evolution of the rna n6-methyladenosine methylome mediated ...€¦ · 8/13/2019 · 128 results...

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Short Title: Evolution of the RNA m6A modification 1

Corresponding author: [email protected]; [email protected] 2

Chuang Ma 3

State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of 4

Life Sciences, Northwest A&F University, Shaanxi, Yangling 712100, China 5

Tel: +86-29-87091109 6

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Evolution of the RNA N6-methyladenosine methylome mediated by 8

genomic duplication 9

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Author names and affiliations: 11

Zhenyan Miao1,2, Ting Zhang1, Yuhong Qi1, Jie Song1, Zhaoxue Han1,2, Chuang Ma1,2,* 12

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1State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of 14

Life Sciences, Northwest A&F University, Shaanxi, Yangling 712100, China 15

2Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest 16

Region, Ministry of Agriculture, Northwest A&F University, Shaanxi, Yangling 712100, China 17

*Corresponding author: [email protected]; [email protected] 18

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One-sentence summary 20

RNA N6-methyladenosine-modified genes exhibit biased subgenome fractionation, and their 21

co-evolutionary relationship with transposable elements is mediated by genomic duplication in 22

maize (Zea mays). 23

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Author contributions 25

C.M. and Z.M. conceived the project; Z.H. prepared plant materials; T.Z., Z.M., and J.S. 26

performed the bioinformatics analysis; Y.Q. performed the experimental validation; Z.M., T.Z., 27

Plant Physiology Preview. Published on August 13, 2019, as DOI:10.1104/pp.19.00323

Copyright 2019 by the American Society of Plant Biologists

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and C.M. wrote the article with help from all authors. 28

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Funding information 30

This work was supported by the National Natural Science Foundation of China (31570371), the 31

Natural Science Foundation Research Project of Shaanxi Province of China (2019JQ-096), the 32

Youth 1000-Talent Program of China, the Hundred Talents Program of Shaanxi Province of China, 33

and the Fund of Northwest A&F University (Z111021603 and Z111021403). 34

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Abstract 36

RNA N6-methyladenosine (m6A) modification is the most abundant form of RNA epigenetic 37

modification in eukaryotes. Given that m6A evolution is associated with the selective constraints 38

of nucleotide sequences in mammalian genomes, we hypothesize that m6A evolution can be linked, 39

at least in part, to genomic duplication events in complex polyploid plant genomes. To test this 40

hypothesis, we presented the maize (Zea mays) m6A modification landscape in a 41

transcriptome-wide manner, and identified 11,968 m6A peaks carried by 5,893 and 3,811 genes 42

from two subgenomes (maize1 and maize2, respectively). Each of these subgenomes covered over 43

2,200 duplicate genes. Within these duplicate genes, those carrying m6A peaks exhibited 44

significant differences in retention rate. This biased subgenome fractionation of m6A-methylated 45

genes is associated with multiple sequence features and is influenced by asymmetric evolutionary 46

rates. We also characterized the co-evolutionary patterns of m6A-methylated genes and 47

transposable elements, which can be mediated by whole genome duplication and tandem 48

duplication. We revealed the evolutionary conservation and divergence of duplicated m6A 49

functional factors, and the potential role of m6A modification in maize responses to drought stress. 50

This study highlights complex interplays between m6A modification and gene duplication, 51

providing a reference for understanding the mechanisms underlying m6A evolution mediated by 52

genome duplication events. 53

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Introduction 55

N6-methyladenosine (m6A) is an internal, prevalent RNA modification, and has been identified in 56



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the RNA of mammals (Adams and Cory, 1975), insects (Levis and Penman, 1978), yeast (Clancy 57

et al., 2002), and plants, such as Arabidopsis (Arabidopsis thaliana; Zhong et al., 2008) and maize 58

(Zea mays; Nichols, 1979). The m6A modification is formed by m6A methyltransferase ‘writer’ 59

proteins (e.g., methyltransferase-like 3 [METTL3], methyltransferase-like 14 [METTL14], and 60

Wilms tumor1-associating protein [WTAP] in mammalian cells) (Bokar et al., 1997; Liu et al., 61

2014; Ping et al., 2014), recognized by ‘reader’ proteins (e.g., YT512-B Homology [YTH] domain 62

proteins) (Xu et al., 2014; Xu et al., 2015) and removed by ‘eraser’ proteins (m6A demethylases; 63

e.g., fat mass and obesity-associated protein [FTO] and alkylated DNA repair protein AlkB 64

homolog 5 [ALKBH5]) (Jia et al., 2011; Zheng et al., 2013), thus forming an epitranscriptomic 65

system of RNA methylations directly analogous to the well-studied reversible DNA and histone 66

modifications (Wang and He, 2014). Loss of function of core components of m6A modification 67

system in mammals have demonstrated that m6A modification affects multiple aspects of RNA 68

metabolism, including stability (Wang et al., 2014b), translation efficiency (Shi et al., 2017), 69

nuclear export (Roundtree et al., 2017), and alternative splicing (Zhao et al., 2014). In Arabidopsis, 70

the disruption of N6-adenosine-methyltransferase MT-A70-like (MTA, METTL3 homolog), 71

methyltransferase MT-A70 family protein (MTB, METTL14 homolog), and FKBP12 interacting 72

protein 37 (FIP37, WTAP homolog) leads to early embryonic lethality (Vespa et al., 2004; Zhong 73

et al., 2008; Ruzicka et al., 2017), and the depletion of ALKBH10B (At4g02940, ALKBH5 74

homolog) effects Arabidopsis floral translation (Duan et al., 2017). Additionally, two YTH-domain 75

proteins (EVOLUTIONARILY CONSERVED C-TERMINAL REGION2 [ECT2] and ECT3) 76

function as m6A readers and control developmental timing and morphogenesis, and trichome 77

morphology (Arribas-Hernandez et al., 2018; Scutenaire et al., 2018; Wei et al., 2018). These 78

pioneering biochemical and genetic researches shed the light on the functional roles of RNA m6A 79

modification that constitutes an important regulatory mechanism in RNA biology (Roundtree et al., 80

2017; Yang et al., 2018). 81

Recently, with the development of m6A sequencing (m6A-seq) technologies, an increasing number 82

of m6A methylome comparison studies have begun to unravel the evolution of this important 83

post-transcriptional modification (Dominissini et al., 2012; Dominissini et al., 2013). The 84

evolutionary conservation of m6A modifications was detected within two yeast species 85

(Saccharomyces mikatae and S. cerevisiae) (Schwartz et al., 2013), across two accessions 86



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of Arabidopsis (Can-0 and Hen-16) (Luo et al., 2014), and between human, chimpanzee, and 87

rhesus (Ma et al., 2017). The transcriptome-wide comparison on m6A modifications from human, 88

chimpanzee, and rhesus revealed that m6A evolution is associated with the selective constraints of 89

DNA sequences, and occurs in parallel with expression evolution of m6A-methylated genes (Ma et 90

al., 2017). Yet virtually nothing is known about the evolution of m6A modification after genome 91

duplications in plant evolution. 92

In plants, genome duplications (GDs), including whole genome duplications (WGDs), segmental 93

duplications, tandem duplications (TDs), and translocated duplications, generate a source of 94

specific genomic context (i.e., duplicated regions) as a dominant force of plant genome evolution 95

(Freeling, 2009). Following GD events, duplicated genes were subjected to different levels of 96

purifying selection, a proportion of which were lost in a process known as fractionation. There are 97

also many duplicate genes retained in the genome as paralog pairs, in which the individual genes 98

may be subfunctionalized (partitioning and sharing the original gene function) and/or 99

neofunctionalized (gaining novel functions) (Panchy et al., 2016). Both the bias in fractionation 100

and the functional divergence of duplicated genes have been reported to be associated with 101

differences in DNA methylation, rates of movement of transposable elements (TEs), gene 102

expression, and post-transcriptional regulation (Wang et al., 2014a; Wang et al., 2015; Panchy et 103

al., 2016; Cheng et al., 2018). Therefore, GDs provide a source of specific genomic context that 104

may have profound influences on transcriptional regulation and post-transcriptional regulation. 105

This raises the question whether, and if so to what degree, the evolution of m6A modification is 106

mediated by GD events in complex polyploid plant genomes. 107

To investigate this question, we performed deep m6A-seq on the leaf tissue of maize and explored 108

the patterns of m6A evolution in maize. Maize underwent a recent WGD event, after its divergence 109

from the lineage that gave rise to sorghum (Sorghum bicolor) ~ 5–12 million years ago 110

(Swigonova et al., 2004). Since that time, the two subgenomes in maize experienced a variety of 111

changes (e.g., chromosomal rearrangements, and gene conversion) (Schnable et al., 2011), and 112

were combined into a diploid genome (Wei et al., 2007; Schnable et al., 2011). Because of this 113

unusual evolutionary history, together with the availability of high-quality of the maize B73 114

reference genome (Jiao et al., 2017) and the sorghum reference genome (Paterson et al., 2009; 115

McCormick et al., 2018), we selected maize as a model crop system to study the evolutionary 116



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implications of m6A modification in the context of GD events. Using the transcriptome-wide map 117

of m6A generated from our study, we made an effort to address some topics of conceptual 118

importance to understanding the evolutionary characteristics of m6A in maize. For example, do 119

GD events contribute to the evolutionary novelty of m6A modification? How is the evolution of 120

m6A modification associated with the expression divergence of duplicate genes? How, or whether, 121

m6A modification and TEs experience some co-evolutionary process following WGD in maize? 122

Our answers to these questions are provided by examining the coordination patterns of RNA m6A 123

modification with gene duplication, evolutionary divergence, gene expression and TE 124

accumulation. 125

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Results 128

Transcriptome-wide mapping of m6A in maize 129

To obtain the transcriptome-wide m6A map in maize, a series of m6A-immunoprecipitation (IP) 130

and the matched input (non-IP control) libraries was constructed and sequenced (Supplemental 131

Table S1). This series included leaf tissue of maize B73 and Han21 seedlings under both 132

well-watered (WW) and drought-stressed (DS) conditions, with three biological replicates each. 133

