Open Access

Identification of miRNAs and their target genes in developing maize ears by combined small RNA and degradome sequencing

  • Hongjun Liu1,
  • Cheng Qin1, 2,
  • Zhe Chen1,
  • Tao Zuo3,
  • Xuerong Yang4,
  • Huangkai Zhou5,
  • Meng Xu5,
  • Shiliang Cao6,
  • Yaou Shen1,
  • Haijian Lin1,
  • Xiujing He1,
  • Yinchao Zhang1,
  • Lujiang Li1,
  • Haiping Ding7,
  • Thomas Lübberstedt8,
  • Zhiming Zhang1Email author and
  • Guangtang Pan1Email author
Contributed equally
BMC Genomics201415:25

DOI: 10.1186/1471-2164-15-25

Received: 23 August 2013

Accepted: 26 December 2013

Published: 14 January 2014

Abstract

Background

In plants, microRNAs (miRNAs) are endogenous ~22 nt RNAs that play important regulatory roles in many aspects of plant biology, including metabolism, hormone response, epigenetic control of transposable elements, and stress response. Extensive studies of miRNAs have been performed in model plants such as rice and Arabidopsis thaliana. In maize, most miRNAs and their target genes were analyzed and identified by clearly different treatments, such as response to low nitrate, salt and drought stress. However, little is known about miRNAs involved in maize ear development. The objective of this study is to identify conserved and novel miRNAs and their target genes by combined small RNA and degradome sequencing at four inflorescence developmental stages.

Results

We used deep-sequencing, miRNA microarray assays and computational methods to identify, profile, and describe conserved and non-conserved miRNAs at four ear developmental stages, which resulted in identification of 22 conserved and 21-maize-specific miRNA families together with their corresponding miRNA*. Comparison of miRNA expression in these developmental stages revealed 18 differentially expressed miRNA families. Finally, a total of 141 genes (251 transcripts) targeted by 102 small RNAs including 98 miRNAs and 4 ta-siRNAs were identified by genomic-scale high-throughput sequencing of miRNA cleaved mRNAs. Moreover, the differentially expressed miRNAs-mediated pathways that regulate the development of ears were discussed.

Conclusions

This study confirmed 22 conserved miRNA families and discovered 26 novel miRNAs in maize. Moreover, we identified 141 target genes of known and new miRNAs and ta-siRNAs. Of these, 72 genes (117 transcripts) targeted by 62 differentially expressed miRNAs may attribute to the development of maize ears. Identification and characterization of these important classes of regulatory genes in maize may improve our understanding of molecular mechanisms controlling ear development.

Background

Maize is one of the most productive crops worldwide, and is widely used as a model plant in genetics research [1]. Maize produces two distinct inflorescences, commonly referred to as the tassel and the ear. In this respect, it differs from other grasses such as rice and wheat. The tassel arises from the apex of the mature plant, while ears originate from axillary bud apices [2]. One obvious difference in morphology between the two inflorescences is the presence (tassel) or absence (ear) of a variable number of long branches originating at the base. In previous studies, the wide range of natural variation among different inbred lines was used to identify quantitative trait loci (QTL) underlying a variety of phenotypes by association mapping [3, 4]. Several genes associated with maize ear development have been identified in genetic and molecular studies [2]. However, knowledge about maize ear development is still limited, and most of the genes involved in this process are still unknown.

In plants, small RNA-guided post-transcriptional regulatory mechanisms play important roles in many aspects of plant biology, including metabolism [5], hormone responses [6], epigenetic control of transposable elements [7], and responses to biotic stress [8] and abiotic stress [9]. The two main types of small RNAs are microRNAs (miRNAs) and small interfering RNAs (siRNAs) [10]. Over recent decades, many miRNA families have been discovered in plants, and have been shown to regulate more aspects of plant biology than siRNAs [5, 11, 12]. Published reports as well as publicly accessible miRNA datasets (miRBase, version 20, http://www.mirbase.org/) [13], mainly based on model plants, suggest that miRNAs in plants are complex and abundant [1417]. Therefore, identification of miRNAs and their targets in diverse species has been a major focus in recent years.

So far, conserved miRNAs in maize have been identified by sequence homology analyses [1820], and new miRNA sequences have been identified by traditional [2123] or high-throughput [17, 2430] sequencing methods. These miRNA sequences can be found in miRBase databases [13]. Functional analysis has been carried out for only a few maize miRNAs, mainly in their role in flower development [21, 3136].

There are three main objectives of this study. The first objective is to identify conserved and novel miRNAs in maize ears at four different developmental stages. The second objective is to combine publically available Arabidopsis thaliana, Oryza sativa, Sorghum bicolor, and Zea mays miRNAs data with the new Zea mays miRNAs data to generate a miRNA microarray platform to analyze the dynamics of miRNA expression. Finally, to discover the targets of conserved and non-conserved miRNAs, we aimed to identify the remnants of small RNA-directed target cleavage by sequencing the 5′ ends of uncapped RNAs using a degradome sequencing approach.

Results

Overview over small RNA library sequencing

To study the involvement of regulatory miRNAs in the complex process of ear development, we profiled miRNA accumulation during ear development in the maize inbred line B73. We constructed a maize small RNA library using mixed RNAs obtained from ears at four different developmental stages. Sequencing was conducted on the Illumina platform. We obtained more than 10.67 million raw clean reads, ranging from 18 nt to 30 nt in length. After trimming adaptor sequences and removing contaminated reads, clean reads were aligned against the Maize B73 RefGen v2 working gene set using SOAP2 software [37]. We found that 7,981,459 (3,436,342 distinct) reads matched perfectly to the maize genome, representing 74.85% of total reads (66.64% of distinct reads). Of the distinct reads, 5.22% matched with non-coding RNAs in Rfam and NCBI Genbank databases; these non-coding RNAs included snoRNAs (0.01%), snRNAs (0.03%), tRNAs (0.15%), rRNAs (1.69%), and siRNAs (3.34%) (Additional file 1). The remaining reads were then used to identify conserved and new miRNAs. The length of these small RNAs ranged from 20 nt to 24 nt. Of these, the 24 nt category was the most abundant small RNA (48.55%) (Figure 1a), followed by 22 nt (14.18%) and 21 nt (8.78%). These were consistent with the typical lengths of plant mature small RNAs reported in other studies [14, 27, 38, 39].
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-15-25/MediaObjects/12864_2013_Article_5669_Fig1_HTML.jpg
Figure 1

Size distribution of small RNAs by deep sequencing and characterizations of maize miRNA. (a) Size distribution of sequenced sRNAs in total RNA; (b) The composition of maize miRNAs of various lengths; (c) The relative nucleotide bias at each position of the miRNAs in total RNA.

Computational identification of genuine miRNAs during maize ear development

To date, research on identifying conserved and novel miRNAs has used several standard methods and databases, including Rfam, GenBank, and miRBase. Because of their low expression levels and sequence depths, it is always difficult to predict miRNAs. Hence, we used a strict strategy with eight steps to predict and identify known and novel miRNAs based on the characteristic features of miRNAs specifically processed by Dicer-like proteins from canonical stem-loop regions of longer RNA precursors [40, 41]. We used an integrated strategy combining high-throughput sequencing with bioinformatics analyses to identify miRNAs meeting all reported previously criteria (see “Methods”) [42].

As shown in the schematic diagram of the strategy (Additional file 2), our computational analysis generated 508 loci folded within typical stem-loop structures (Additional file 3). After excluding 38 loci that overlapped with protein-coding gene exons, 76 loci overlapping transposable elements and other repetitive elements, and 9 loci with free energy lower than −20 kcal/mol (Additional file 4), the remaining 385 loci were considered to be candidate miRNA genes. We used miRAlign to identify paralogs or orthologs of these 385 candidate miRNA genes by comparing their sequences with those of known miRNAs, as described previously [43]. From this analysis, we detected 99 known miRNA genes encoding 96 mature miRNAs and three miRNA star (miRNA*) (Additional file 4). We also detected 64 novel miRNA* sequences (Additional file 5).

In plants, it is difficult to identify new miRNAs, even when they have the characteristic hairpin feature, because of abundant inverted repeats that can also fold into dysfunctional hairpins [44, 45]. Thus, we used additional strategies that were not based on phylogenetic conservation to identify non-conserved pre-miRNAs. We used MiPred (http://www.bioinf.seu.edu.cn/miRNA/) to distinguish pre-miRNAs from other similar segments in the maize genome. Among the remaining 286 candidate pre-miRNA-like hairpins, 52 were classified as pseudo-pre-miRNAs and 198 were not pre-miRNA-like hairpins. The other 36 loci, which encoded 26 non-redundant mature miRNAs (Table 1), were identified as maize-specific miRNA genes. Of these 26 miRNAs, 25 belonged to new families that have not been reported in plants (Additional file 4, Additional file 5). Here, we have designated them in the form of their zma-miR-specific-number, e.g., zma-miRs2. When several maize-specific miRNAs belonged to the same family, we named them in a similar manner to that used to name known mature miRNAs [15] (e.g. zma-miRs6a and zma-miRs6b). All of the new miRNA precursors had regular stem-loop structures. We also detected four miRNA* (Additional file 3, Additional file 4, Additional file 5, Additional file 6), providing further evidence for the existence of this class of miRNAs in maize.
Table 1

Newly identified miRNAs

miRNA

Sequence (5'-3′)

Ori

Size (nt)

