet number per spike.



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http://scholar.google.com/scholar?as_q=Fertile transgenic wheat from microprojectile bombardment of scutellar tissue&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Becker,&as_ylo=1994&as_allsubj=all&hl=en&c2coff=1

http://www.ncbi.nlm.nih.gov/pubmed?term=Boden%2C S%2EA%2E%2C Cavanagh%2C C%2E%2C Cullis%2C B%2ER%2E%2C Ramm%2C K%2E%2C Greenwood%2C J%2E%2C Jean Finnegan%2C E%2E%2C Trevaskis%2C B%2E%2C and Swain%2C S%2EM%2E %282015%29%2E Ppd%2D1 is a key regulator of inflorescence architecture and paired spikelet development in wheat%2E Nat Plants 1%3A14016%2E&dopt=abstract

http://search.crossref.org/?page=1&rows=2&q=Boden, S.A., Cavanagh, C., Cullis, B.R., Ramm, K., Greenwood, J., Jean Finnegan, E., Trevaskis, B., and Swain, S.M. (2015). Ppd-1 is a key regulator of inflorescence architecture and paired spikelet development in wheat. Nat Plants 1:14016.

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http://scholar.google.com/scholar?as_q=Ppd-1 is a key regulator of inflorescence architecture and paired spikelet development in wheat&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=Ppd-1 is a key regulator of inflorescence architecture and paired spikelet development in wheat&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Boden,&as_ylo=2015&as_allsubj=all&hl=en&c2coff=1

http://www.ncbi.nlm.nih.gov/pubmed?term=Bonnett%2C O%2ET%2E %281936%29%2E The development of the wheat spike%2E J Agr Res 53%3A445%2D451%2E&dopt=abstract

http://search.crossref.org/?page=1&rows=2&q=Bonnett, O.T. (1936). The development of the wheat spike. J Agr Res 53:445-451.

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http://scholar.google.com/scholar?as_q=The development of the wheat spike&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=The development of the wheat spike&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Bonnett,&as_ylo=1936&as_allsubj=all&hl=en&c2coff=1

http://www.ncbi.nlm.nih.gov/pubmed?term=Bradley%2C D%2E%2C Ratcliffe%2C O%2E%2C Vincent%2C C%2E%2C Carpenter%2C R%2E%2C and Coen%2C E%2E %281997%29%2E Inflorescence commitment and architecture in Arabidopsis%2E Science 275%3A80%2D83%2E&dopt=abstract

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Transcriptome association identifies regulators of wheat …...2017/08/14 · seed s (Figure 1 A and Supplemental Figure 1) (Bonnett, 1936; Fisher, 67 1973). Like maize and rice,