Raw sequencing reads were processed to discard adaptor sequences and low-quality bases using 134

the Trimmomatic v0.36 tool (Bolger et al., 2014). The resulting reads from maize B73 and Han21 135

samples were respectively aligned to the maize B73 reference genome (B73_RefGen_v4) and 136

Han21 pseudogenome using Tophat v2.1.1 (Kim et al., 2013). To build Han21 pseudogenome, 137

single nucleotide polymorphisms (SNPs) between B73 and Han21 were identified by aligning 138

Han21 RNA-seq data to maize B73 reference genome (B73_RefSeq_v4) using STAR (Dobin et al., 139

2013), following by SNP calling using GATK UnifiedGenotyper (McKenna et al., 2010). 94,761 140

SNPs within transcribed regions were used to replace the corresponding nucleotides in the maize 141

B73 reference genome to generate a pseudo-reference genome for Han21. Read distribution 142

analysis showed that the reads from m6A-IP samples are highly accumulated around the stop 143

codon and within 3′-untranslated regions (3′-UTRs) in all experimental conditions (Supplemental 144

Figure S1). We detected 8,224 to 11,134 m6A peaks in each individual biological replicate 145

(Supplemental Figure S2). For each experiment condition (one inbred line under one 146

environmental condition), highly confident peaks were identified referring the previous study 147

(Yoon et al., 2017). Briefly, by intersecting peak regions in a pairwise fashion among all three 148

replicates, regions that overlap in at least two of three replicates were designated as high 149

confidence m6A peak regions. Strong correlations were observed for the abundance of confident 150

peaks between biological replicates (Supplemental Figure S3). Confident m6A peaks from 151

different experimental conditions were further merged into a unique set of m6A peaks. As a result, 152

a total of 11,968 unique m6A peaks with high confidence were finally detected from 11,219 maize 153

genes (Supplemental Table S2), accounting for an average of ~1.07 m6A peaks within 154

transcription units from each gene. The m6A peaks in maize are abundant in 3′-UTRs (74.6% of 155

m6A peaks), near stop codons (20.8%), and coding sequences (3.2%) (Figure 1A). Enrichment 156



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analysis showed that the stop codon was the most enriched segment, representing ~ eight-fold 157

enrichment, followed by the 3′-UTR (~ four-fold enrichment) (Figure 1B). Similar distribution 158

patterns of m6A peaks were also observed in the separate analysis of m6A-seq data from B73 and 159

Han21 (Supplemental Figure S4). At the genome level, 11,219 m6A-methylated genes (i.e., genes 160

whose transcripts carrying m6A peaks; in abbreviation, as m6A genes) were unevenly distributed 161

across each chromosome (Figure 1D). 162

We observed that 90.6% of 11,968 m6A peaks contain the canonical motif RRACH (where R 163

represents A/G, A is m6A, and H represents A/C/U) in maize (Figure 1C; Supplemental Table S3). 164



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As shown in Supplemental Table S3, this proportion is comparable to those (80.6%, 92.3% and 165

81.2%) estimated using m6A peaks generated from three recent Arabidopsis m6A methylome 166

studies (Luo et al., 2014; Shen et al., 2016; Anderson et al., 2018). These 11,968 m6A peaks were 167

further scanned for enriched motifs using MEME suite (http://meme-suite.org/index.html; Bailey 168

et al., 2009). As expected, the RRACH motif is significantly enriched within maize m6A peaks 169

(Supplemental Figure S5). We also noted an interesting motif URUAY (where Y represents C/U) 170

(Figure 1C), which can also be detected from m6A peaks from each replicate sample 171

(Supplemental Figure S6). URUAY motif has recently been regarded as a plant-specific consensus 172

motif recognized by m6A reader protein ECT2 (Wei et al., 2018). Indeed, as shown in 173

Supplemental Figure S5, this URUAY motif is also enriched within m6A peaks generated from 174

three recent Arabidopsis m6A methylome studies (Luo et al., 2014; Shen et al., 2016; Anderson et 175

al., 2018). By using another commonly-used motif enrichment analysis software HOMER suite 176

(v4.10; http://homer.ucsd.edu/homer) (Heinz et al., 2010), enriched URUAY motif can also be 177

detected from maize and Arabidopsis m6A data used in our study (Supplemental Figure S5). Dot 178

blot assay was also performed to validate the specificity of m6A antibody for URUAY motif 179

(Supplemental Figure S7). 180

The transcriptome-wide m6A map in maize is provided for the benefit of the readers in the future 181

analysis. An overview of the transcriptome-wide m6A map supported by JBrowse (Buels et al., 182

2016) and downloadable Browser Extensible Data (BED) format files may be accessed in the 183

Maize Epigenetics Data Browser (MEDB), which is publicly available at 184

http://bioinfo.nwafu.edu.cn/MaizeBrowse/index.html. 185

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m6A genes exhibit distinct sequence features from non-m6A genes 187

To identify sequence features that may associate with m6A modification, we first tested the 188

Pearson correlation coefficient (PCC) between the frequency of m6A genes and different sequence 189

features (gene length, exon length, exon number, guanine-cytosine [GC] content, intron length and 190

gene distance) along the maize genome in a sliding window of 100 adjacent genes (Supplemental 191

Table S4). The statistical significance of PCC was assessed using 10,000 permutation tests, in each 192

of which 11,219 ‘m6A genes’ were randomly selected from the maize B73 genome annotation and 193



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a new PCC value was calculated for generating a background distribution. The corresponding 194

P-value was calculated as P=(1+N)/10,000; here N is the number of PCC in the background 195

distribution, which exceeds the PCC for the original data. P-value < 0.0001 denotes none of 196

10,000 permutation tests exceeding the PCC for the original data. We observed that the frequency 197

of m6A genes was slightly positively correlated with exon length (PCC = 0.06; P-value = 0.0955) 198

(Supplemental Figure S8), but significantly positively correlated with gene length (PCC = 0.33; 199

P-value < 0.0001) and exon number (PCC = 0.57; P-value < 0.0001) (Figure 2, A and B; 200

Supplemental Figure S8). In addition, the frequency of m6A genes was significantly negatively 201

correlated with GC content (PCC = -0.16; P-value = 0.0002) and gene distance (PCC = -0.36; 202

P-value < 0.0001) (Figure 2, C and D; Supplemental Figure S8). 203

Complementary to the correlation analysis using contiguous windows, we further performed 204

statistical analysis using sequence features from individual genes. Maize genes were split into 205



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three groups according to the corresponding number of m6A peaks from our study: low-m6A genes 206

(10,559 genes carrying only one m6A peak), high-m6A genes (660 genes carrying at least two m6A 207

peaks), and non-m6A genes (28,105 genes carrying no m6A peak) (Supplemental Table S2). 208

Compared with non-m6A genes, m6A genes (both low-m6A genes and high-m6A genes) had 209

significantly more exons (Figure 2E), lower GC content (Figure 2F) and longer introns (Figure 210

2G). The mean gene length is in the order of high-m6A genes (11,796 bp) > low-m6A genes (7,271 211

bp) > non-m6A genes (2,849 bp) (Figure 2H). These results may indicate that longer genes tend to 212

have a higher probability of containing the m6A peaks. 213

The above statistical analysis using contiguous windows and individual genes was also performed 214

on 10,604 and 10,085 m6A genes determined from m6A-seq datasets of B73 and Han21, separately. 215

We found that there is no substantial difference between the statistical results for the two inbred 216

lines (Supplemental Figure S9; Supplemental Figure S10). This result is as expected, as a 217

substantial number of m6A genes (9,470) were overlapped in both maize inbred lines 218

(Supplemental Figure S11A). We observed that significant differences exist between 1,134 219

B73-specific and 615 Han21-specific m6A genes in the distribution of GC content and average 220

intron length (Supplemental Figure S11B). Next, we examined whether there are significant 221

differences in nucleotide sequence variation between m6A genes from these two inbred lines 222

(Han21 and B73). Comparison analysis revealed that the SNP density in m6A genes is 223

significantly lower than that in non-m6A genes (Supplemental Figure S12). 224

Overall, these results above suggest that m6A modification in maize is correlated with multiple 225

sequence features, including gene length, exon number, intron length, GC content, and SNP 226

density. 227

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Biased fractionation of m6A genes exists between two subgenomes in 229

maize 230

The maize genome underwent its most recent WGD shortly after divergence from sorghum 231

(Schnable et al., 2011). After the WGD event, one copy of many duplicated gene pairs in maize 232

was lost (fractionated), leaving the other one as a singleton. Because of the biased gene 233

fractionation, the loss of duplicated genes is uneven between two maize subgenomes (maize1 and 234



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maize2), so that the maize1 subgenome retains more genes than maize2 subgenome (Brohammer 235

et al., 2018). In human and yeast, the evolutionary consequences of gene duplication are 236

associated with DNA methylation (Keller and Yi, 2014), gene expression (Gout et al., 2010), as 237

well as post-translational modification (Amoutzias et al., 2010). Considering that m6A 238

modification is correlated with multiple sequence features (Figure 2; Supplemental Figure S11) 239

and has a role in regulating gene expression (Yue et al., 2015; Roignant and Soller, 2017; 240

Roundtree et al., 2017), we raised the question of whether m6A modification might also be linked 241

to duplicated gene retention. 242

We first examined the duplication status of m6A genes retained after the most recent WGD event. 243

Genes in maize1 and maize2 were identified by performing syntenic analysis between the maize 244

B73 reference genome and the sorghum reference genome (Brohammer et al., 2018). Among 245

11,219 m6A genes, 5,893 and 3,811 were annotated as maize1 and maize2 genes, respectively 246

(Supplemental Table S2). The singleton-duplicate ratio of m6A genes in maize1 (1:0.66; 3,551 vs. 247

2,342) is significantly higher than that of m6A genes in maize2 (1:1.43; 1,566 vs. 2,245) (χ2 test; 248