Loci

miRNA abundance

Sequence of cloned putative miRNA* (5′-3′)

miRNA* abundance

Precursor location

zma-miRs1a

GGUGAACCACCGGACAUCGCAC

5′

22

1

13

NO

0

Chr10:170455..170536:-

zma-miRs1b

GGUGAACCACCGGACAUCGCAC

3′

22

1

21

NO

0

Chr1:261835708..261835791:-

zma-miRs2

UUUGACCAAGUUUGUAGAAAA

5′

21

1

10

NO

0

Chr10:7112184..7112314:-

zma-miRs3

UAUACACUGUGGUUGUGGAUG

5′

21

1

64

NO

0

Chr10:140401501..140401631:-

zma-miRs4

UAGCCAAGCAUGAUUUGCCCGU

5′

22

1

7

NO

0

Chr1:297173995..297174083:+

zma-miRs5

UGAUGCCAUUCAUUAAUCUC

5′

20

3

16

NO

0

Chr1:16908651..16908718:-

zma-miRs6a

UGGGUCAAGAAAGUAGAUGAAG

5′

22

6

4439

UCAUGUAUUUCUUCAUCCAGG

6

Chr1:145766064..145766151:-

zma-miRs6b

UGGGUCAAGAAAGUAGAUGAAG

5′

22

2

4314

UCAUGUAUUUCUUCAUCCAGG

6

Chr8:65126597..65126681:+

zma-miRs7

UUGGAGGGGAUUGAGGGGGCUA

5′

22

1

13

NO

0

Chr2:160587100..160587178:-

zma-miRs8

UUGGAUUGGUUUAGAGUGGUU

5′

21

3

9

NO

0

Chr2:193765038..193765121:-

zma-miRs9

UUAGGCUCGGGGACUAUGGUG

5′

21

1

56

CCGUGGCUCCUGCUCCUGAUG

1

Chr3:37277918..37278069:+

zma-miRs10

AUCGGCUGAUCGUUUGGCCUG

5′

21

1

12

NO

0

Chr3:95960134..95960203:+

zma-miRs11

CGUGGAACUUCUUCGGCGUAG

5′

21

1

59

GUGCCGAAGAAGAACUUCCUGCA

2

Chr4:6987996..6988075:-

zma-miRs12

UGGCUGUGAUGACAAAAAGGU

5′

21

1

14

NO

0

Chr5:13726506..13726621:+

zma-miRs13

UGAGUUUAGGGACUGGGAUGG

3′

21

1

5

NO

0

Chr5:33732172..33732274:+

zma-miRs14

UGAAACUGUCACAGCAUGAUC

5′

21

1

6

NO

0

Chr5:7648838..7648949:-

zma-miRs15

UGUUCGGUUGCUCAGGAACGGU

5′

22

1

9

NO

0

Chr6:102874227..102874366:+

zma-miRs16

UUGCCAGGAGGAGGAUGGAGC

3′

21

1

65

NO

0

Chr9:7189032..7189100:+

zma-miRs17

UGAAAAGCUAGAACGAUUUAC

3′

21

1

9

NO

0

Chr9:33858898..33859002:-

zma-miRs18

GUACUACGGGUACUGCGAGC

3′

20

1

5

NO

0

Chr1:140132808..140132921:+

zma-miRs19a

UGGUUGACAUAUGGACCCCAC

5′

21

1

5

NO

0

Chr2:196666638..196666791:+

zma-miRs19b

UGGUUGACAUAUGGACCCCAC

5′

21

1

5

NO

0

Chr2:196666638..196666790:-

zma-miRs19c

UGGUUGACAUAUGGACCCCAC

5′

21

1

5

NO

0

Chr5:182646213..182646347:+

zma-miRs19d

UGGUUGACAUAUGGACCCCAC

5′

21

1

5

NO

0

Chr9:114976246..114976391:-

zma-miRs20

AGGGCUUGUUCGUUUUGGAGU

5′

21

1

35

NO

0

Chr4:41972777..41972854:-

zma-miRs21

GGUGUUGGUGCCUGUAGCGG

5′

20

1

7

NO

0

Chr7:6041852..6041939:+

*indicated miRNA star (miRNA*).

Characterization of newly identified miRNAs in maize

As expected, approximately all 22 the conserved miRNA families in the small RNA library were identified in this study. However, we detected miRNA* sequences of zma-miR171h/k and zma-miR408b instead of their corresponding mature miRNA sequences (Additional file 5). We also identified five mature miRNAs (zma-miR160e, zma-miR166e/f, zma-miR169g, and zma-miR172e) previously predicted by similarity searches [18, 20] and unexpectedly found their corresponding miRNA* sequences (zma-miR160e*, zma-miR166e*/f*, and zma-miR169g*), which were not available in miRBase. Besides the known miRNAs, we also identified 26 new miRNA candidates (Table 1, Additional file 6), and nine (zma-miRs1a/b, zma-miRs2, zma-miRs4, zma-miRs7, zma-miRs9, zma-miRs12, zma-miRs14, and zma-miRs17) were previously reported [27] (Additional file 5). The sequence of miRs4 was similar to that of members of the miR169 family (Additional file 7), indicating that miRs4 may be a member of that family. Most of the new miRNAs could only be produced from one locus. However, zma-miRs6b and four other new miRNA genes (zma-miRs5, zma-miRs6a, zma-miRs8, and zma-miRs23) could be produced from two or more loci (Additional file 4). Among the newly identified miRNAs, 21-nt miRNAs were the most abundant category (52.6%) (Figure 1b). Analysis of the nucleotide sequences of these miRNAs revealed that uridine (U) was the most common nucleotide at the 5′ end (>80%) (Figure 1c), and the 10th and 11th nucleotides, which match to the cleavage site of targets, were usually adenine (A). Also, U was the most common nucleotide at positions 21 and 22 in these miRNAs.

Next, we conducted microarray assays to analyze expressions of the known and newly identified miRNAs during maize ear development. We detected transcripts of all of the conserved miRNAs and 20 out of 26 (76%) maize-specific miRNAs in the microarray experiment. Those that were undetected either had a low affinity to the chip probes or very low transcript levels (sequencing frequency <50) (Additional file 8). These results suggest that Solexa sequencing is a more specific and efficient tool than the miRNA microarray assay for identifying mature miRNAs. In our study, we detected six miRNA families (miR408/482/827/397/398/2118) in the microarray assay that were not present in the Solexa sequencing data (Additional file 8, Additional file 9). These miRNAs need to be further validated.

Although we identified 122 miRNAs (96 conserved + 26 non-conserved) and 64 miRNA*s, they showed a diverse range of abundance, and only a few miRNA families dominated in the miRNA library and microarray assay data. The six most abundantly expressed miRNA families were miR166, miR168, miR167, miR156, miR159, and miRs6. There were extremely low frequencies of miR395, miR399, miR2275, miRs12, and miRs19, possibly because these families are expressed in a tissue-specific manner. Most of the miRNA*s showed very low transcript levels (sequencing frequency <30), much lower than those of their homoplastic miRNAs, consistent with previous findings [12]. The transcript level of zma-miR408b was lower than that of zma-miR408b*, and the mature product from the 3′ arm of the hairpin suggested that the 3′ arm may be functional.

Expression profiles of known and newly identified miRNAs

To analyze miRNA expression during maize ear development, we analyzed the miRNA expression profiles of ear samples collected at four different developmental stages using microarray assays. Conserved mature miRNAs are generally conserved among plant species and are stably expressed in diverse tissues. However, when microarray technology is used to analyze expression, members of the same miRNA family with 1–3 nt sequence differences need to be normalized for further analyses because hybridization can occur between members of the same miRNA family across different species [46]. Hence, a total of 53 miRNAs, about 8.4% (53/632) of the probes on the microarray, were identified as putative differentially expressed miRNAs (P = 0.01) (Table 2). Of these, 45 miRNAs aligned with 59 members of 21 maize miRNA families, while the others corresponded to members of miRNA families from three other plant species, including rice (osa-miR156/162/164/168/396/529) Arabidopsis (ath-miR156/164/167) and sorghum (sbi-miR396). The results shown in Additional file 10: Figure S3 indicated that the differentially expressed miRNAs may be specially regulated in diverse pathways during ear development. A sample of 12 expressed miRNAs was randomly selected for validation by stem-loop qRT-PCR. The trends in the expression of these miRNAs detected by microarray experiments were consistent (7 miRNAs) or partially (5 miRNAs) consistent with those determined in stem-loop qRT-PCR analyses (Figure 2).
Table 2

The differentially expressed miRNA families and members

miR family

Members of identified miRNAs

miR family members

signal (> 500)

zma-miR156

zma-miR156a/j/k

zma-miR156a/b/c/d/e/f/g/h/i/l

no

 

osa-miR156l

zma-miR156j

no

 

ath-miR156g

zma-miR156k

no

zma-miR160

zma-miR160a

zma-miR160a/b/c/d/g/h

no

zma-miR162

zma-miR162

zma-miR162

no

 

osa-miR162a/b

  

zma-miR164

zma-miR164a/e/f/h

zma-miR164a/b/c/d/g

yes

 

osa-miR164c/e

zma-miR164e

yes

 

ath-miR164c

zma-miR164f

yes

  

zma-miR164h

no

zma-miR167

zma-miR167a/e

zma-miR167a/b/c/d

yes

 

ath-miR167c/d

zma-miR167e/f/g/h/i/j

yes

zma-miR168

zma-miR168a

zma-miR168a/b

yes

 

osa-miR168a/b

  

zma-miR171

zma-miR171b/c/d/g

zma-miR171b/d/e/i/j

no

  

zma-miR171c

no

  

zma-miR171g

no

zma-miR172

zma-miR172e

zma-miR172e

no

zma-miR319

zma-miR319a

zma-miR319a/b/c/d

yes

zma-miR390

zma-miR390a

zma-miR390a/b

no

zma-miR396

zma-miR396c

zma-miR396c/d

no

 

osa-miR396f/g

  
 

sbi-miR396d

  

zma-miR408

zma-miR408b*

zma-miR408b*

no

zma-miR528

zma-miR528a

zma-miR528a/b

yes

zma-miR529

zma-miR529

zma-miR529

no

 

osa-miR529b

  

zma-miR827

zma-miR827

zma-miR827

no

zma-miRs7

zma-miRs7

zma-miRs7

yes

zma-miRs9

zma-miRs9

zma-miRs9

no

zma-miRs16

zma-miRs16

zma-miRs16

no

*indicated miRNA star (miRNA*).