P-value < 0.001), which is consistent with the trend in total subgenome genes (Figure 3A). The 249

significant difference in singleton-duplicate ratio between maize1 and maize2 was also apparent 250

when m6A genes in tandem duplication were not involved (Supplemental Figure S13). Notably, 251

the frequency of m6A genes in maize2 singletons is significantly higher than that in maize1 252

singletons (Figure 3B). These divergences of m6A genes between two subgenomes are most likely 253

the evolutionary consequence of biased subgenome fractionation. The biased fractionation of m6A 254

singletons between two subgenomes may associate with multiple sequence features. As shown in 255

Figure 3, C and D, singletons carrying m6A peaks (m6A singletons) in maize1 have significantly 256

more exons and longer nucleotides than those in maize2 (Student’s t-test; P-value < 0.05), but 257

these features were not significantly different between total maize1 singletons and maize2 258

singletons. These suggest that the biased fractionation of m6A genes may be relative to gene 259

length. 260

We then compared the evolutionary rate (ω) of m6A genes from maize1 and maize2 subgenomes. 261

The evolutionary rates, ratio of non-synonymous substitution (Ka)/synonymous sites (Ks), of 262

genes in maize were estimated using interspecific comparisons with putatively orthologous 263

sequences between maize and sorghum. The ω values of m6A genes were significantly higher than 264



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those of non-m6A genes; and Ks values of m6A genes were considerably lower than those of 265

non-m6A genes (Student’s t test; P-value < 0.001) (Supplemental Figure S14). This is indicative of 266

higher evolutionary rate and less evolutionary time of m6A genes. We also observed that the 267

evolutionary rate of m6A singletons in maize1 was significantly lower than that of m6A singletons 268

in maize2, but the evolutionary rate of non-m6A singletons was not significantly different between 269

maize1 and maize2 (Table 1). These indicated that m6A singletons in maize1 have experienced a 270

higher intensity of purifying selection than those in maize2. This asymmetric purifying selection 271

may have an influence on biased fractionation of m6A singletons. In contrast, m6A duplicates in 272

maize1 and maize2 evolved under similar levels of purifying selection, but the evolutionary rate of 273

non-m6A duplicates in maize1 was significantly lower than that of non-m6A duplicates in maize2 274

(Table 1). These indicated that m6A modification could associate with the divergence of 275



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evolutionary rate between duplicate genes in two subgenomes. 276

Further analysis of m6A duplicates showed that the evolutionary time (Ks) was significantly 277

different from duplicate gene pairs with different m6A patterns: non-m6A (NM) pattern (neither of 278

two partners carrying m6A peaks), diverged-m6A (DM) pattern (one partner had m6A peaks while 279

the other did not), and identical-m6A (IM) pattern (both of two partners carrying m6A peaks). 280

Duplicate genes with NM pattern had the significantly highest level of Ks values, followed by 281

duplicate genes with DM and IM patterns (Student’s t-test; P-value < 0.001) (Supplemental Figure 282

S15). Gene transposition could cause the separation of syntenic duplicates into two singletons. 283

Protein sequence comparison between the maize and sorghum genome identified 198 and 108 284

pairs of transposed singletons in maize1 and maize2, respectively (Supplemental Table S2). We 285

observed that transposed gene pairs had a significantly higher proportion of divergence of m6A 286

status than syntenic duplicate gene pairs without transposition (χ2 test; P-value < 0.001) 287

(Supplemental Figure S16). These observations indicate that m6A modification divergence in 288

young duplicate pairs was smaller than that in older duplicate pairs; and gene transposition could 289

enhance the extent of m6A divergence between duplicate pairs. 290

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Co-evolutionary consequences of m6A methylome and TEs influence 292

duplicates retention and expression divergence 293

We then explored whether there was an association between m6A modification and gene 294

expression in maize. Similar with those for m6A-seq, a series of RNA-seq libraries were 295

constructed using leaf tissue of maize B73 and Han21 seedlings under both WW and DS 296

conditions, with three biological replicates each (Supplemental Table S1). For each of these 297

RNA-seq libraries, the expression abundance of maize genes was estimated in terms of FPKM 298

(fragments per kilobase per million). We observed that the expression abundance of m6A genes 299

was significantly higher than that of non-m6A genes (Figure 4A); and the singleton-duplicate ratio 300

of m6A genes (1:0.71; 6,329 vs. 4,520) is significantly lower than that of non-m6A genes (1:0.34; 301

18,523 vs. 6,234) (χ2 test; P-value < 0.001), reflecting an overall higher retention rate for 302

duplicated genes methylated by m6A (Figure 4B). The association between m6A modification and 303

gene expression was also revealed by the differential expression abundance between m6A 304



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duplicates and singletons in maize (Supplemental Figure S17). 305

With the m6A-seq and RNA-seq data at hand, we were interested in whether m6A modification 306

divergence associated with expression divergence (ED) for duplicate genes. For a duplicate gene 307

pair (G1, G2), the ED was calculated using the formula: ED = (E1-E2)/(E1+E2), where E1 and E2 308

denote the mean expression level of G1 and G2, respectively. The ED of m6A genes with DM 309

pattern was significantly higher than that of genes with IM pattern (Figure 4C). For genes with 310

DM pattern, the methylated partners exhibited a higher level of gene expression than 311

non-methylated partners (Figure 4D). These findings suggested that m6A modification was more 312

likely to occur on actively transcribed genes and could be associated with the retention rate and 313

expression divergence of duplicate gene pairs. 314



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Previous studies have reported that the frequency of TEs, which is associated with local genomic 315

stability, also affects the expression of their neighboring genes (Sahebi et al., 2018). This 316

prompted us to investigate whether there is an evolutionary interplay between m6A and TEs. In 317

our data we observed that m6A genes are closer to TEs than non-m6A genes (Figure 5A); and the 318

frequency of TE-related genes (gene loci overlap with TE loci) carrying m6A peaks (61.1%; 319

6,850/11,219) was significantly higher than that of non-m6A genes (57.3%; 16,118/28,090) (χ2 test; 320

P-value < 0.01) (Figure 5B). Moreover, by comparing the evolutionary rates of m6A genes with 321

those of non-m6A genes, significantly higher ω values were observed from m6A genes (Figure 5C); 322

and ω values of TE-related m6A genes were significantly higher than those of non-TE-related m6A 323

genes (Figure 5D). These evidences suggest that, after WGD in maize, genes with m6A 324

modification had undergone relaxed selection, which is accompanied by the gathering of TEs 325

close to genes, such tendencies would be indicative of co-evolution between m6A genes and TEs. 326

The co-evolution between m6A genes and TEs was also exemplified by evolutionary analysis of 327

tandemly duplicated (TD) genes methylated by m6A. We totally obtained 4,448 TD genes from 328

1,758 TD clusters identified by Kono and colleagues (Kono et al., 2018). We observed that both 329

Ka values and Ks values of m6A and non-m6A TD genes are significantly higher than those of 330

non-TD genes. However, ω values for m6A TD genes are significantly lower than those of m6A 331

non-TD genes; and there is no significant difference between ω values for non-m6A TD genes and 332

non-m6A non-TD genes (Supplemental Table S5). These suggest that although both m6A and 333

non-m6A genes involved in TD events have had a higher substitution rate than those not involved, 334

m6A genes have been under stronger selective constraint during TD events than non-m6A genes. 335

After that, we found the frequency of m6A in TD genes is significantly lower than that in non-TD 336

genes (Figure 5E), and the ratio of DM pattern to IM pattern in TD clusters (2.04:1; 228 vs. 112) 337

was significantly higher than that observed in WGD duplicates (0.56:1; 990 vs. 1,765) (Figure 5F). 338

Remarkably, m6A TD genes were significantly less distant from TEs than non-TD genes (Figure 339

5G); and 65.9% (323/490) m6A TD genes were TE-related genes. This ratio was significantly 340

lower in non-TD genes (60.8%; 6,527/10,729) (Figure 5H). These results suggest that the 341

evolutionary scenario of m6A TD genes is accompanied by a preferential accumulation of TEs 342

during the process of TD events. 343

344



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Evolution of m6A functional factors and their influences on 345

hypomethylation of transcripts of drought-stress response genes 346

Phylogenetic analysis identified the functional counterparts of the m6A methyltransferases (MTA, 347

MTB, FIP37, putative ortholog of human KIAA1429 [VIRILIZER], and the E3 ubiquitin ligase 348

HAKAI), demethylases (ALKBH10B), and binding proteins (ECT2, ECT3 and ECT4) in maize 349



17 (Supplemental Figure S18). According to the phylogenetic tree and genomic coordinates, five 350



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homologous pairs formed by the WGD event were identified: ZmFIP37a/ZmFIP37b, 351

ZmHAKAIa/ZmHAKAIb, ZmECTa/ZmeECTb, ZmECTd/ZmECTe, and 352

ZmALKBH10a/ZmALKBH10b (Figure 6A). For ZmHAKAIa/ZmHAKAIb and ZmECTd/ZmECTe, 353

their corresponding partners showed very similar evolutionary rates, as reflected by Ka, Ks, and ω 354

(Figure 6B). In contrast, for the other three duplicate pairs, their corresponding partners exhibited 355

notably distinct evolutionary rates, which appeared to exhibit different strengths of purifying 356

selection. These results indicate that evolutionary conservation and divergence of m6A functional 357

factors may be medicated by WGD. 358

We performed RNA-seq analysis to quantify the expression abundance of m6A functional factors 359

in the leaf sample of two maize inbred lines responses to drought stress. In the drought-sensitive 360

inbred line B73, two ALKBH10 homologs and two ECT2/4 homolog were significantly 361

up-regulated by drought stress (Figure 6C). These expression patterns were also verified by qPCR 362

assay (Supplemental Figure S19). Meanwhile, the m6A abundance in drought-stressed samples 363

was significantly lower than that in well-watered samples, indicating global m6A hypomethylation 364

induced by drought stress (Supplemental Figure S20, A and B). For each maize line, differential 365

methylated peaks (DMPs) were identified by comparing the abundance of m6A peaks between DS 366

and WW conditions (Supplemental Table S6). The drought-tolerant Han21 line (2,998) has more 367

DMPs than the drought-sensitive B73 line (386). However, the proportion of hypomethylated 368

DMPs in Han21 (92.0%; 2,758/2,998) is comparable to that in B73 (87.8%; 339/386) 369

(Supplemental Figure S20C). These results may reflect the phenomenon that levels of m6A 370

modification are significantly decreased during drought stress in both inbred lines. Considering 371

the expression patterns of m6A methyltransferase and demethylase genes, the decrease in m6A 372

methylation during drought stress is most likely contributed by the induced expression of m6A 373

demethylase genes. 374

Most of DMPs were located in 3′-UTR and stop codon regions in both B73 and Han21 375

(Supplemental Figure S21A). A recent article reviewed that the genic locations of m6A peaks play 376

distinct roles in mediating the functional output of m6A modification (Shen et al., 2019). Therefore, 377

we performed Gene Ontology (GO) enrichment analysis of genes containing DMPs to explore 378

functional characteristics of DMPs in the context of genic location. In B73, the GO terms “protein 379

phosphorylation” and “regulation of apoptotic process” were specifically enriched in genes with 380



19

DMPs within 3′-UTR, while the GO terms “histone exchange” and “cytoplasmic translation” were 381

specifically enriched in genes within DMPs near stop codon (Supplemental Figure S21B). In 382

Han21, the GO terms “regulation of transcription” and “protein transport” were specifically 383

enriched in genes with DMPs within 3′-UTR, while the GO terms “photoreactive repair” and 384

“siderophore biosynthetic process” were specifically enriched in genes within DMPs near DMPs 385

(Supplemental Figure S21B). These results revealed that genes containing DMPs in specific genic 386

location play roles in distinct biological processes in B73 and Han21. 387

Han21 line was significantly more tolerant of drought stress than B73, which manifests as 388

dramatic phenotypic differences observed, including increased plant height, alterations in relative 389

water content and water loss rate, and robustness in root development (Supplemental Figure S22). 390

To understand the potential role of m6A hypomethylation in these phenotypic differences, we 391

performed GO analysis of genes containing hypomethylated m6A peaks in B73 and Han21, 392

respectively (Supplemental Figure S23). In B73, genes involved in cuticle development, negative 393

regulation of apoptotic signaling pathway, and response to abscisic acid are highly enriched. In 394

Han21, genes were significantly enriched in response to abiotic stress processes, such as cellular 395

response to oxidative stress, response to osmotic stress, acetyl-CoA metabolic process, and 396

ethylene mediated signaling pathway; as well as several developmental pathways, such as cell 397

morphogenesis and development, embryo development ending in seed dormancy, glucose and 398

starch metabolic process, and supramolecular fiber organization. These gene functions showed 399

clear correspondence with the observed phenotypic responses of these two maize inbred lines. 400

Moreover, the differences in drought tolerance between B73 and Han21 can also be explored at 401

the level of individual genes covering DMPs. VACUOLAR INVERTASE 2 (VI2) is a positive 402

regulator of root elongation in Arabidopsis (Sergeeva et al., 2006). The Han21 line showed more 403

than a two-fold reduction in methylation levels of m6A peaks in VI2 during drought stress 404

(Supplemental Figure S24). m6A peaks in Actin-7 (ACT7), a member of the actin gene family 405

involved in root growth, cell division, and root architecture in Arabidopsis (Kandasamy et al., 406

2009), is also less methylated in drought-stressed samples of maize B73 line (Supplemental Figure 407

S24). Furthermore, increased accumulation of epicuticular waxes is known to limit water loss and 408

increase water-deficit tolerance (Aharoni et al., 2004; Zhang et al., 2005; Kosma et al., 2009). The 409

striking disparity in relative water content and water loss rate is accompanied by marked 410



20

differences in the methylation levels of m6A peaks in genes associated with wax deposition 411

between B73 and Han21. For example, ECERIFERUM4 (CER4) and CER10, two genes encode 412

the alcohol-forming fatty acyl-coenzyme A reductase, are involved in cuticular wax biosynthesis 413

in Arabidopsis (Rowland et al., 2006). Methylation levels of m6A peaks in putative maize 414

orthologs of these two genes are significantly inhibited by drought stress in Han21 and B73, 415

respectively (Supplemental Figure S24). Furthermore, methylation level of m6A peaks in wax 416

ester synthase/acyl-coenzyme A: diacylglycerol acyltransferase (WSD1), a gene encodes 417

diacylglycerol acyltransferase that is required for stem wax ester biosynthesis in Arabidopsis (Li et 418

al., 2008), is also significantly repressed by drought stress in Han21 (Supplemental Figure S24). 419

Then, we performed m6A-IP-qPCR assay to further confirmed the m6A methylation levels of these 420

DMPs. As shown in Supplemental Figure S25, the results of m6A-IP-qPCR assay is consistent 421

with those of m6A-seq. Together, these observations demonstrate that the concerted 422

hypomethylation of candidate transcripts encoding proteins may associate with wax accumulation 423

during response to drought stress. 424

425

426



21

Discussion 427

Over the past several years, several studies have demonstrated the important roles of epigenetic 428

modifications in evolutionary history of eukaryotic genomes (Zemach et al., 2010; Chang and 429

Liao, 2012; Wang et al., 2014a; Patten, 2016). However, few transcriptome-wide studies have 430

attempted to investigate the evolutionary patterns of RNA m6A modification in the context of GD 431

events. In the present study, we used maize as a model system to study the evolution of m6A 432

methylome following GD events. Our analysis suggested that m6A modification alteration 433

following GD events appears to be a significant source of evolutionary novelty within plants. 434

435

The transcriptome-wide map of m6A in maize 436

Our study presents the transcriptome-wide map of RNA m6A modifications in the leaf tissue of 437

maize seedlings. This resource provides us an opportunity to globally characterize m6A in large, 438

complex plant genome. In our maize m6A-seq data, URUAY motif has a lower enrichment 439

E-value than the canonical RRACH motif. Given that the URUAY motif is identified as a 440

plant-specific consensus motif recognized by m6A reader protein ECT2 and the UGUA motif can 441

be methylated by endogenous Arabidopsis m6A writer proteins (Wei et al., 2018), these 442

phenomena indicated that those m6A writer proteins with methylation activity for the URUAY 443

motif may be conserved between Arabidopsis and maize. However, our motif enrichment analysis 444

using previously published Arabidopsis m6A-seq data showed that the enrichment E-values of 445

URUAY motif are generally higher than those of RRACH motif (Supplemental Figure S5). These 446

differences between maize and Arabidopsis likely represent the different m6A site biases and 447

unique biological meanings of m6A methylation between two species, or may result from the 448

distinct technical biases in m6A-seq library preparation among these studies. Further in-depth 449

structural and functional analysis of m6A writer and reader proteins may help to clarify these 450

biases. 451

Additionally, we found that the frequency of m6A genes is positively correlated with gene length, 452

exon number and intron length. We infer that the longer genes may have a higher probability of 453

containing m6A modification. Previous study has shown that gene length is increasing with 454

evolutionary time (Grishkevich and Yanai, 2014); and longer genes are more likely to be retained 455



22

as duplicates (McGrath et al., 2014; Guo, 2017). Further considering the higher retention rate of 456

m6A duplicates compared with non-m6A genes (Figure 4B), we suspect that gene length may be an 457

important genic property that linked the evolutionary relationship between m6A modification and 458

gene duplication in maize. 459

To benefit the readers in the future analysis, we developed a web browser named MEDB to host 460

the transcriptome-wide m6A map and to support navigation of the map and its interactive 461

visualization, integration, comparison and analysis. Taking advantages of the JBrowse system 462

(Buels et al., 2016), MEDB also allows users to transfer their private m6A methylome data to the 463

browser as custom tracks for easy cross-study comparison. For ensuring the data security, MEDB 464

does not require the users to upload their own files to the server. Instead, it can access local files or 465

a uniform resource locator (URL) specifying the location of a remote file. The cross-study 466

comparison can be performed through a degree of in-browser data analysis by combining data in 467

tracks using arithmetic and set operations, for example finding the union, intersection or exclusive 468

of two tracks. Notably, combination tracks can be further used as input to other combination tracks, 469

allowing users to build up arbitrarily complex analysis tracks. 470

471

Correlation between m6A methylation and biased subgenome 472

fractionation 473

In maize, a proportion of duplicated genes were lost because of different levels of purifying 474

selection on two subgenomes, a process known as biased fractionation. Genes in the 475

over-fractionated subgenome (maize1) are distinct with respect to overall fitness, in contrast to 476

genes in the under-fractionated subgenome (maize2) (Schnable et al., 2011). Our analysis revealed 477

that the singleton-duplicate ratio of m6A in maize1 was significantly higher than that in maize2 478

(Figure 3A), suggesting that biased subgenome fractionation also occurred in m6A genes. Further 479

investigation revealed that maize1 singletons exhibited significantly lower m6A frequency than 480

maize2 singletons (Figure 3B), and ω values were significantly lower in m6A singletons of maize1 481

than those of maize2 (Table 1), indicating significantly higher levels of purifying selection on the 482

fractionated m6A genes in maize1 than those in maize2. These results are complementary 483

evidences for the hypothesis that maize1 underwent stronger purifying selection that resulted in a 484



23

higher frequency of fractionation (Schnable et al., 2012; Pophaly and Tellier, 2015). 485

A bias of gene expression dominance toward the less fractionated subgenome has been previously 486

observed in maize (Schnable et al., 2011). Indeed, we showed that m6A genes had higher 487

expression levels than non-m6A genes; and m6A modification divergence was correlated with gene 488

expression divergence between duplicate partners (Figure 4; Supplemental Figure S17). 489

Considering that gene expression levels impose a strong constraint on gene duplications and 490

subgenome fractionation, these observations indicate that m6A modification divergence of 491

duplicate genes influences gene expression abundance and ultimately, the divergence of 492

subgenome dominance in maize. 493

494

Complex interplays among m6A modification, TE accumulation, and 495

tandem duplication 496

The disruptive effects of TEs have been extensively documented, as they can integrate into the 497

regulatory or coding region of host genes or induce ectopic/nonallelic recombination, which is 498

often associated with lower levels of gene stability (Jangam et al., 2017). In our study, m6A genes 499

were found to be less distant from TEs than non-m6A genes, and the frequency of TEs in m6A 500

genes was higher than that in non-m6A genes (Figure 5, A and B). m6A genes involved in TD 501

events also showed less distance from TEs than those not involved, and the frequency of 502