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-15-25/MediaObjects/12864_2013_Article_5669_Fig2_HTML.jpg
Figure 2

Expression of 12 miRNAs in developmental ears in maize. The expression pattern of 12 miRNAs detected by microarray experiment (a) and stem-loop quantitative Real-Time PCR (qRT-PCR) (b). Stages 1, 2, 3, and 4 represent stage I, stage II, stage III, and stage IV, respectively. *indicated the significant difference in adjacent two developmental stages (one-way ANOVA and Fisher′s LSD, p < 0.01, n = 4). The blue, pink, green, and purple bars in graph b depicted the stem-loop qRT-PCR relative expression level ± standard error of three replicates for each miRNA in the four developmental stages of maize ears, respectively.

Target prediction of conserved and non-conserved miRNAs by degradome sequencing

To identify small RNA targets at a global level in maize, we used the recently developed degradome library sequencing technology [47, 48]. We generated four libraries from maize ears at different developmental stages (I-IV) as described above. High-throughput sequencing yielded 13,638,690 (1,706,149 distinct), 18,257,616 (2,887,536 distinct), 9,477,595 (682,164 distinct), and 8,393,209 (2,490,241 distinct) 20-nt sequences representing the 5′ ends of uncapped, poly-adenylated RNAs for stages I to IV, respectively. The total number of signatures matching to the genome was 10,596,420 for stage I, 14,571,419 for stage II, 7,415,394 for stage III, and 6,524,350 for stage IV. The number of distinct sequences in the four libraries matching to the genome was 1,123,608 (66%) for stage I, 1,995,882 (69%) for stage II, 423,065 (62%) for stage III, and 1,746,858 (70%) for stage IV. The number of signatures that matched to only one location in the genome was relatively high: 825,904 (74%) for stage I, 1,521,543 (76%) for stage II, 317,671 (75%) for stage III, and 1,318,724 (70%) for stage IV, suggesting that 20-nt signatures are sufficient to identify their origin in the maize genome. Of these, 973,186 (87%), 1,816,631 (91%), 382,792 (90%) and 1,580,297 (90%) distinct signatures for stage I, II, III, and IV, respectively, could be mapped to annotated maize gene models (B73 RefGen_v2). A small proportion of the distinct signatures (0.8%, 0.7%, 0.9%, and 0.7% for stage I, II, III, and IV, respectively) could also be mapped to the maize chloroplast or mitochondrial genomes. The number of distinct signatures matching to rRNAs, tRNAs, small nucleolar RNAs (snRNAs) or small nuclear RNAs was 10,101 (0.9%) for stage I, 9,596 (0.5%) for stage II, 4,521 (1.1%) for stage III, and 11,572 (0.7%) for stage IV. These were removed before subsequent analyses (Additional file 10). Similarly, we removed the sequences matching to repeats/transposons that were revealed by searches against the repeat database (http://www.girinst.org/server/RepBase/). Interestingly, a significant proportion of distinct signatures from the four libraries matched to introns and intergenic regions, similar to the findings of previous transcript profiling analyses [47, 48].

Based on previous studies, a characteristic scenario of miRNA-guided slicing is that the cleavage takes place precisely between the 10th and 11th nt from the 5′ end of miRNA in the complementary region of the target transcript [4751]. We used CleaveLand pipeline [52] to identify sliced miRNA targets in the maize transcriptome. Various sequenced tags were plotted on each of the target transcripts (Additional file 11). The cleaved target transcripts were categorized into five classes (categories 0, 1, 2, 3, and 4) as reported previously for Arabidopsis[47], grapevine [49], rice [51], and soybean [50].

For conserved miRNAs and ta-siRNAs, 120 target genes (234 transcripts) were identified in ears at the four stages of development (Table 3, Figure 3 Additional file 12). Reads associated with most of these miRNA targets were over-represented (Additional file 11). However, only 15% of the miRNA targets (19 out of 127) were identified in all four stages. The targets were classified into categories 0–4 based on the abundance of degradome tags indicative of miRNA-mediated cleavage. In stage I, II, III, and IV, there were 5, 19, 7, and 20 targets classified as category 0 (where miRNA-guided cleavage remnants were the most abundantly recovered species) [53]. There were 5, 2, 20, and 3 targets in stage I, II, III, and IV, respectively, classified as category 1 (where the abundance of cleavage products was equal to the maximum, and there was more than one maximum position on the transcript). In stage I, II, III, and IV, there were 22, 28, 27, and 20 targets classified as category 2 (where the abundance of cleavage sequences was less than the maximum but greater than the median for the transcript). In stage I, II, III, and IV there were 10, 7, 13 and 5 target transcripts classified as category 3 (where the total abundance of degradome sequences at the cleavage site was equal to or less than the median for the transcript). All other transcripts were classified as category 4 (where only one raw read matched the 5′ end of a slicing remnant). Only 4, 8, 0, and 9 targets in stage I, II, III, and IV, respectively, were in category 4. Among the identified targets, category 2 was the most abundant category among the four degradome libraries (Tables 3, 4 and Additional file 11).
Table 3

Conserved maize miRNA targets identified by degradome sequencing in this study

miRNAa

Target gene

Location of the target site

Repeat normalized read abundance (category)b

Target gene annotationc

Other degradome evidence

5′-RACE or genetic experiment

   

I

II

III

IV

   

miR156

GRMZM2G156621

CDS

2.50 (3)

3.50 (2)

39.00 (2)

 

SBP-box transcription factor#

[26]

N

miR156

GRMZM2G067624

3′-UTR

18.00 (2)

46.50 (2)

51.50 (2)

97.00 (2)

SBP-box transcription factor#

[26, 28]

N

miR156

GRMZM2G163813

CDS/3′-UTR

3.33 (2)

17.67 (2)

  

SBP-box transcription factor#

[26, 28]

N

miR156

GRMZM2G113779

3′-UTR

18.00 (2)

4.00 (2)

86.00 (2)

352.00 (0)

SBP-box transcription factor

[28]

N

miR156

GRMZM2G126827

CDS

2.50 (3)

3.50 (2)

39.00 (2)

 

SBP-box transcription factor#

[26]

N

miR156

GRMZM2G318882

5′-UTR

0.03 (1)

   

SBP-box transcriptionfactor

N

N

miR156

GRMZM2G136158

CDS

0.03 (1)

   

SBP-box transcription factor

N

N

miR156

GRMZM2G460544

CDS

  

2.00 (3)

 

SBP-box transcription factor#

[26, 28]

N

miR156

GRMZM5G878561

CDS

  

6.50 (3)

7.50 (0)

SBP-box transcription factor

[26]

N

miR156

GRMZM2G307588

CDS

  

2.00 (3)

 

SBP-box transcription factor#

[26, 28]

N

miR156

GRMZM2G371033

CDS

  

6.50 (1)

7.50 (0)

SBP-box transcription factor

[26]

N

miR156

GRMZM2G160917

CDS/3′-UTR

  

2.00 (3)

 

SBP-box transcription factor#

[26, 28]

N

miR156

GRMZM2G126018

CDS

  

2.00 (2)

 

SBP-box transcription factor#

[28]

N

miR159

GRMZM2G139688

CDS

76.00 (2)

169.00 (0)

86.00 (2)

3.00 (2)

MYB domain transcription factor#

[26, 28]

N

miR159

GRMZM2G038195

CDS

2.5 (2)

 

5.50 (2)

 

Metallophosphoesterase#

N

N

miR159

GRMZM2G093789

CDS

 

0.67 (3)

  

MYB domain transcription factor#

[28]

N

miR159

GRMZM2G167088

CDS

 

2.00 (2)

 

0.33 (4)

MYB domain transcription factor#

N

N

miR159

GRMZM2G416652

CDS

 

2.00 (2)

 

0.33 (4)

Homeodomain-like#

N

N

miR159

GRMZM2G028054

CDS

 

24.00 (2)

  

MYB domain transcription factor#

[26, 28]

N

miR159

GRMZM2G075064

CDS

 

0.67 (2)

  

MYB domain transcription factor#

N

N

miR159

GRMZM2G423833

CDS

 

0.67 (3)

  

MYB domain transcription factor#

[28]

N

miR159

GRMZM2G089361

CDS

   

1.33 (3)

TCP Transcription factor#

[26]

N

miR159

GRMZM2G387828

CDS

   

4.00 (1)

unknown

N

N

miR160

GRMZM2G005284

CDS

2.00 (1)

2.00 (2)

  

Auxin response factor#

N

N

miR160

AC207656.3_FGT002

CDS

6.50 (0)

18.00 (2)

  

Auxin response factor#

[26]

N

miR160

GRMZM2G159399

CDS

8.5 (2)

23.00 (2)

 

1.00 (4)

Auxin response factor#

[26, 28]

N

miR160

GRMZM5G808366

CDS

10.00 (0)

3.00 (2)

32.00 (1)

 

Auxin response factor

N

N

miR160

GRMZM2G153233

CDS

24.00 (0)

68.00 (0)

37.00 (0)

1.00 (4)

Auxin response factor#

[26, 28]

N

miR160

GRMZM2G390641

CDS

 

2.33 (0)

5.00 (2)

 

Auxin response factor#

[26]

N

miR160

GRMZM2G081406

CDS

 

2.33 (3)

5.00 (3)

 

Auxin response factor#

[26, 28]

N

miR162

GRMZM2G040762

CDS

   

1.00 (4)

DICER-LIKE1

N

N

miR164

GRMZM2G457630

5′-UTR

 

2.50 (0)

  

No apical meristem (NAM) protein

N

N

miR164

GRMZM2G370846

3′-UTR

  

2.33 (1)

 

No apical meristem (NAM) protein

N

N

miR164

GRMZM2G370850

3′-UTR

  

2.33 (1)

 

No apical meristem (NAM) protein

N

N

miR164

GRMZM2G393433

CDS

  

2.50 (3)

 

Helix-loop-helix DNA-binding#

[28]

N

miR164

GRMZM2G163975

CDS

-

-

2.50 (1)

-

Helix-loop-helix DNA-binding#

N

N

miR166

GRMZM2G109987

CDS

0.6 (3)

2.40 (2)

15.80 (2)

27.80 (2)

bZIP transcription factor (rld1)#

[28]

N

miR166

GRMZM2G042250

CDS

 

2.00 (2)

15.75 (2)

17.00 (2)

bZIP transcription factor (rld2)#

[28]

N

miR166

GRMZM2G469551

CDS

  

25.50 (2)

28.00 (0)

bZIP transcription factor#

[26]

N

miR166

GRMZM2G123644

CDS

   

0.60 (2)

unknown

[26]

N

miR166

GRMZM2G336718

CDS

   

0.60 (2)

unknown

[26]

N

miR166

GRMZM2G055957

3′-UTR

   

9.50 (0)

unknown

[26, 28]

N

miR166

GRMZM2G178102

CDS

   

0.60 (3)

bZIP transcription factor#

[26, 28]

N

miR166

GRMZM2G003509

CDS

   

0.60 (2)

bZIP transcription factor#

[26, 28]

N

miR167

GRMZM2G028980

CDS

 

77.00 (0)

  

Auxin response factor#

[26]

N

miR167

GRMZM2G089640

CDS

 

74.00 (0)

 

40.50 (0)

Auxin response factor#

[26, 30]

N

miR167

GRMZM2G078274

CDS

  

6.60 (2)

147.80 (0)

Auxin response factor#

[26, 30]

N

miR167

GRMZM2G475882

CDS

  

6.60 (2)

 

Auxin response factor#

[26, 28]

N

miR169

GRMZM5G829103

3′-UTR

32.00 (2)

1923.40 (0)

24.60 (2)

3.40 (2)

CCAAT-binding transcription factor

[26, 28]

N

miR169

GRMZM2G165488

3′-UTR

7.25 (2)

2804.25 (0)

8.25 (2)

1.25 (2)

CCAAT-binding transcription factor#

[26, 28, 30]

[30]

miR169

GRMZM2G037630

3′-UTR

12.00 (2)

13.00 (2)

 

2.00 (2)

CCAAT-binding transcription factor

[26, 28]

N

miR169

GRMZM2G000686

3′-UTR

3.60 (2)

21.80 (0)

2.70 (2)

0.10 (4)

CCAAT-binding transcription factor#

[26, 28]

N

miR169

GRMZM5G853836

3′-UTR

21.00 (2)

50.00 (2)

27.50 (2)

 

unknown

[26, 28]

N

miR169

GRMZM5G857944

3′-UTR

129.67 (0)

139.00 (0)

67.33 (0)

16.00 (0)

CCAAT-binding transcription factor

[26, 28]

N

miR169

GRMZM2G038303

3′-UTR

17.00 (2)

4.00 (2)

23.00 (0)

12.00 (0)

CCAAT-binding transcription factor#

[28, 30]

N

miR169

GRMZM5G849099

3′-UTR

4.00 (2)

   

unknown

N

N

miR169

GRMZM2G409430

CDS

3.50 (0)

   

unknown

N

N

miR169

GRMZM2G091964

3′-UTR

138.67 (2)

257.33 (0)

27.00 (2)

99.33 (0)

CCAAT-binding transcription factor#

[26, 28, 30]

[30]

miR169

GRMZM2G078124

CDS/3′-UTR

 

1.00 (2)

  

Molluscan rhodopsin C-terminal tail#

[28, 30]

[30]

miR171

GRMZM2G418899

CDS

1.80 (2)

80.40 (0)

4.60 (2)

0.60 (2)

GRAS transcription factor#

[26, 29]

N

miR171

GRMZM2G037792

CDS

1.80 (3)

80.40 (0)

4.60 (3)

0.60 (3)

GRAS transcription factor#

[26, 28, 29]

[25]

miR171

GRMZM5G825321

CDS

1.80 (2)

80.40 (0)

4.60 (3)

0.60 (2)

GRAS transcription factor

[26, 28]

N

miR171

GRMZM2G110579

CDS

0.33 (4)

35.67 (2)

2.67 (3)

1.67 (2)

GRAS transcription factor

[26, 28]

N

miR171

GRMZM2G098800

CDS

1.80 (2)

29.67 (0)

2.67 (3)

1.67 (2)

GRAS transcription factor#

[26, 28, 29]

N

miR172

GRMZM2G176175

CDS

1.40 (3)

9.20 (0)

6.40 (0)

1.80 (0)

AP2 transcription factor (sid1)#*

[28, 30]

[32, 33]

miR172

GRMZM5G862109

CDS/3′-UTR

1.40 (3)

80.20 (2)

8.73 (2)

1.80 (2)

AP2 transcription factor (ids1)#*

[28]

[32, 33]

miR172

GRMZM2G138676

CDS

0.20 (4)

   

AP2 transcription factor

N

N

miR172

GRMZM2G174784

CDS/3′-UTR

1.00 (3)

3.00 (2)

15.00 (2)

 

AP2 transcription factor#

[28]

N

miR172

GRMZM2G076602

CDS

 

10.00 (0)

13.00 (0)

 

AP2 transcription factor#

[30]

N

miR172

GRMZM5G879527

3′-UTR

   

2.50 (0)

AP2 transcription factor

N

N

miR172

GRMZM2G160730

3′-UTR

   

0.50 (4)

Glossy15#*

[28, 30]

[36]

miR319

GRMZM2G412073

CDS

4.00 (3)

   

unknown#

N

N

miR319

GRMZM2G028054

CDS

5.00 (2)

2.00 (2)

 

7.33 (2)

MYB domain transcription factor#

[26, 28]

N

miR319

GRMZM2G180568

CDS

 

3.00 (0)

17.00 (1)

 

unknown

N

N

miR319

GRMZM2G020805

CDS

 

1.00 (4)

  

unknown

[26, 28]

N

miR319

GRMZM2G089361

CDS

 

5.00 (2)

8.33 (0)

88.00 (0)

TCP Transcription factor#

[26, 28, 30]

N

miR319

GRMZM2G115516

CDS

 

5.00 (2)

8.33 (2)

88.00 (0)

TCP Transcription factor#

[26, 28]

N

miR319

GRMZM2G109843

CDS

   

53.33 (0)

MYB domain transcription factor

[30]

N

miR319

GRMZM2G056612

CDS

   

70.33 (0)

TCP Transcription factor

N

N

miR319

GRMZM2G015037

CDS

   

0.33 (4)

MYB domain transcription factor

[26]

N

miR319

AC205574.3_FG006

CDS

   

0.33 (4)

TCP Transcription factor

[28]

N

miR390

GRMZM2G124744

3′-UTR

 

35.33 (0)

  

Inorganic pyrophosphatase

[28]

N

miR390

GRMZM2G155490

3′-UTR

  

56.00 (0)

 

unknown#

N

N

miR390

GRMZM5G806469

3′-UTR

  

13.00 (2)

 

unknown

[28]

N

miR390

GRMZM2G304745

CDS

  

16.00 (1)

 

Leucine-rich repeat#

N

N

miR393

GRMZM2G135978

CDS

 

7.00 (2)

10.00 (2)

 

Transport inhibitor response 1-like

[26, 28]

N

miR394

GRMZM2G119650

CDS

3.00 (3)

20.00 (2)

 

3.00 (2)

Cyclin-like F-box#

[26]

[28]

miR394

GRMZM2G064954

CDS

 

18.00 (2)

14.00 (2)

13.00 (2)

Cyclin-like F-box#

[26, 30]

N

miR395

GRMZM2G149952

CDS

 

1.00 (4)

  

ATP-sulfurylase#

[26, 28, 30]

N

miR396

GRMZM2G018414

CDS

2.50 (2)

  

447.50 (0)

Glutamine-Leucine-Glutamine#

N

N

miR396

GRMZM2G099862

CDS

5.00 (2)

 

2.25 (3)

30.75 (0)

Glutamine-Leucine-Glutamine#

[30]

N

miR396

GRMZM2G098594

CDS

1.00 (2)

  

0.33 (3)

Glutamine-Leucine-Glutamine#

N

N

miR396

GRMZM2G119359

CDS

1.00 (3)

  

0.33 (3)

Glutamine-Leucine-Glutamine

N

N

miR396

GRMZM2G478709

CDS

8.00 (2)

0.50 (4)

4.00 (3)

 

SKI-interacting protein SKIP#

N

N

miR396

GRMZM2G124566

CDS

 

0.33 (4)

  

Growth-regulating factor#

N

N

miR396

GRMZM2G045977

CDS

 

0.33 (4)

  

Growth-regulating factor#

N

N

miR396

GRMZM2G041223

CDS

 

0.33 (4)

  

Putative growth-regulating factor 6#

[30]

N

miR396

GRMZM2G129147

CDS

 

0.33 (4)

  

Growth-regulating factor (GDF5)#

[30]

N

miR396

GRMZM2G178261

CDS

 

0.50 (3)

  

Growth-regulating factor 1 (GDF1)#

[28, 30]

N

miR396

GRMZM2G443903

CDS

 

0.50 (3)

  

K Homology#

[30]

N

miR396

GRMZM2G033612

CDS

  

16.00 (2)

637.00 (0)

Glutamine-Leucine-Glutamine

N

N

miR397

GRMZM2G094699

CDS

  

5.00 (2)

 

unknown

N

N

miR398

AC234183.1_FGT002

CDS

   

17.00 (0)

unknown

N

N

miR399

GRMZM2G153087

CDS

1.00 (4)

   

PHD finger protein

N

N

miR399

GRMZM2G082384

CDS

  

2.25 (3)

 