TE-related genes was much higher in TD genes than in non-TD genes (Figure 5, G and H). These 503

suggests that genes flanked by TEs were more likely to be methylated by m6A; and then this 504

coordination of m6A and TEs experienced a more relaxed selection (Figure 5D). Together with 505

former observations that m6A modification can enhance stability of target genes (Figure 4), we 506

propose preferential accumulation of TEs near m6A genes, as a co-evolutionary outcome of m6A 507

modification and TEs, involves a compromise between optimal levels of gene stability and 508

prevention of the damage done by active TEs. 509

In addition, we found that gene members in tandem duplicated arrays showed higher rates of 510

divergence in m6A modification than those in syntenic duplication pairs (Figure 5F), and m6A 511

genes involved in TD events evolved more quickly and underwent stronger purifying selection 512

than those not involved (Supplemental Table S5). This is hypothesized to be a consequence of the 513



24

effects of gene balance; the theory being that genes encoding proteins that interact with large 514

numbers of other proteins are more sensitive to changes in stoichiometry than those that do not 515

(Birchler and Veitia, 2010). The stoichiometry of members of multi-subunit complexes can affect 516

the amount of functional complete product, which in turn, affects patterns of gene expression and 517

ultimately, the phenotype and evolutionary fitness (Birchler and Veitia, 2012). The observation 518

that TD genes have higher loss rates of m6A modification relative to non-TD genes (Figure 5E) is 519

a complementary evidence for the hypothesis that these genes may be undergoing 520

subfunctionalization, representing an evolutionary outcome of m6A modification medicated by TD 521

events. 522

523

Potential roles of m6A modification in maize response to drought 524

stress 525

Differential gene expression has been proven to be responsible for drought responses in plants 526

(Zhu, 2016; Miao et al., 2017). Differential levels of m6A modification under drought stress has 527

also been observed in both B73 and Han21 (Supplemental Figure S20), suggesting that m6A 528

modification may be another important contributor for drought responses. Here, we discuss the 529

evolutionary consequences of five duplicated gene pairs encoding m6A functional factors that may 530

contribute to responses to drought stress (Figure 6, B and C). The two members of each of two 531

duplicated gene pairs (ZmHAKAIa/ZmHAKAIb, and ZmECTd/ZmECTe) exhibited similar 532

intensities of purifying selection, suggesting that they have been exposed to similar selective 533

constraints. This explained why these members all showed mild expression. In contrast, the 534

evolutionary rates of two members of ZmFIP37 pairs varied, but their expression values were 535

smooth. It is possible that these two members have differential effects on other phenotypes or 536

during other developmental stages, regardless of whether their specific roles in drought responses 537

have diverged. Notably, variations in the evolutionary rates and levels of expression between 538

members of the ZmALKBH10 pair were observed, in line with recent reports in Arabidopsis (Duan 539

et al., 2017), and which could be the outcomes of neofunctionalization or subfunctionalization as 540

the adaptive consequences of gene duplication. The up-regulated expression of genes encoding 541

demethylases (ZmALKBH10) appears to be the primary force driving m6A hypomethylation 542



25

under drought stress, which indicates that m6A hypomethylation may play a positive role in 543

drought response. 544

545

Future perspectives: Exploring the dynamics of RNA m6A 546

methylation in different plant species with spatial, temporal, and 547

environmental dimensions 548

In this study, we highlighted the importance of generating transcriptome-wide m6A map in plant 549

species, and uncovered the evolutionary patterns of m6A modifications associated with genomic 550

duplication using m6A-seq data from the leaf tissue of maize seedlings. As the most abundant 551

internal mRNA modification, m6A has gained increasing interests in the last few years to 552

understand its dynamic roles of post-transcriptional regulation mechanism underlying key plant 553

developmental processes, including embryo development (Ruzicka et al., 2017), shoot stem cell 554

fate (Shen et al., 2016), floral transition (Duan et al., 2017), and trichome morphogenesis (Wei et 555

al., 2018) in Arabidopsis m6A studies. We expect that, in the future, the m6A modification 556

dynamics will be explored using transcriptome-wide m6A maps profiled from different 557

developmental stages and tissues of maize (as well as other important plant species) under diverse 558

environmental conditions. Comparison of m6A modifications within and across species will be 559

performed to further elucidate the evolutionary mode of post-transcriptional regulation in plants. 560

561

Materials and Methods 562

Plant growth and sample preparation 563

Seeds from the B73 and Han21 inbred lines of maize (Zea mays) were germinated, and seedlings 564

were transferred to a growth chamber with controlled environmental conditions (28°C day/26°C 565

night, 16 hr light/8 hr dark). The relative water content of soil was maintained at 80% of the soil 566

moisture capacity for well-watered seedlings and at 40% of soil moisture capacity for 567

drought-stressed seedlings. When seedlings developed three fully expanded leaves, leaf samples 568

(three biological replicates for each experimental condition) were harvested, immediately frozen 569

in liquid nitrogen, and stored at -80°C for RNA isolation and sequencing. 570



26

571

RNA isolation and PolyA+ mRNA selection 572

For each leaf sample, total RNA was extracted using the RNAiso Plus (Code No. 9109, TaKaRa) 573

according to the manufacturer’s instructions. PolyA+ mRNA selection was performed using oligo 574

(dT)25 magnetic beads (Code No. S1419S, NEB) following the manufacturer’s protocol. 575

576

High-throughput m6A-seq and RNA-seq 577

mRNA was randomly fragmented into ~200-nucleotide-long fragments by RNA Fragmentation 578

Reagents (Ambion). Fragmented RNA was incubated for 2 hr at 4°C with 0.5 mg/mL anti-m6A 579

polyclonal antibody (Synaptic Systems Cat. No. 202003) in IP buffer (150 mM NaCl, 0.1% [v/v] 580

octylphenoxypolyethoxyethanol [Igepal CA-630], and 10 mM Tris-HCl [pH 7.4]) supplemented 581

with RNasin Plus RNase inhibitor (Promega). The mixture was then immunoprecipitated by 582

incubation with protein-A beads at 4°C for an additional 2 hr. After extensive washing, bound 583

RNA was eluted from the beads with 0.5 mg/mL N6-methyladenosine in IP buffer, and precipitated 584

by ethanol. TruSeq standard mRNA Sample Prep Kit (Illumina) was used to construct the libraries 585

from immunoprecipitated RNA and input RNA according to a published protocol (Dominissini et 586

al., 2013). Sequencing was performed on an Illumina HiSeq platform (Illumina Inc., San Diego, 587

CA, USA) and 50 base-pair (bp) single-end reads were generated. Library for RNA-seq was 588

generated using TruSeq Stranded mRNA Sample Prep Kit (Illumina). The resulting libraries were 589

sequenced on an Illumina HiSeq platform (Illumina Inc., San Diego, CA, USA) to produce 2× 590

122-bp paired-end reads. 591

592

Analysis of sequencing data 593

Raw reads from RNA-seq and m6A-seq were trimmed to remove adaptor sequences and 594

low-quality bases using the Trimmomatic v0.36 tool (Bolger et al., 2014). The quality of trimmed 595

RNA-seq and m6A-seq reads were examined using the FastQC program 596

(https://www.bioinformatics.babraham.ac.uk/projects/fastqc). 597

Single nucleotide polymorphisms (SNPs) between B73 and Han21 were identified following the 598

GATK Best Practices workflow (Van der Auwera et al., 2013). In brief, Han21 RNA-seq reads 599



27

were firstly mapped to B73 reference genome using STAR with parameters set as 600

“--outFilterMultimapNmax 1 --outSAMstrandField intronMotif --twopassMode Basic” (Dobin et 601

al., 2013). Then, AddOrReplaceReadGroups and MarkDuplicates functions in Picard suite 602

(v2.20.0; https://broadinstitute.github.io/picard) were used to add read groups and remove 603

duplicates, respectively. Subsequently, the SplitNCigarReads function in GATK suite (McKenna 604

et al., 2010) was used to split reads into exon segments and hard-clip sequences overhanging into 605

the intronic regions. SNPs were detected using HaplotypeCaller tool in GATK suite with 606

parameters set as “-allowPotentiallyMisencodedQuals -dontUseSoftClippedBases 607

-stand_call_conf 20 -ERC GVCF -nct 20”. The CombineGVCFs and GenotypeGVCFs functions 608

in GATK suite were used to merge gvcf files and to generate vcf files. The results were further 609

filtered using VariantFiltration tool in GATK suite with recommended parameters (-window 35 610

-cluster 3 -filterName FS -filter "FS > 60.0" -filterName QD -filter "QD < 2.0"). Finally, samtools 611

v1.9 (Li et al., 2009) was used to select highly confidence SNPs with the following requirements: 612

(1) non-reference alleles need to be consistent, (2) SNPs supported with ≥ 10 reads, and (3) SNPs 613

supported with ≥ 2 samples. 614

Han21 pseudogenome was built based on SNPs identified from our RNA-seq data. The trimmed 615

B73 and Han21 RNA-seq reads were respectively aligned to the maize B73 reference genome 616

(B73_RefGen_v4) and Han21 pseudogenome using Tophat v2.1.1 (Kim et al., 2013) with 617

maximum intron length set to 10kb, with default settings for other parameters, respectively. 618

Unique mapping reads were provided as input to Cufflinks v2.2.1 (Trapnell et al., 2013) for 619

normalization and estimation of gene expression level in terms of fragments per kilobase of 620

transcript per million mapped reads (FPKM = Counts of mapped fragments × 109/ [Length of 621

transcript × Total count of the mapped fragments]). Differential analysis was conducted using the 622