Mrp, conserved site;ATPase-like

N

N

miR529

GRMZM2G318882

5′-UTR

0.03 (1)

   

SBP-box transcription factor

N

N

miR529

GRMZM2G136158

CDS

0.03 (1)

0.04 (1)

0.01 (1)

0.001 (1)

SBP-box transcription factor

N

N

miR529

GRMZM2G031501

5′-UTR

0.20 (4)

   

unknown

[26, 30]

N

miR529

GRMZM5G878561

CDS

 

1.50 (3)

 

1.50 (2)

SBP-box transcription factor

[26]

N

miR529

GRMZM2G031983

5′-UTR

 

0.04 (1)

0.01 (1)

 

SBP-box transcription factor

N

N

miR529

GRMZM2G371033

CDS

 

1.50 (3)

 

1.50 (2)

SBP-box transcription factor

[26]

N

miR529

GRMZM2G163813

CDS/3′-UTR

 

0.33 (4)

  

SBP-box transcription factor#

[26]

N

miR529

GRMZM2G149022

CDS

  

0.01 (1)

 

unknown

N

N

miR529

GRMZM2G084947

5′-UTR

  

0.01 (1)

 

unknown

N

N

miR529

GRMZM2G169121

3′-UTR

  

0.01 (1)

 

unknown

N

N

miR529

GRMZM2G062052

5′-UTR

  

0.01 (1)

 

ZCN19 protein

N

N

miR529

GRMZM2G148074

5′-UTR

  

0.01 (1)

 

Transcription factor, K-box

N

N

miR529

GRMZM2G031870

5′-UTR

  

0.01 (1)

 

SBP-box transcription factor

N

N

miR529

GRMZM2G096234

3′-UTR

  

0.01 (1)

 

Non-specific lipid-transfer protein

N

N

miR529

GRMZM2G141955

5′-UTR

  

0.01 (1)

 

SBP-box transcription factor

N

N

miR529

AC233899.1_FGT004

5′-UTR

  

0.01 (1)

 

homeodomain-leucine zipper transcription factor

N

N

miR529

GRMZM2G165355

CDS

  

0.01 (1)

 

zinc finger protein 7

N

N

miR529

GRMZM2G131280

3′-UTR

   

0.001 (1)

SBP-box transcription factor

N

N

TAS3

GRMZM2G056120

CDS

0.50 (3)

1.25 (3)

11.25 (2)

5.00 (2)

DNA binding

N

N

TAS3

GRMZM5G874163

3′-UTR

0.50 (3)

1.58 (2)

11.25 (2)

5.00 (2)

DNA binding

N

N

TAS3

GRMZM2G437460

CDS

 

11.00 (2)

8.50 (2)

0.50 (4)

Auxin response factor

N

N

TAS3

GRMZM2G030710

CDS

  

1287.00 (0)

1.00 (4)

DNA binding

N

N

aMiRNA data from miRBase 19.0. bCalculation based on the method in Addo-Quaye et al.[47],. cGene annotations fromB73 RefGen_v2 (release 5b. 60 in February 2011). *validated in Zhai et al; Zhao et al. [28, 30], #Predicted by Zhang et al. [17]. Abbreviation: tpm, transcripts per million; CDS, coding sequence; UTR, untranslated region; siRNA, small interfering RNA.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-15-25/MediaObjects/12864_2013_Article_5669_Fig3_HTML.jpg
Figure 3

Summary of cleaved miRNA target categories found with degradome analyses in four developmental stages of maize ears.

Table 4

Non-conserved maize miRNA targets identified by degradome sequencing in this study

miRNAa

Target gene

Location of the target site

Repeat normalized read abundance (category)b

Target gene annotationc

Other degradome evidence

5′-RACE or genetic experiment

   

I

II

III

IV

   

miR2275

GRMZM2G044004

3′-UTR

0.33 (4)

   

nucleic acid binding protein

N

N

miR2275

GRMZM2G154532

5′-UTR/CDS

   

0.57 (2)

NAD dependent epimerase/dehydratase family protein

N

N

miR2275

GRMZM2G329532

CDS

   

1.00 (1)

unknown

N

N

miR2118

GRMZM2G066406

5′-UTR

  

16.00 (1)

2.00 (1)

unknown

N

N

miR2118

GRMZM2G031788

CDS

   

2.00 (1)

unknown

N

N

miRs4

GRMZM2G078124

CDS/3′-UTR

 

1.00 (2)

  

Molluscan rhodopsin C-terminal tail

N

N

miRs4

GRMZM5G829103

3′-UTR

 

1923.40 (0)

  

CCAAT-binding transcription factor#

[26, 28]

N

miRs4

GRMZM2G165488

3′-UTR

 

2804.25 (0)

  

CCAAT-binding transcription factor#

[26, 28]

N

miRs4

GRMZM2G000686

3′-UTR

 

21.80 (0)

  

CCAAT-binding transcription factor#

[26, 28]

N

miRs9

GRMZM2G467356

CDS

   

12.00 (0)

Ferredoxin

N

N

miRs14

GRMZM2G079683

3′-UTR

1.00 (4)

   

unknown

N

N

miRs15

GRMZM2G091189

3′-UTR

 

0.33 (4)

  

transcription factor

N

N

miRs17

GRMZM2G160041

CDS

0.25 (4)

  

1.75 (0)

DNA binding protein

N

N

miRs17

GRMZM2G085550

3′-UTR

  

5.00 (1)

 

unknown

N

N

aMiRNA data from miRBase 19.0. bCalculation based on the method in Addo-Quaye et al. [47], cGene annotations fromB73 RefGen_v2 (release 5b. 60 in February 2011). #Predicted by Zhang et al. [17]. Abbreviation: tpm, transcripts per million; CDS, coding sequence; UTR, untranslated region; siRNA, small interfering RNA.

We identified target genes for almost all of the 22 conserved miRNA families. The conserved miRNAs were able to target various numbers of genes, ranging from 1 to 18. Among the conserved miRNA families, zma-miR156 and zma-miR529 had the highest number of gene targets. zma-miR156 targeted 13 unique genes including SPL genes and zma-miR529 targeted 18 unique genes including ZCN19 (a possible maize FT ortholog) (Table 3), indicating that these two families might play key roles in ear development [31, 54]. Most of the conserved miRNAs targeted multiple gene loci. Their gene targets were members of different families of transcription factors, such as SBP-box transcription factor, AUXIN RESPONSE FACTOR (ARF), TCP, MYB, bZIP, AP2, and GRAS. We also identified 57 new target genes of conserved miRNAs in maize (Table 3). Among the 127 miRNA target genes, 67 (53%) had been predicted previously [17], 56 (44%) cross-validated with other degradome libraries prepared from plants under different stress conditions [25, 2830], and 8 have been validated by 5′-RACE and/or genetic experiments [25, 28, 30, 32, 33, 36]. The targets of conserved miRNAs were highly abundant in the four sequenced target libraries, and were often classified as category 0, 1, or 2 targets (Table 3, Additional file 11). For instance, miR169 targeted seven different CCAAT-binding transcription factors in the four stages (category 0 or 2) with very high abundance, but it also guided the slicing of three other non-conserved targets with very low abundance. Interestingly, some target transcripts were regulated by pairs of miRNAs: both miR156 and miR529 targeted five members of the same SBP family, and the miR159/319 pair regulated three MYB domain transcription factors. This result suggested that there is complex regulation of these genes by these miRNA pairs, consistent with the findings of a previous study [49] (Table 3).

Out of 26 non-conserved zma-miRNAs including 21 new miRNAs with four corresponding miRNA*, we identified targets for only seven miRNAs (miR2118, miR2775, miRs4, miRs9, miRs14, miRs15 and miRs17). We used absolute numbers to plot the cleavages on target mRNAs; this was referred to as a target plot (t-plot) by German et al.[48]. Except for miRs4, the targets mostly belonged to category 2 or 4 with very low abundance, which differed from the targets of conserved miRNAs (Tables 3, 4). Four identified targets of miRs4 (category 0 or 2) were the same as those of miR169, providing further evidence that miRs4 is a member of the miR169 family.

GO analysis of targets regulated by differentially expressed miRNAs

In our study, we predicted 72 genes (117 transcripts) for 62 differentially expressed miRNAs from 11 miRNA families. More than 90% of these miRNAs had putative functions (Tables 2, 3 and Additional file 13). 73% of these differentially expressed miRNA families played an important role in post-transcriptional regulation by targeting mRNAs encoding transcription factors in SBP, ARF, GRAS, and AP2 families (Tables 2, 3 and Additional file 13). GO analysis revealed that the target genes were involved in a wide spectrum of regulatory functions and biological processes including regulation of transcription, DNA binding, response to hormone stimulus, nucleic acid metabolic process, gene expression, cellular macromolecule synthesis, and cellular nitrogen compound metabolism. This was consistent with the fact that the small RNA and the degradome libraries for miRNA sequence analysis were constructed from developing maize ears (Additional file 14). In addition, a specific feature of our study was that we found more genes in families involved in metabolic process, biological regulation, cellular biosynthetic process, and nucleic acid binding function at the later stages of maize ear development (Table 2, Additional file 14). The accumulation of dry matter such as starch and saccharides is the main event in ear development, and a large number of target genes may participate in this pathway. The differentially expressed miRNAs may regulate expression of these target genes to control ear development and biomass yield in maize.

Discussion

Small RNAs play important roles in gene regulation in plants [12]. In this study, we have annotated miRNA genes based on the complete assembly of the maize genome. In total, 98 known miRNAs and 26 new miRNAs were identified in maize ears by deep sequencing. This confirmed previous results reported by Zhang et al. [17]. These newly identified miRNAs may belong to lineage-specific families, and showed little or no expression at the miRNA level. We identified 62 miRNAs as differentially expressed miRNAs by microarray assays.