Cuffdiff program in Cufflinks. In this study, maize B73 reference genome sequences and 623

annotation were downloaded from Ensembl Plants (Release 41; https://plants.ensembl.org; Kersey 624

et al., 2018). 625

The trimmed B73 and Han21 m6A-seq reads were respectively aligned to the maize B73 reference 626

genome and Han21 pseudogenome using the STAR v2.5.3a (Dobin et al., 2013) with parameters 627

“--alignIntronMin 20 --alignIntronMax 10000 --outFilterMultimapNmax 1 628

--outFilterMismatchNmax 1”, respectively. Peak calling was performed using a "SlidingWindow" 629



28

method slightly modified from previous analysis (Luo et al., 2014), and implemented with the R 630

package PEA (Zhai et al., 2018). To call m6A peaks, the reference genome was scanned using a 631

25-bp sliding window. A fisher exact test was used to identify windows enriched for m6A, by 632

comparing normalized read counts of each window for IP and input samples. Benjamini-Hochberg 633

was implemented to adjust the P-value to false discovery rate (FDR) for multiple testing using the 634

R function “p.adjust”. Significant windows were identified if fold change of normalized read 635

count was more than two and FDR value was less than 0.05. Adjacent significant windows were 636

merged together to form peak regions. Peaks with length less than 100-nt in length were excluded 637

from the analysis. The called peaks within lowly expressed genes (FPKM < 1) were discarded. For 638

each experiment condition, peaks that overlap in at least two of three replicates were merged as 639

confidence m6A peaks using slice function (lower=2, rangesOnly=TRUE) in IRanges package 640

(Lawrence et al., 2013). Confident m6A peaks from four experimental conditions (B73_WW, 641

B73_DS, Han21_WW and Han21_DS) were further merged into a unique set of m6A peaks using 642

slice function (lower=1, rangesOnly=TRUE) in IRanges package. The m6A peaks with significantly 643

differential m6A modification levels between drought-stressed and well-watered conditions were 644

determined using the QNB software (Liu et al., 2017), with the criteria set as enrichment fold 645

change ≥ 2 or ≤ 0.5, and FDR ≤ 0.05. Gene Ontology enrichment analysis was performed using 646

topGO (Alexa et al., 2006). 647

648

Identification of enriched motifs within m6A peaks 649

Two well-known motif analysis suites, MEME (Bailey et al., 2009) and HOMER (Heinz et al., 650

2010), were used to perform the motif enrichment analysis. The DREME (Discriminative Regular 651

Expression Motif Elicitation) tool in the MEME suite (http://meme-suite.org/tools/dreme) was 652

used to discover relatively short (up to 8 bp), ungapped motifs that are enriched within a set of 653

target sequences (m6A peak sequences) relative to a set of control sequences (shuffled m6A peak 654

sequences). The set of target sequences (target set) is composed with m6A peak sequences 655

extracted from reference genome (maize: B73_RefGen_v4; Arabidopsis: TAIR10) using the 656

fastaFromBed function in bedtools v2.28 (Quinlan and Hall, 2010). The set of control sequences 657

(control set) is generated by randomly shuffling each of m6A peak sequences while preserving the 658



29

nucleotide frequencies. This shuffling process is performed by using the ‘fasta-shuffle-letters’ 659

utility (k=1) provided by the MEME suite. These two sets of sequences were input to DREME for 660

discovering motifs with the following parameters: minimum length of the motif: 5; maximum 661

length of the motif: 7; E-value threshold: 1E-5. 662

For a specified motif (e.g., RRACH or URUAY), AME tool in the MEME suite 663

(http://meme-suite.org/tools/ame) and findMotifs.pl script in the HOMER tool 664

(http://homer.ucsd.edu/homer) were respectively used to calculate the significance level of relative 665

enrichment of this motif within target sequences relative to control sequences. 666

667

Synonymous (Ks) and nonsynonymous (Ka) substitutions in 668

homologous maize genes 669

The maize subgenome data (genes in maize1 and maize2) in our study were obtained from 670

(Brohammer et al., 2018), which were identified via performing syntenic analysis between the 671

maize genome and the sorghum (Sorghum bicolor) genome. Briefly, the SynMap pipeline (Lyons 672

et al., 2008) was run for aligning maize B73 reference genome (B73_RefGen_v4) against the 673

sorghum reference genome (v3.1; http://phytozome.jgi.doe.gov) to identify maize genes in 674

syntenic blocks relative to the ancestral state. Then, the subgenome identity of each maize 675

chromosome was determined using a previously described method (Schnable et al., 2011). The 676

maize tandem duplicate genes in our study were obtained from (Kono et al., 2018). The coding 677

sequences of homologous gene pairs were aligned using the MAFFT v7.271 software (Katoh and 678

Standley, 2013). On the basis of sequence alignments, the synonymous (Ks) and nonsynonymous 679

(Ka) substitutions and the resulting Ka/Ks values were calculated using the Model Averaging (MA) 680

method in the KaKs_calculator v2.0 (Wang et al., 2010). 681

682

Identification of gene transposition 683

The protein sequences of singleton genes in maize containing syntenic orthologs in sorghum were 684

searched against protein sequences of the singletons without syntenic orthologs in sorghum, using 685

Protein BLAST (BLASTP). The BLAST hits with more than 80% similarity, which were over 80% 686

in length, were considered potential candidate genes for transposition. When two singletons from 687



30

maize were more similar to each other than they were to the sorghum syntenic orthologs, and one 688

of the two maize genes was syntenic to the sorghum, the non-syntenic copy of the gene was 689

defined as a potentially transposed duplicate gene. 690

691

Phylogenetic analysis and identification of genes encoding m6A 692

functional factors 693

The Arabidopsis writer, eraser, and reader of m6A modification were used, as previously described 694

(Duan et al., 2017; Ruzicka et al., 2017; Arribas-Hernandez et al., 2018; Scutenaire et al., 2018; 695

Wei et al., 2018). The annotated protein sequences of three species (Arabidopsis thaliana 696

Araport11, Oryza sativa v7, and Sorghum bicolor v3.1.1) were downloaded from Phytozome V12 697

(https://phytozome.jgi.doe.gov/pz/portal.html). The Arabidopsis sequences were used as query 698

sequences to obtain homologous proteins in three other species using local BLASTP with a cutoff 699

as E-value ≤10-5. Multiple sequence alignments of candidate full-length amino acid sequences 700

were performed using MUSCLE with default options in the MEGA 7 software (Kumar et al., 701

2016). Phylogenetic trees were generated using the neighbor-joining method with 1,000 702

bootstraps. 703

704

Dot blot assay 705

The dot blot assay was performed following a previously published protocol (Nagarajan et al., 706

2019). In brief, we randomly chose two m6A peaks from our m6A-seq data that contained the 707

UGUAU and UGUAC motifs, respectively. The two sequences were used as templates to 708

synthesize four RNA oligos that contained either m6A or A at a single internal position within 709

URUAY motif. Oligos were denatured at 72°C in a heat block for 3 min. Then the samples were 710

loaded to the Amersham Hybond-N+ membrane (RPN119B, GE Healthcare). Membrane was then 711

UV crosslinked in a HL-2000 HybriLinkerTM Hybridization Oven. After crosslinking, membrane 712

was washed in 10 ml of wash buffer for 5 min and then blocked in 10 ml of blocking buffer for 1 713

hr at room temperature with gentle shaking. Subsequently, membrane was incubated with 714

anti-m6A antibody (Synaptic Systems Cat. No. 202003, diluted 1:500) overnight at 4°C. 715

Membrane was then washed in 10 ml of the wash buffer for 5 min three times, followed by 716



31

incubation with HRP-conjugated mouse anti-rabbit IgG (Santa Cruz Biotechnology Cat. No. 717

sc-2357, diluted 1:10000) for 1 hr at room temperature. Membrane was again washed in 10 ml of 718

the wash buffer for 10 min four times. Then, membrane was incubated with 5 ml of ECL Western 719

Blotting Substrate for 5 min in darkness at room temperature and exposed with Hyperfilm ECL to 720

a proper exposure period. 721

722

Measurement of gene expression levels using real-time quantitative 723

PCR (qPCR) 724

Total RNA was extracted following above described methods, and the total RNA was treated with 725

DNaseI (Code No. 2270A, TaKaRa). First-strand cDNA was synthesized using PrimeScriptTM II 726

1st Strand cDNA Synthesis Kit (Code No. 6210A, TaKaRa) according to the manufacturer’s 727

instructions. qPCR was performed in three biological replicates × three technical replicates using 728

CFX96 Real-Time PCR Detection System (Bio-Rad) with TB GreenTM Premix Ex TaqTM II (Tli 729

RNaseH Plus) (Code No. RR820A, TaKaRa). The Cyclophilin (GenBank: M55021) was used as 730

an internal control (Lin et al., 2014). The 2-ΔΔCT method was used to calculate the gene expression 731

levels. The primers used for RT-qPCR are listed in Supplemental Table S7. 732

733

Global m6A quantification 734

Total RNA isolation and two rounds of PolyA+ mRNA selection was performed following above 735

described methods. The change of global m6A levels in mRNA was measured by EpiQuik m6A 736

RNA Methylation Quantification Kit (Colorimetric) (Epigentek Cat. No. P-9005) following the 737

manufacturer’s protocol. 738

739

Measurement of m6A methylation levels of specific m6A peaks using 740

m6A-IP-qPCR 741

For validation of m6A-seq results, m6A immunoprecipitation was performed using WW and DS 742

B73 and Han21 samples, respectively. RNA samples were fragmented into ~300-nucleotide-long 743

fragments. Fragmented RNA was incubated for 2 hr at 4°C with 0.5 mg/mL anti-m6A polyclonal 744



32

antibody (Synaptic Systems Cat. No. 202003). After ethanol precipitation, the input RNA and 745

immunoprecipitated RNA were subjected to reverse transcription and qPCR assays. The 746