The recently reported high-throughput experimental approach [47, 48] allowed us to create a detailed miRNA: target interaction atlas for maize. In the current work, we identified a total of 131 genes (245 transcripts) targeted by 102 small RNAs including 98 miRNAs and 4 ta-siRNAs (Tables 3, 4 and Additional file 12). Among the 131 genes, 54 were cross-validated in other degradome libraries [25, 2830], by 5′-RACE, and/or by genetic experiments [17, 25, 30, 32, 36], showing that degradome sequencing is a powerful tool for identifying targets regulated by miRNAs. Surprisingly, most highly conserved miRNAs were detectable in maize ears at all four developmental stages (Additional file 8), but sliced targets were not detected at all stages (Additional file 12). It is possible that the differentially expressed miRNAs regulate both the spatial pattern and the level of target mRNA expression, as previously demonstrated in some cases [55, 56]. It is equally possible that this represents a limitation of degradome sequencing. Results can be affected by many unpredictable factors such as ligation efficiency, PCR bias, etc. There were 127 target genes of 22 conserved miRNA families. Among the target genes, 72.4% encoded transcription factors (Table 3). These targets were not only conserved families, such as SBP, MYB, ARF, bZIP (basic-leucine Zipper), NAC, GRAS, AP2, and TCP transcription factor gene families, but also non-conserved genes encoding metallophosphoesterase, DICER-LIKE1, No Apical Meristem (NAM) proteins, and PHD finger proteins. The conserved targets may participate in maize ear development. We also identified 13 genes targeted by non-conserved miRNAs. One ARF gene and three DNA-binding transcription factor genes cleaved by ta-siRNAs were also identified (Table 4). The conserved miRNAs silenced more targets than did maize-specific miRNAs. It is possible that conserved miRNAs play a crucial role in post-transcriptional regulation in different plant species [12]. However, maize-specific miRNAs may function only to regulate gene expression during gramineae- or maize-specific biological processes. Although conserved miRNAs mainly regulate genes encoding transcription factors, maize-specific miRNAs are considered to be young miRNAs that have evolved recently, and are often expressed at lower levels than conserved miRNAs [39, 57].

Previous studies showed that miR156 and miR172 function throughout flower development from the earliest stages (floral induction, stage I) to very late stages (floral organ cell-type specification, stage IV) [3134]. miR156a-l probably targets several SPL genes during the juvenile-to-adult phase transition in maize (Figure 4a, Tables 2 and 3), and is postulated to indirectly activate miR172 via SPL[31]. miR172 has been shown to negatively regulate GL15 (Table 3), which promotes maintenance of the juvenile state [31]. The levels of miR156 and miR172 are conflicting during phase transition (Figure 4b). Meanwhile, miR172e likely controls IDS1 and SID1, which are responsible for maize spikelet sex determination and meristem cell fate [3234], by both translational repression and mRNA degradation (Table 3; Figure 4a). Beyond miR156 and miR172, miR164 targets genes encoding NAM proteins, and may be involved in regulating ear development (Table 3), similar to how miR164 is postulated to regulate NAC-domain targets in Arabidopsis [58]. Although most miRNA families appear to target a single class of targets, the miR159/319 family regulates both MYB and TCP transcription factors, which may control petal morphogenesis as previously reported [59].
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-15-25/MediaObjects/12864_2013_Article_5669_Fig4_HTML.jpg
Figure 4

miR156 and miR172 in maize flower development (Adapted from Poethig (2009). (a) The development of the maize ear (female inflorescence) and the juvenile phase regulated by miR156 and miR172. In the ear, the inflorescence meristem (IM, stage I) directly gives rise to spikelet pair meristems (SPM, stage II). SPMs give rise to two spikelet meristems (SM, stage III), which subsequently form two floral meristems (FM, stage IV). miR172 and its targets IDS1 and SID1 function to influence the SM to FM conversion. IDS1 is also a negative regulator of its homolog SID1. Both IDS1 and SID1 are positive regulators of the SM to FM transition. (b) The expression pattern of zma-miR156a and zma-miR172e. * indicated the significant difference in adjacent two developmental stages (one-way ANOVA and Fisher’s LSD, p < 0.01, n = 4).

Some miRNAs have been shown to be involved the signaling pathway that mediates responses to the phytohormone auxin. For example, miR167 targets four AUXIN RESPONSE FACTOR (ARF) genes, and miR160 targets six ARF genes. In addition to the miRNAs mentioned above, one miRNA family (miR162) targets a gene central to miRNA genesis; the differentially expressed zma-miR162 targets DICER-LIKE1 (DCL1), a homolog of DCL1 in Arabidopsis that is required for miRNA accumulation [60]. In summary, genome-wide identification of all targets provided useful information to explore the functions of miRNAs in maize.

Conclusions

In this study, we have confirmed the expression of conserved, known non-conserved and new maize miRNAs using high-throughput approaches to better understand the role of miRNAs in developmental maize ears. Besides, we have identified 131 target genes of both known and new miRNAs and ta-siRNAs using recently developed tools for the global identification of miRNA targets. Specifically, 72 genes (117 transcripts) targeted by 62 differentially expressed miRNAs from 11 miRNA families may play important roles in ear development in maize. Maize represents a model for cultivated crop plants. As these characters are quite different for other model plants (e.g. Arabidopsis and Medicago), we expect to discover new roles of miRNAs in post-transcriptional regulation. We also provided some evidence of the important function of miRNAs in regulating developmental process. Identification and characterization of this important class of regulatory genes in maize may improve our understanding of molecular mechanism controlling maize ear development.

Methods

Plant materials and RNA isolation

Seed of maize inbred lineB73 was first sterilized and germinated in an incubator, then grown in a controlled environment at 28°C/21°C (day/night) under a 16-h day/8-h night photoperiod with a relative humidity of 70%. Ear development can be divided into four stages: the growth point elongation phase, spikelet differentiation phase, the floret primordium differentiation phase and floret organ differentiation phase. Plant materials (ears) were collected as described previously [48]. Briefly, ears were manually collected at the four developmental stages according to the plant features (number of visible leaves, leaf age index, and number of unfolded and folded leaves) combined with microscopic observation. All the samples were harvested and immediately frozen in liquid nitrogen and stored at −80°C. The total RNA from each sample was then isolated using Trizol (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions.

Small RNA library preparation and sequencing

The total RNAs were pooled for each of four developmental stages for Solexa sequencing. After small RNA cloning, the sequencing procedures were conducted as described previously [61]. In brief, sequencing was performed as follows: approximate 100 ug of total RNA was purified by polyacrylamide gel electrophoresis (PAGE), to enrich for molecules in the range of 18–30nt, and ligated with adapters to the 5′ and 3′ terminals of the RNA. Then, small RNA molecules were used as templates for cDNA synthesis. In total, 18 PCR cycles and agarose gels were used for amplification and fragments of around 90 nt including both small RNA and adaptors, separately. The purified DNA was used Solexa sequence analysis performed by the Illumina platform. Digital-quality data were generated from the image files produced by the sequencer. After quality control using common pipeline, clean reads were directly used for further bioinformatics analysis.

Degradome library construction

Small cDNA libraries using the sliced ends of poly adenylated transcripts from maize ears of four developmental stages were constructed according to previous reports [47, 48]. By using the Oligotex kit (Qiagen, location), 200 μg of total RNA was used for extracting poly (A) RNA, which were ligated with an RNA adapter consisting of a MmeI recognition site in its 3′ end. After ligation, first-strand cDNA was generated using oligod (T) and the PCR product was amplified using five PCR cycles. The PCR product was purified and digested with MmeI. The digested PCR product was then ligated to a double-stranded DNA oligonucleotide with degenerate nucleotides at the 5′-or 3′-ends. The ligation product was further gel purified and amplified using 10 PCR cycles. The final PCR product was purified and sequenced using Illumina’s sequencing by synthesis (SBS) sequencing technology.

MiRNA microarray assays

MiRNA microarray assays of different developmental stages were performed by LC Sciences (Houston, TX, USA). The custom μparaflo™ microfluidic chip contained 632 unique plant miRNAs of release version 18, representing 1,187 miRNAs (including miRNA*) from 4 plant species (http://microrna.sanger.ac.uk/) [13], and 26 additional unique miRNAs of maize identified by Solexa sequencing, representing 26 novel miRNAs. Each chip contained four repetitions of each probe. In total, the 1,215 miRNAs were composed of 224, 496, 148 and 347 miRNAs from Arabidopsis thaliana, Oryza sativa, Sorghum bicolor and Zea mays, separately. RNA labeling, microarray hybridization, array scanning, and data’s analysis were performed essentially as previously described [46].

Bioinformatics analysis of sequencing data

Both small RNA reads and degradome reads were generated by Illumina Genome Analyzer II. As for the small RNA library, the data were processed and analyzed as previously described by Wang et al.[27] and Zhang et al.[17]. In brief, unique reads ranging from18-25 nt were collected and mapped to the maize genome (B73 RefGen_v2 (release 5b. 60 in February 2011)) reference sequences [62] by SOAP2 [37]. After removing sequences matching non-coding rRNAs, tRNAs, snRNAs and snoRNAs in the Rfam and NCBI Genbank databases, the matched Solexa reads that were extracted 250 nt of the sequence flanking the genomic sequences were used for RNA secondary structure prediction, which was performed by mFold 3.5 [63] and analyzed by MIREAP (https://sourceforge.net/projects/mireap/) to identify new candidates using default settings. The candidate miRNA list was further trimmed based on the criteria as described [17, 42]. Based on the hairpin structure of the pre-miRNA, the corresponding miRNA star sequence was also identified.

Degradome reads were filtered using custom Perl script. The remaining distinct 20–21 nt sequences that perfectly matched maize contigs were collected for further analysis. The 15 nt upstream and 5′ end of the reads that mapped to maize contigs were extracted to generate 30-sequence tags, which were used to align to newly identified miRNAs and miRBase (Release19.0, August, 2012) using the Cleave and pipeline [52]. Alignments were collected as candidate targets if they fulfilled the criteria as described before [50].

GO functional enrichment analysis of all candidate targets during different developmental stages was carried out using Blast2GO (version 2.3.5, http://www.blast2go.org/) and GO annotations were performed using AgriGO (http://bioinfo.cau.edu.cn/agriGO/). KEGG pathway analyses of differentially expressed genes were performed using Cytoscape software (version 2.6.2) (http://www.cytoscape.org/) with the ClueGO plugin (http://www.ici.upmc.fr/cluego/cluegoDownload.shtml) [64].

Stem-loop quantitative real-time PCR (qRT-PCR) analysis

Validations of 13 randomly selected mature miRNAs were carried out by stem-loop reverse transcription-PCR (RT–PCR) (Additional file 15). Total RNA (200 ng) was used to initiate the reverse transcription reaction. Primers for the stem–loop RT-PCR were designed using methods as described by Chen et al.[65] and Varkonyi-Gasic et al.[66]. The stem-loop RT-PCR was using the Applied Bio systems 7500 Real-Time PCR System (Applied Bio systems, Foster City, CA). All primers were listed in Additional file 12: Table S8. All reactions were run in triplicate. 5S rRNAs was used as the internal control for stem–loop RT-PCR [67].

Data access

The RNA sequencing data have been deposited in the NCBI under the accession number GSE47837.

Notes

Declarations

Acknowledgements

The work was supported by grants from the National Natural Science Foundation of China (31271740), the National Hi-Tech program of China (2012AA10307), and the Major Project of China on New varieties of GMO Cultivation (2011ZX08003-003).

Authors’ Affiliations

(1)
Maize Research Institute of Sichuan Agricultural University/Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Ministry of Agriculture
(2)
Zunyi Institute of Agricultural Sciences
(3)
Interdepartmental Genetics Graduate Program, Iowa State University
(4)
Animal Nutrition Institute, Sichuan Agricultural University
(5)
BGI-Shenzhen
(6)
Maize Research Institute of Heilongjiang, Academy of Agricultural Sciences
(7)
Sichuan Agricultural University
(8)
Department of Agronomy, Iowa State University