Cyclophilin (GenBank: M55021) was used as an internal control, since (1) Cyclophilin mRNA did 747

not show any obvious m6A peak from m6A-seq data; (2) Cyclophilin showed relatively invariant 748

expression levels between WW and DS samples; and (3) Cyclophilin is considered to be a 749

housekeeping gene. Samples were performed in three biological replicates × three technical 750

replicates. The m6A level of specific mRNA fragments was calculated by the ratio of RNA 751

abundances, IP/input, as previously described (Shen et al., 2016). Briefly, relative enrichment of 752

each fragment was calculated first by normalizing the amount of a target cDNA fragment against 753

that of internal control, and then by normalizing the value for the immunoprecipitated sample 754

against that for the input. The primers used for m6A-IP-qPCR are listed in Supplemental Table S7. 755

756

Physiological phenotypes of maize B73 and Han21 757

Seeds of the maize inbred lines, Han21 and B73, were germinated, and seedlings were 758

transplanted into pots filled with sand and transferred to a growth chamber with controlled 759

environmental conditions (16 hr light/8 hr dark cycle, 28°C day/26°C night temperature). The 760

relative water content of soil was maintained at 80% of the soil moisture capacity for well-watered 761

seedlings and at 40% of soil moisture capacity for drought-stressed seedlings. 762

To measure the relative water content of leaves, fresh leaves were harvested and weighed to 763

determine the fresh weight (FW). Leaves were then saturated in water for 24 hr at 4°C and 764

weighed for the turgid weight (TW). Lastly, leaves were dried in an oven at 80°C for 24 hr, and 765

the dry weight (DW) was measured. The RWC (%) was calculated as (FW – DW) / (TW – DW) × 766

100. 767

To measure the rate of water loss, fresh leaves were harvested, weighed for the FW, and then 768

placed in a culture dish for 24 hr (22°C and 70% relative humidity) to measure the dehydrated 769

weight (W24). Then, samples were dried in an oven at 80°C for 24 hr, and the DW was measured. 770

The rate of water loss (%) was calculated as (FW – W24) / DW × 100. 771

772

Statistical analysis 773



33

The Student’s t-test was performed using t.test function in R package. The χ2 test was performed 774

using chisq.test function in R package. 775

776

Accession numbers 777

All sequencing data have been deposited into the National Center for Biotechnology Information’s 778

Sequence Read Archive database under the accession numbers SRP153627 and SRP125635. 779

780

Supplemental Data 781

Supplemental Figure S1. Distribution pattern of m6A-IP reads along transcripts. 782

Supplemental Figure S2. Intersection among m6A peaks identified in three biological replicates 783

of four experimental conditions. 784

Supplemental Figure S3. Correlation of m6A peak abundance among three biological replicates 785

in four experimental conditions. 786

Supplemental Figure S4. Distribution of m6A peaks in four experimental conditions. 787

Supplemental Figure S5. Both RRACH and URUAY motifs are enriched within m6A peaks in 788

maize and Arabidopsis. 789

Supplemental Figure S6. The enriched RRACH and URUAY motifs identified from m6A peaks 790

in each replicated sample. 791

Supplemental Figure S7. Dot blot analysis demonstrates m6A antibody specificity for URUAY 792

motif. 793

Supplemental Figure S8. Statistical significance of correlation coefficients between the 794

frequency of m6A genes with different gene features. 795

Supplemental Figure S9. Statistical significance of correlation coefficients between the 796

frequency of m6A genes with different gene features in B73 and Han21. 797

Supplemental Figure S10. Comparison of gene features (exon number, GC content, and intron 798

length) among different types of m6A genes in B73 and Han21, respectively. 799

Supplemental Figure S11. Comparison of m6A genes in B73 and Han21. 800

Supplemental Figure S12. Comparison of sequence variation patterns between m6A genes and 801

non-m6A genes. 802



34

Supplemental Figure S13. Comparison of singleton-duplication ratio of m6A genes and total 803

subgenome genes excluding tandem duplicates in maize1 and maize2. 804

Supplemental Figure S14. Comparison of Ka (nonsynonymous substitution), Ks (synonymous 805

substitution), and ω (evolutionary rate) values of m6A genes and non-m6A genes. 806

Supplemental Figure S15. Comparison of evolutionary time (Ks) of three categories of 807

duplicates (identical-m6A [IM] pattern, diverged-m6A [DM] pattern, and non-m6A [NM] pattern). 808

Supplemental Figure S16. Pairwise comparison of frequencies of m6A divergence among maize1 809

transposition, maize2 transposition, and duplicates without transposition. 810

Supplemental Figure S17. Comparison of expression abundance between duplicates and 811

singletons of genes with m6A modification. 812

Supplemental Figure S18. Phylogenetic relationship of m6A functional factors among maize, 813

sorghum, and rice. 814

Supplemental Figure S19. Relative mRNA levels of m6A functional factors in well-watered 815

(WW) and drought-stressed (DS) seedling samples of B73 and Han21. 816

Supplemental Figure S20. Hypomethylation of m6A induced by drought stress in B73 and 817

Han21. 818

Supplemental Figure S21. Functional characteristics of differentially methylated peaks (DMPs) 819

in the context of genic location in B73 and Han21. 820

Supplemental Figure S22. Phenotypic responses of B73 and Han21 under well-watered (WW) 821

and drought-stressed (DS) conditions. 822

Supplemental Figure S23. Genes containing drought-induced hypomethylated peaks are involved 823

in various biological processes of plant development and abiotic stress. 824

Supplemental Figure S24. Dynamic m6A peaks of five drought-responsive genes (ZmVI2, 825

ZmACT7, ZmCRE4, ZmCRE10, and ZmWSD1) in B73 and Han21. 826

Supplemental Figure S25. Validation of m6A peaks in five drought-responsive genes (ZmVI2, 827

ZmACT7, ZmCRE4, ZmCRE10, and ZmWSD1). 828

Supplemental Table S1. Sequenced and mapped reads in m6A-seq, input RNA-seq, and 829

mRNA-seq samples. 830

Supplemental Table S2. Characterization of maize genes regarding m6A modification and 831

duplicate status. 832



35

Supplemental Table S3. Frequency of m6A peaks containing RRACH motif and URUAY motif in 833

maize and Arabidopsis. 834

Supplemental Table S4. Correlation of m6A gene frequency with sequence features. 835

Supplemental Table S5. Comparison of evolutionary rates of m6A genes involved and not 836

involved in tandem duplication (TD). 837

Supplemental Table S6. Differentially methylated peaks in B73 and Han21 in response to 838

drought stress. 839

Supplemental Table S7. Primers used in this study. 840

841

Acknowledgements 842

We thank all our lab members at NWAFU for their discussion on the project. The authors declare 843

that they have no conflict of interest. 844

845

Tables 846

Table 1. Comparison of evolutionary rates (ω) of m6A singletons and m6A duplicates, and 847

non-m6A singletons and non-m6A duplicates in maize1 and maize2, respectively. 848

Comparison m6A singletons m6A duplicates P-value a

Maize1 0.2485 ± 0.1560 0.2256 ± 0.1617 < 0.0001

Maize2 0.2598 ± 0.1631 0.2283 ± 0.1568 < 0.0001

P-value a 0.0101 0.2861

Comparison non-m6A singletons non-m6A duplicates P-value a

Maize1 0.2372 ± 0.7533 0.2088 ± 0.1625 0.0108

Maize2 0.2330 ± 0.1734 0.2146 ± 0.1632 < 0.0001

P-value a 0.4050 0.0244

a Statistical analysis was conducted using the Student’s t test 849

850

Figure Legends 851

Figure 1. Overview of m6A methylome in maize. (A) Fractions and (B) relative enrichment of 852

m6A peaks in five non-overlapping transcript segments: 5′-untranslated regions (5′ UTRs), start 853



36

codons (200-nt window centered on the translational start sites), coding sequences (CDS), stop 854

codons (200-nt window centered on the translational stop codons), and 3′-untranslated regions (3′ 855

UTRs). (C) The motif on the top represents the canonical RRACH motif within 90.6% m6A peaks. 856

The motif on the bottom is the enriched URUAY motif. (D) The landscape of m6A genes and 857

distribution of genomic features across the maize genome. From outside to inside, each track 858

represents (I) frequency of m6A genes, (II) mean gene length, (III) mean guanine-cytosine (GC) 859

content, (IV) mean exon lengths, (V) mean intron lengths, (VI) mean exon number, and (VII) 860

mean distance to adjacent gene; larger distances are associated with centromeric sequences. 861

862

Figure 2. The correlation of m6A genes with multiple gene features. (A–D) Correlations 863

between the frequency of m6A genes and mean gene length, mean exon number, mean 864

guanine-cytosine (GC) content, and mean distance to adjacent gene. (E–G) Comparison of gene 865

features (exon number, GC content, and intron length) among different m6A gene types. Genes are 866

divided into three categories, according to the number of m6A sites per gene. Statistical analysis 867

was conducted using the Student’s t-test. **, P-value < 0.001. (H) Density plot of gene length for 868

three gene categories. 869

870

Figure 3. Evolutionary influences on RNA m6A methylome bias between two maize 871

subgenomes. (A) Comparison of singleton-duplication ratio of m6A genes and total subgenome 872

genes in maize1 and maize2. Statistical analysis was conducted using the χ2 test. **, P-value < 873

0.001. (B) Comparison of m6A genes frequency in maize1 singletons and maize2 singletons. 874

Statistical analysis was conducted using the χ2 test. **, P-value < 0.001. (C–D) Comparison of 875

exon number and gene length of m6A singletons in maize1 and maize2. Statistical analysis was 876

conducted using the Student’s t-test. 877

878

Figure 4. RNA m6A modification enhanced gene stability and contributed to duplicate 879

retention. (A) Comparison of expression abundance between m6A genes and non-m6A genes. (B) 880

Comparison of ratios of duplicates to singletons in m6A genes and non-m6A genes. (C) 881

Comparison of expression abundance divergency between identical-m6A (IM) pattern duplicates 882

and diverged-m6A (DM) pattern duplicates. (D) Comparison of expression abundance between 883



37

two diverged-m6A duplicate partners, methylated partner (MP) and non-methylated partner 884

(Non-MP). In A, C, and D, box-plots range from the first (Q1) to the third quartile (Q3) of the 885

distribution and represents the interquartile range (IQR). A line across the box indicates the 886

median. The whiskers are lines extending from Q1 and Q3 to end points that are defined as the 887

most extreme data points within Q1 − 1.5 × IQR and Q3 + 1.5 × IQR, respectively. Statistical 888

analysis was conducted using the Student’s t-test. **, P-value < 0.001. In B, statistical analysis 889

was conducted using the χ2 test. **, P-value < 0.001. 890

891

Figure 5. Evidences for co-evolution of m6A genes and transposon elements (TEs). (A) 892

Comparison of distance to the nearest TEs between m6A genes and non-m6A genes. (B) 893

Comparison of TE frequency between m6A genes and non-m6A genes. (C) Comparison of 894

evolutionary rates between m6A genes and non-m6A genes. (D) Comparison of evolutionary rates 895

between TE-related m6A genes and non-TE-related m6A genes. (E) Comparison of frequency of 896

m6A methylation between tandem duplicated (TD) genes and non-TD genes. (F) Comparison of 897

frequencies of diverged-m6A (DM) pattern and identical-m6A (IM) pattern between TD clusters 898

and whole genome duplication (WGD) pairs. (G) Comparison of distance to the nearest TEs 899

between m6A TD genes and m6A non-TD genes. (H) Comparison of frequency of TEs between 900

m6A TD genes and m6A non-TD genes. In A, C, D, G, box-plots range from the first (Q1) to the 901

third quartile (Q3) of the distribution and represents the interquartile range (IQR). A line across the 902

box indicates the median. The whiskers are lines extending from Q1 and Q3 to end points that are 903

defined as the most extreme data points within Q1 − 1.5 × IQR and Q3 + 1.5 × IQR, respectively. 904

Statistical analysis was conducted using the Student’s t-test. *, P-value < 0.05; **, P-value < 905

0.001. In B, E, F, H, statistical analysis was conducted using the χ2 test. *, P-value < 0.05; **, 906

P-value < 0.001. 907

908

Figure 6. Evolutionary dynamics of genes encoding m6A functional factors. (A) Orthologous 909

relationships of genes encoding m6A functional factors between maize and sorghum. Purple boxes 910

indicate maize genes, and yellow boxes indicate sorghum genes. Green boxes represent syntenic 911

orthologs of m6A functional factors. (B) Evolutionary rates of maize m6A functional factors 912

compared with their syntenic homologs in sorghum. (C) RNA-seq expression profiles of genes 913



38

encoding m6A functional factors. DS, drought-stressed; WW, well-watered. Data are means ± SD 914

(n=3, three biological replicates). a, the significance test of differential expression was conducted 915

using cuffdiff software, false discovery rate (FDR)-adjusted P-value ≤ 0.05. 916

917

918



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https://www.ncbi.nlm.nih.gov/pubmed?term=Roignant JY%2C Soller M %282017%29 m%286%29A in mRNA%3A An ancient mechanism for fine%2Dtuning gene expression%2E Trends Genetics&dopt=abstract

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https://www.ncbi.nlm.nih.gov/pubmed?term=Roundtree IA%2C Luo GZ%2C Zhang Z%2C Wang X%2C Zhou T%2C Cui Y%2C Sha J%2C Huang X%2C Guerrero L%2C Xie P%2C He E%2C Shen B%2C He C %282017%29 YTHDC1 mediates nuclear export of N%286%29%2Dmethyladenosine methylated mRNAs%2E Elife 6%3A e&dopt=abstract

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https://scholar.google.com/scholar?as_q=YTHDC1 mediates nuclear export of N(6)-methyladenosine methylated mRNAs.&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Roundtree&as_ylo=2017&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Rowland O%2C Zheng H%2C Hepworth SR%2C Lam P%2C Jetter R%2C Kunst L %282006%29 CER4 encodes an alcohol%2Dforming fatty acyl%2Dcoenzyme A reductase involved in cuticular wax production in Arabidopsis%2E Plant Physiology&dopt=abstract

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https://www.ncbi.nlm.nih.gov/pubmed?term=Schnable JC%2C Springer NM%2C Freeling M %282011%29 Differentiation of the maize subgenomes by genome dominance and both ancient and ongoing gene loss%2E Proceedings of the National Academy of Sciences of the United States of America&dopt=abstract

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https://www.ncbi.nlm.nih.gov/pubmed?term=Schwartz S%2C Agarwala SD%2C Mumbach MR%2C Jovanovic M%2C Mertins P%2C Shishkin A%2C Tabach Y%2C Mikkelsen TS%2C Satija R%2C Ruvkun G%2C Carr SA%2C Lander ES%2C Fink GR%2C Regev A %282013%29 High%2Dresolution mapping reveals a conserved%2C widespread%2C dynamic mRNA methylation program in yeast meiosis%2E Cell&dopt=abstract

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https://scholar.google.com/scholar?as_q=High-resolution mapping reveals a conserved, widespread, dynamic mRNA methylation program in yeast meiosis&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Schwartz&as_ylo=2013&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Scutenaire J%2C Deragon JM%2C Jean V%2C Benhamed M%2C Raynaud C%2C Favory JJ%2C Merret R%2C Bousquet%2DAntonelli C %282018%29 The YTH domain protein ECT2 ss an m%286%29A reader required for normal trichome branching in Arabidopsis%2E Plant Cell&dopt=abstract

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https://scholar.google.com/scholar?as_q=The YTH domain protein ECT2 ss an m(6)A reader required for normal trichome branching in Arabidopsis&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Scutenaire&as_ylo=2018&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Sergeeva LI%2C Keurentjes JJ%2C Bentsink L%2C Vonk J%2C van der Plas LH%2C Koornneef M%2C Vreugdenhil D %282006%29 Vacuolar invertase regulates elongation of Arabidopsis thaliana roots as revealed by QTL and mutant analysis%2E Proceedings of the National Academy of Sciences of the United States of America&dopt=abstract

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https://scholar.google.com/scholar?as_q=Vacuolar invertase regulates elongation of Arabidopsis thaliana roots as revealed by QTL and mutant analysis&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

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https://www.ncbi.nlm.nih.gov/pubmed?term=Zhai J%2C Song J%2C Cheng Q%2C Tang Y%2C Ma C %282018%29 PEA%3A an integrated R toolkit for plant epitranscriptome analysis%2E Bioinformatics&dopt=abstract

https://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Zhai&hl=en&c2coff=1

https://scholar.google.com/scholar?as_q=PEA: an integrated R toolkit for plant epitranscriptome analysis&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

https://scholar.google.com/scholar?as_q=PEA: an integrated R toolkit for plant epitranscriptome analysis&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Zhai&as_ylo=2018&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Zhang JY%2C Broeckling CD%2C Blancaflor EB%2C Sledge MK%2C Sumner LW%2C Wang ZY %282005%29 Overexpression of WXP1%2C a putative Medicago truncatula AP2 domain%2Dcontaining transcription factor gene%2C increases cuticular wax accumulation and enhances drought tolerance in transgenic alfalfa %28Medicago sativa%29%2E Plant Journal&dopt=abstract

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https://scholar.google.com/scholar?as_q=&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Zhang&as_ylo=2005&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Zhao X%2C Yang Y%2C Sun BF%2C Shi Y%2C Yang X%2C Xiao W%2C Hao YJ%2C Ping XL%2C Chen YS%2C Wang WJ%2C Jin KX%2C Wang X%2C Huang CM%2C Fu Y%2C Ge XM%2C Song SH%2C Jeong HS%2C Yanagisawa H%2C Niu Y%2C Jia GF%2C Wu W%2C Tong WM%2C Okamoto A%2C He C%2C Rendtlew Danielsen JM%2C Wang XJ%2C Yang YG %282014%29 FTO%2Ddependent demethylation of N6%2Dmethyladenosine regulates mRNA splicing and is required for adipogenesis%2E Cell Research&dopt=abstract

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https://scholar.google.com/scholar?as_q=FTO-dependent demethylation of N6-methyladenosine regulates mRNA splicing and is required for adipogenesis&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

https://scholar.google.com/scholar?as_q=FTO-dependent demethylation of N6-methyladenosine regulates mRNA splicing and is required for adipogenesis&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Zhao&as_ylo=2014&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Zheng G%2C Dahl JA%2C Niu Y%2C Fedorcsak P%2C Huang CM%2C Li CJ%2C Vagbo CB%2C Shi Y%2C Wang WL%2C Song SH%2C Lu Z%2C Bosmans RP%2C Dai Q%2C Hao YJ%2C Yang X%2C Zhao WM%2C Tong WM%2C Wang XJ%2C Bogdan F%2C Furu K%2C Fu Y%2C Jia G%2C Zhao X%2C Liu J%2C Krokan HE%2C Klungland A%2C Yang YG%2C He C %282013%29 ALKBH5 is a mammalian RNA demethylase that impacts RNA metabolism and mouse fertility%2E Molecular Cell&dopt=abstract

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https://scholar.google.com/scholar?as_q=ALKBH5 is a mammalian RNA demethylase that impacts RNA metabolism and mouse fertility&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

https://scholar.google.com/scholar?as_q=ALKBH5 is a mammalian RNA demethylase that impacts RNA metabolism and mouse fertility&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Zheng&as_ylo=2013&as_allsubj=all&hl=en&c2coff=1

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https://scholar.google.com/scholar?as_q=MTA is an Arabidopsis messenger RNA adenosine methylase and interacts with a homolog of a sex-specific splicing factor&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

https://scholar.google.com/scholar?as_q=MTA is an Arabidopsis messenger RNA adenosine methylase and interacts with a homolog of a sex-specific splicing factor&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Zhong&as_ylo=2008&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Zhu JK %282016%29 Abiotic stress signaling and responses in plants%2E Cell&dopt=abstract

https://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Zhu&hl=en&c2coff=1

https://scholar.google.com/scholar?as_q=Abiotic stress signaling and responses in plants&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

https://scholar.google.com/scholar?as_q=Abiotic stress signaling and responses in plants&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Zhu&as_ylo=2016&as_allsubj=all&hl=en&c2coff=1


evolution of the rna n6-methyladenosine methylome mediated ...€¦ · 8/13/2019 · 128 results...

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