References

  1. Bennetzen JL, Hake SC: Its Biology. Handbook of Maize. 2009, New York: SpringerView ArticleGoogle Scholar
  2. Vollbrecht E, Schmidt R: Development of the Inflorescences. Handbook of Maize: Its Biology. Edited by: Bennetzen JL, Hake SC. 2009, New York: Springer, 13-40.View ArticleGoogle Scholar
  3. Upadyayula N, da Silva HS, Bohn MO, Rocheford TR: Genetic and QTL analysis of maize tassel and ear inflorescence architecture. Theor Appl Genet. 2006, 112 (4): 592-606.PubMedView ArticleGoogle Scholar
  4. Zhao W, Canaran P, Jurkuta R, Fulton T, Glaubitz J, Buckler E, Doebley J, Gaut B, Goodman M, Holland J, et al: Panzea: a database and resource for molecular and functional diversity in the maize genome. Nucleic Acids Res. 2006, 34 (Database issue): D752-D757.PubMed CentralPubMedView ArticleGoogle Scholar
  5. Nag A, Jack T: Sculpting the flower; the role of microRNAs in flower development. Curr Top Dev Biol. 2010, 91: 349-378.PubMedView ArticleGoogle Scholar
  6. Liu Q, Chen YQ: Insights into the mechanism of plant development: interactions of miRNAs pathway with phytohormone response. Biochem Biophys Res Commun. 2009, 384 (1): 1-5.PubMedView ArticleGoogle Scholar
  7. Lisch D: How important are transposons for plant evolution?. Nat Rev Genet. 2013, 14 (1): 49-61.PubMedView ArticleGoogle Scholar
  8. Voinnet O: Post-transcriptional RNA silencing in plant-microbe interactions: a touch of robustness and versatility. Curr Opin Plant Biol. 2008, 11 (4): 464-470.PubMedView ArticleGoogle Scholar
  9. Sunkar R, Chinnusamy V, Zhu J, Zhu JK: Small RNAs as big players in plant abiotic stress responses and nutrient deprivation. Trends Plant Sci. 2007, 12 (7): 301-309.PubMedView ArticleGoogle Scholar
  10. Carthew RW, Sontheimer EJ: Origins and Mechanisms of miRNAs and siRNAs. Cell. 2009, 136 (4): 642-655.PubMed CentralPubMedView ArticleGoogle Scholar
  11. Chuck G, Candela H, Hake S: Big impacts by small RNAs in plant development. Curr Opin Plant Biol. 2009, 12 (1): 81-86.PubMedView ArticleGoogle Scholar
  12. Jones-Rhoades MW, Bartel DP, Bartel B: MicroRNAS and their regulatory roles in plants. Annu Rev Plant Biol. 2006, 57: 19-53.PubMedView ArticleGoogle Scholar
  13. Kozomara A, Griffiths-Jones S: miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 2011, 39 (Database issue): D152-D157.PubMed CentralPubMedView ArticleGoogle Scholar
  14. Fahlgren N, Howell MD, Kasschau KD, Chapman EJ, Sullivan CM, Cumbie JS, Givan SA, Law TF, Grant SR, Dangl JL, et al: High-throughput sequencing of Arabidopsis microRNAs: evidence for frequent birth and death of MIRNA genes. PLoS One. 2007, 2 (2): e219-PubMed CentralPubMedView ArticleGoogle Scholar
  15. Meyers BC, Axtell MJ, Bartel B, Bartel DP, Baulcombe D, Bowman JL, Cao X, Carrington JC, Chen X, Green PJ, et al: Criteria for annotation of plant MicroRNAs. Plant Cell. 2008, 20 (12): 3186-3190.PubMed CentralPubMedView ArticleGoogle Scholar
  16. Nobuta K, Venu RC, Lu C, Belo A, Vemaraju K, Kulkarni K, Wang W, Pillay M, Green PJ, Wang GL, et al: An expression atlas of rice mRNAs and small RNAs. Nat Biotechnol. 2007, 25 (4): 473-477.PubMedView ArticleGoogle Scholar
  17. Zhang L, Chia JM, Kumari S, Stein JC, Liu Z, Narechania A, Maher CA, Guill K, McMullen MD, Ware D: A genome-wide characterization of microRNA genes in maize. PLoS Genet. 2009, 5 (11): e1000716-PubMed CentralPubMedView ArticleGoogle Scholar
  18. Maher C, Timmermans M, Stein L, Ware D: 2004 IEEE Computational Systems Bioinformatics Conference (CSB’04). Identifying MicroRNAs in Plant Genomes. 2004, 718-723.Google Scholar
  19. Zhang B, Pan X, Anderson TA: Identification of 188 conserved maize microRNAs and their targets. FEBS Lett. 2006, 580 (15): 3753-3762.PubMedView ArticleGoogle Scholar
  20. Zhang BH, Pan XP, Wang QL, Cobb GP, Anderson TA: Identification and characterization of new plant microRNAs using EST analysis. Cell Res. 2005, 15 (5): 336-360.PubMedView ArticleGoogle Scholar
  21. Juarez MT, Kui JS, Thomas J, Heller BA, Timmermans MC: microRNA-mediated repression of rolled leaf1 specifies maize leaf polarity. Nature. 2004, 428 (6978): 84-88.PubMedView ArticleGoogle Scholar
  22. Mica E, Gianfranceschi L, Pe ME: Characterization of five microRNA families in maize. J Exp Bot. 2006, 57 (11): 2601-2612.PubMedView ArticleGoogle Scholar
  23. Zhang Z, Lin H, Shen Y, Gao J, Xiang K, Liu L, Ding H, Yuan G, Lan H, Zhou S, et al: Cloning and characterization of miRNAs from maize seedling roots under low phosphorus stress. Mol Biol Rep. 2012, 39 (8): 8137-8146.PubMed CentralPubMedView ArticleGoogle Scholar
  24. Ding D, Wang Y, Han M, Fu Z, Li W, Liu Z, Hu Y, Tang J: MicroRNA transcriptomic analysis of heterosis during maize seed germination. PLoS One. 2012, 7 (6): e39578-PubMed CentralPubMedView ArticleGoogle Scholar
  25. Jiao Y, Song W, Zhang M, Lai J: Identification of novel maize miRNAs by measuring the precision of precursor processing. BMC Plant biology. 2011, 11: 141-PubMed CentralPubMedView ArticleGoogle Scholar
  26. Shen Y, Jiang Z, Lu S, Lin H, Gao S, Peng H, Yuan G, Liu L, Zhang Z, Zhao M, et al: Combined small RNA and degradome sequencing reveals microRNA regulation during immature maize embryo dedifferentiation. Biochem Biophys Res Commun. 2013, 441 (2): 425-430.PubMedView ArticleGoogle Scholar
  27. Wang L, Liu H, Li D, Chen H: Identification and characterization of maize microRNAs involved in the very early stage of seed germination. BMC Genom. 2011, 12: 154-View ArticleGoogle Scholar
  28. Zhai L, Liu Z, Zou X, Jiang Y, Qiu F, Zheng Y, Zhang Z: Genome-wide identification and analysis of microRNA responding to long-term waterlogging in crown roots of maize seedlings. Physiol Plant. 2013, 147 (2): 181-193.PubMedView ArticleGoogle Scholar
  29. Zhao M, Tai H, Sun S, Zhang F, Xu Y, Li WX: Cloning and characterization of maize miRNAs involved in responses to nitrogen deficiency. PLoS One. 2012, 7 (1): e29669-PubMed CentralPubMedView ArticleGoogle Scholar
  30. Zhao Y, Xu Z, Mo Q, Zou C, Li W, Xu Y, Xie C: Combined small RNA and degradome sequencing reveals novel miRNAs and their targets in response to low nitrate availability in maize. Ann Bot. 2013, 112 (3): 633-642.PubMed CentralPubMedView ArticleGoogle Scholar
  31. Chuck G, Cigan AM, Saeteurn K, Hake S: The heterochronic maize mutant Corngrass1 results from overexpression of a tandem microRNA. Nat Genet. 2007, 39 (4): 544-549.PubMedView ArticleGoogle Scholar
  32. Chuck G, Meeley R, Hake S: Floral meristem initiation and meristem cell fate are regulated by the maize AP2 genes ids1 and sid1. Development. 2008, 135 (18): 3013-3019.PubMedView ArticleGoogle Scholar
  33. Chuck G, Meeley R, Irish E, Sakai H, Hake S: The maize tasselseed4 microRNA controls sex determination and meristem cell fate by targeting Tasselseed6/indeterminate spikelet1. Nat Genet. 2007, 39 (12): 1517-1521.PubMedView ArticleGoogle Scholar
  34. Chuck G, Meeley RB, Hake S: The control of maize spikelet meristem fate by the APETALA2-like gene indeterminate spikelet1. Genes Dev. 1998, 12 (8): 1145-1154.PubMed CentralPubMedView ArticleGoogle Scholar
  35. Irish EE: Experimental analysis of tassel development in the maize mutant tassel seed 6. Plant Physiol. 1997, 114 (3): 817-825.PubMed CentralPubMedGoogle Scholar
  36. Lauter N, Kampani A, Carlson S, Goebel M, Moose SP: microRNA172 down-regulates glossy15 to promote vegetative phase change in maize. Proc Natl Acad Sci U S A. 2005, 102 (26): 9412-9417.PubMed CentralPubMedView ArticleGoogle Scholar
  37. Li R, Yu C, Li Y, Lam TW, Yiu SM, Kristiansen K, Wang J: SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics. 2009, 25 (15): 1966-1967.PubMedView ArticleGoogle Scholar
  38. Moxon S, Jing R, Szittya G, Schwach F, Rusholme Pilcher RL, Moulton V, Dalmay T: Deep sequencing of tomato short RNAs identifies microRNAs targeting genes involved in fruit ripening. Genome Res. 2008, 18 (10): 1602-1609.PubMed CentralPubMedView ArticleGoogle Scholar
  39. Rajagopalan R, Vaucheret H, Trejo J, Bartel DP: A diverse and evolutionarily fluid set of microRNAs in Arabidopsis thaliana. Genes Dev. 2006, 20 (24): 3407-3425.PubMed CentralPubMedView ArticleGoogle Scholar
  40. Ambros V, Bartel B, Bartel DP, Burge CB, Carrington JC, Chen X, Dreyfuss G, Eddy SR, Griffiths-Jones S, Marshall M, et al: A uniform system for microRNA annotation. RNA. 2003, 9 (3): 277-279.PubMed CentralPubMedView ArticleGoogle Scholar
  41. Berezikov E, Cuppen E, Plasterk RH: Approaches to microRNA discovery. Nat Genet. 2006, 38 (Suppl): S2-S7.PubMedView ArticleGoogle Scholar
  42. Kutter C, Schob H, Stadler M, Meins F, Si-Ammour A: MicroRNA-mediated regulation of stomatal development in Arabidopsis. Plant Cell. 2007, 19 (8): 2417-2429.PubMed CentralPubMedView ArticleGoogle Scholar
  43. Wang X, Zhang J, Li F, Gu J, He T, Zhang X, Li Y: MicroRNA identification based on sequence and structure alignment. Bioinformatics. 2005, 21 (18): 3610-3614.PubMedView ArticleGoogle Scholar
  44. Jiang P, Wu H, Wang W, Ma W, Sun X, Lu Z: MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res. 2007, 35 (Web Server issue): W339-W344.PubMed CentralPubMedView ArticleGoogle Scholar
  45. Ng KL, Mishra SK: De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures. Bioinformatics. 2007, 23 (11): 1321-1330.PubMedView ArticleGoogle Scholar
  46. Ding D, Zhang L, Wang H, Liu Z, Zhang Z, Zheng Y: Differential expression of miRNAs in response to salt stress in maize roots. Ann Bot. 2009, 103 (1): 29-38.PubMed CentralPubMedView ArticleGoogle Scholar
  47. Addo-Quaye C, Eshoo TW, Bartel DP, Axtell MJ: Endogenous siRNA and miRNA targets identified by sequencing of the Arabidopsis degradome. Curr Biol. 2008, 18 (10): 758-762.PubMed CentralPubMedView ArticleGoogle Scholar
  48. German MA, Pillay M, Jeong DH, Hetawal A, Luo S, Janardhanan P, Kannan V, Rymarquis LA, Nobuta K, German R, et al: Global identification of microRNA-target RNA pairs by parallel analysis of RNA ends. Nat Biotechnol. 2008, 26 (8): 941-946.PubMedView ArticleGoogle Scholar
  49. Pantaleo V, Szittya G, Moxon S, Miozzi L, Moulton V, Dalmay T, Burgyan J: Identification of grapevine microRNAs and their targets using high-throughput sequencing and degradome analysis. Plant J. 2010, 62 (6): 960-976.PubMedGoogle Scholar
  50. Song QX, Liu YF, Hu XY, Zhang WK, Ma B, Chen SY, Zhang JS: Identification of miRNAs and their target genes in developing soybean seeds by deep sequencing. BMC Plant Biol. 2011, 11: 5-PubMed CentralPubMedView ArticleGoogle Scholar
  51. Zhou M, Gu L, Li P, Song X, Wei L, Chen Z, Cao X: Degradome sequencing reveals endogenous small RNA targets in rice (Oryza sativa L. ssp. indica). Front Biol. 2010, 5 (1): 67-90.View ArticleGoogle Scholar
  52. Addo-Quaye C, Miller W, Axtell MJ: CleaveLand: a pipeline for using degradome data to find cleaved small RNA targets. Bioinformatics. 2009, 25 (1): 130-131.PubMed CentralPubMedView ArticleGoogle Scholar
  53. Ma Z, Coruh C, Axtell MJ: Arabidopsis lyrata small RNAs: transient MIRNA and small interfering RNA loci within the Arabidopsis genus. Plant Cell. 2010, 22 (4): 1090-1103.PubMed CentralPubMedView ArticleGoogle Scholar
  54. Colasanti J, Muszynski M: The Maize Floral Transition. In: Handbook of Maize. Its Biology. Edited by: Bennetzen J, Hake S. 2009, New York: Springer, 41-55.Google Scholar
  55. Nagpal P, Ellis CM, Weber H, Ploense SE, Barkawi LS, Guilfoyle TJ, Hagen G, Alonso JM, Cohen JD, Farmer EE, et al: Auxin response factors ARF6 and ARF8 promote jasmonic acid production and flower maturation. Development. 2005, 132 (18): 4107-4118.PubMedView ArticleGoogle Scholar
  56. Wu MF, Tian Q, Reed JW: Arabidopsis microRNA167 controls patterns of ARF6 and ARF8 expression, and regulates both female and male reproduction. Development. 2006, 133 (21): 4211-4218.PubMedView ArticleGoogle Scholar
  57. Allen E, Xie Z, Gustafson AM, Sung GH, Spatafora JW, Carrington JC: Evolution of microRNA genes by inverted duplication of target gene sequences in Arabidopsis thaliana. Nat Genet. 2004, 36 (12): 1282-1290.PubMedView ArticleGoogle Scholar
  58. Mallory AC, Dugas DV, Bartel DP, Bartel B: MicroRNA regulation of NAC-domain targets is required for proper formation and separation of adjacent embryonic, vegetative, and floral organs. Curr Biol. 2004, 14 (12): 1035-1046.PubMedView ArticleGoogle Scholar
  59. Nag A, King S, Jack T: miR319a targeting of TCP4 is critical for petal growth and development in Arabidopsis. Proc Natl Acad Sci U S A. 2009, 106 (52): 22534-22539.PubMed CentralPubMedView ArticleGoogle Scholar
  60. Park W, Li J, Song R, Messing J, Chen X: CARPEL FACTORY, a Dicer homolog, and HEN1, a novel protein, act in microRNA metabolism in Arabidopsis thaliana. Curr Biol. 2002, 12 (17): 1484-1495.PubMedView ArticleGoogle Scholar
  61. Lu C, Meyers BC, Green PJ: Construction of small RNA cDNA libraries for deep sequencing. Methods. 2007, 43 (2): 110-117.PubMedView ArticleGoogle Scholar
  62. Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, Liang C, Zhang J, Fulton L, Graves TA, et al: The B73 maize genome: complexity, diversity, and dynamics. Science. 2009, 326 (5956): 1112-1115.PubMedView ArticleGoogle Scholar
  63. Zuker M: Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 2003, 31 (13): 3406-3415.PubMed CentralPubMedView ArticleGoogle Scholar
  64. Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman WH, Pages F, Trajanoski Z, Galon J: ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009, 25 (8): 1091-1093.PubMed CentralPubMedView ArticleGoogle Scholar
  65. Chen C, Ridzon DA, Broomer AJ, Zhou Z, Lee DH, Nguyen JT, Barbisin M, Xu NL, Mahuvakar VR, Andersen MR, et al: Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res. 2005, 33 (20): e179-PubMed CentralPubMedView ArticleGoogle Scholar
  66. Varkonyi-Gasic E, Wu R, Wood M, Walton EF, Hellens RP: Protocol: a highly sensitive RT-PCR method for detection and quantification of microRNAs. Plant methods. 2007, 3: 12-PubMed CentralPubMedView ArticleGoogle Scholar
  67. Lang Q, Jin C, Lai L, Feng J, Chen S, Chen J: Tobacco microRNAs prediction and their expression infected with Cucumber mosaic virus and Potato virus X. Mol Biol Rep. 2011, 38 (3): 1523-1531.PubMedView ArticleGoogle Scholar

Copyright

© liu et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement