Identification of miRNAs and Their Target Genes Associated with

May 21, 2015 - High seed vigor is significant for agriculture. Low seed vigor of sweet corn hindered the popularization of sweet corn (Zea mays L. sac...
4 downloads 5 Views 1MB Size
Download PDF
Article pubs.acs.org/JAFC

Identification of miRNAs and Their Target Genes Associated with Sweet Corn Seed Vigor by Combined Small RNA and Degradome Sequencing Shumin Gong,†,‡ Yanfei Ding,†,‡ Shanxia Huang,§ and Cheng Zhu*,‡ ‡

Key Laboratory of Marine Food Quality and Hazard Controlling Technology of Zhejiang Province, College of Life Sciences, China Jiliang University, Hangzhou, Zhejiang 310018, People’s Republic of China § College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang 310029, People’s Republic of China S Supporting Information *

ABSTRACT: High seed vigor is significant for agriculture. Low seed vigor of sweet corn hindered the popularization of sweet corn (Zea mays L. saccharata Sturt). To better understand the involvement and regulatory mechanism of miRNAs with seed vigor, small RNA libraries from seeds non-artificially aged and artificially aged for 2 days were generated by small RNA sequencing. A total of 27 differentially expressed miRNAs were discovered, of which 10 were further confirmed by real-time quantitative polymerase chain reaction. Furthermore, targets of miRNAs were identified by degradome sequencing. A total of 1142 targets that were potentially cleaved by 131 miRNAs were identified. Gene ontology (GO) annotations of target transcripts indicated that 26 target genes cleaved by 9 differentially expressed miRNAs might play roles in the regulation of seed vigor, such as peroxidase superfamily protein targeted by PC-5p-213179_17 playing a role in the oxidation−reduction process and response to oxidative stress. These findings provide valuable information to understand the involvement of miRNAs with seed vigor. KEYWORDS: sweet corn, seed vigor, miRNA regulation, small RNA sequencing, degradome sequencing

■

INTRODUCTION Maize (Zea mays L.) is one of the most important crops worldwide as well as a model plant for biological research.1 Sweet corn (Zea mays L. saccharata Sturt), with a high sugar content, is a variety of maize. It has better taste and more nutrition than normal maize. However, low seed vigor of sweet corn hindered the popularization of sweet corn in agriculture. High seed vigor is significant for agriculture. Germination rates and crop yields are all influenced by seed vigor.2 It is a challenge to produce high-vigor seeds. Small RNA may be involved in the regulation of seed vigor of sweet corn seeds. A number of miRNAs with specific function have been reported in maize seeds.3−5 MicroRNAs (miRNAs) are endogenous, small, single-stranded, non-coding RNA, which play an important role in the transcriptional and posttranscriptional regulation in eukaryotes.6,7 In plants, the miRNA genes are transcribed into primary miRNA (primiRNA) by RNA polymerase (RNA Pol) II or Pol III enzyme, and then the pri-miRNAs are processed into stem-loop precursors (pre-miRNAs) by dicer-like (DCL) proteins.8−11 Subsequently, the pre-miRNAs are further processed to miRNA::miRNA* duplex by DCL1, with assistance from the double-stranded RNA-binding protein HYL1. The miRNA::miRNA* duplexes are methylated by HEN1 on the 3′ terminal and loaded onto AGO1.12 Then, the miRNAs are exported to the cytoplasm by a HASTY protein and cleaved into mature miRNAs.13,14 Mature miRNAs are incorporated into the RNAinduced silencing complex (RISC), where the mature singlestranded miRNA guides the RNA slicing activity of AGO1 to partially complementary mRNA, while the miRNA* are degraded gradually.15−17 Unlike miRNAs in animals, plant © XXXX American Chemical Society

miRNAs generally interact with their targets through perfect or near-perfect complementarity and lead to the cleavage of target mRNA.18−20 To date, miRNAs are widely involved in biological processes in plant, such as development, cell differentiation, hormone regulation, and abiotic and biotic stress response.21−23 Recently, miRNAs involved in the development of seeds have been identified in Arabidopsis, rice, maize, and Brassica napus. miR156 targets SQUAMOSA promoter-binding proteinlike (SPL) 10 and SPL11, which led to abnormal cell divisions.24 miR159, targeting MYB33 and MYB101, two abscisic acid (ABA)-positive regulators was turned to play a vital role in seed germination.25 The miR167 was reported to target auxin response factor (ARF) 6 and ARF8, which regulate the auxin signal.26,27 In rice, there are several miRNAs, including miR168 and miR817, expressed differently in artificially aged seeds, indicating that they were associated with the rice seed vigor.28 It is unknown how miRNA expression changes during the process of seed aging in sweet corn. In this study, miRNAs in embryos of a maize hybrid, Yuetian13, and seeds artificially aged for 2 days were deepsequenced to identify miRNAs associated with seed vigor. Meanwhile, degradome sequencing technology was used to identify the target genes of differentially expressed miRNAs. These findings provide important information regarding regulatory networks involved in seed vigor in sweet corn. Received: January 29, 2015 Revised: May 9, 2015 Accepted: May 21, 2015

A

DOI: 10.1021/acs.jafc.5b00522 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

Journal of Agricultural and Food Chemistry

■

■

MATERIALS AND METHODS

Article

RESULTS AND DISCUSSION Seed Vigor Changes under Artificial Aging Treatment. Germination percentage reflects seed vigor. With sustained artificial aging under 45 °C and 100% RH conditions, seed vigor decreased gradually. The germination percentage reduced to about 50% after 2 days of aging (Figure 1).

Plant Materials and Germination Assay. The sweet corn (Yuetian 13) seeds were brought from Guangdong King Zuo Agricultural Science and Technology Company. The seeds were artificially aged under high temperature and humidity (45 °C and 100% relative humidity) conditions. Seeds were surface-sterilized. Seed germination was tested on moist filter paper. In each Petri dish (90 mm diameter), 20 seeds were randomly placed on the filter paper and then 10 mL of deionized water was added. The seeds were germinated for 4 days under dark conditions in a controlled (30 °C) growth chamber. Germination was considered when the coleoptiles were longer than 2 mm. Three replicates were carried out for each treatment. RNA Isolation. The seeds were artificially aged under high temperature and humidity (45 °C and 100% relative humidity) conditions for 2 days.29 Seeds that non-artificially aged (0 days) were used as a control (CK). We cut the pericarp and endosperm off the seeds with a knife carefully, and then the samples were immediately frozen in liquid nitrogen and stored at −80 °C. Total RNA was extracted using Trizol reagent (Invitrogen, Carlsbad, CA) following the procedure of the manufacturer. Small RNA Library Construction, Sequencing, and Sequencing Data Analysis. The total RNA was used for Solexa sequencing. Four libraries were constructed: two sets were from seeds nonartificially aged (CK), the other two sets were from seeds artificially aged for 2 days. According to Illumina’s TruSeq Small RNA Sample Preparation Guide, small RNA libraries were constructed using Illumina’s TruSeq Small RNA Preparation Kit (Illumina, San Diego, CA).30 Small RNAs were ligated to a 3′ RNA adapter and a 5′ RNA adapter. The purified cDNAs after reverse transcription and polymerase chain reaction (PCR) were sequenced by the Illumina Hiseq2000 (Illumina, San Diego, CA). Raw sequencing reads were obtained using Illumina’s Sequencing Control Studio software, version 2.8 (SCS, version 2.8) (Illumina, San Diego, CA). After quality control using a common pipeline filter, clean reads were directly used for further bioinformatics analysis with ACGT101-miR (LC Sciences, Houston, TX).31 Then, unique sequences (18−25 nucleotides) were mapped to specific species precursors in the miRNA database (miRBase 20 released) in maize (http://www.mirbase.org) by BLAST search to identify known miRNAs and novel 3p- and 5pderived miRNAs. New candidate miRNAs were identified by extracting 250 nucleotides of the sequence flanking genome sequence of unique small RNAs using MIREAP (http://sourceforge.net/ projects/mireap/), followed by the prediction of secondary structures by the Mfold program. The new candidate miRNA were identified on the basis of the criteria as described.3 Degradome Library Construction. The degradome cDNA library was constructed on the basis of the method described previously32 using sliced ends of polyadenylated transcripts from sweet corn seeds. Identification of the potentially sliced targets of the known and novel miRNA identified by high-throughput sequencing and degradome analysis were processed using the publicly available CleaveLand 3.0 software package and the ACGT301-DGE, version 1.0 program (LC Sciences, Houston, TX).33 Quantitative Real-Time (qRT)-PCR. Total RNA was extracted from the sweet corn seeds artificially aged for 0 and 2 days with Trizol reagent (Invitrogen) following the instructions of the manufacturer. These samples were treated under the same conditions as those for miRNA and degradome sequencing. The total RNA (1 μg) was reverse-transcribed using miRNA-specific stem-loop primers. Next, qRT-PCR was performed using Rotor-Gene Q (QIAGEN). The primers used are listed in Table S11 of the Supporting Information. The relative expression ratio was calculated using the 2−ΔΔCt method with 5S rRNA as the reference gene. All of the gene expression data were obtained from three individual biological replicates and processed according to strict statistical methods.

Figure 1. Germination percentage of Yuetian 13 under artificial aging treatment. AG0 means non-artificially aged; AG1 means artificially aged for 1 day; AG2 means artificially aged for 2 days; AG3 means artificially aged for 3 days; and AG4 means artificially aged for 4 days.

Overview over Deep-Sequencing Results. To study the possible miRNAs related with sweet corn seed vigor, we profiled miRNA accumulation during seed artificial aging. We constructed four maize small RNA libraries using mixed RNAs obtained from seeds non-artificially aged (CK) and artificially aged for 2 days. Deep sequencing generated a total number of 9 218 636 and 7 784 067 raw reads from libraries of seeds nonartificially aged named AG0a and AG0b and 7 203 021 and 8 043 385 raw reads from libraries of seeds artificially aged for 2 days named AG2a and AG2b. After removing sequences that 3′ adapter was not found and reads with lengths of 25 nucleotides, junk reads, there were 6 264 835, 5 943 490, 5 107 877, and 5 791 540 mappable reads obtained for AG0a, AG0b, AG2a, and AG2b libraries, respectively (see Table S1 of the Supporting Information). Most of these miRNAs were 20− 24 nucleotides. The length distribution of these small RNA sequences showed that the most abundant sequences were 24 nucleotides long, followed by 22 nucleotides long and 21 nucleotides long (Figure 2). This was consistent with most plant mature small RNAs.34−36 The mappable reads were then searched against the Rfam (http://rfam.janelia.org), mRNA (ftp.jgi-psf.org/pub/compgen/phytozome/v9.0/Zmays/ annotation/), and repeat (http://www.girinst.org/repbase) databases. Finally, after excluding sequences that aligned to those databases, 5 037 856, 4 796 100, 4 269 398, and 4 951 734 sequences remained in the AG0a, AG0b, AG2a, and AG2b libraries, respectively (see Tables S2 and S3 of the Supporting Information). Identification of Known miRNAs in Seeds of Sweet Corn. To identify the miRNAs in our data set, sRNA sequences identified by deep sequencing were Blastn-searched against the currently known mature plant miRNAs in the miRNA database miRBase. There are currently 172 pre-miRNAs listed that B

DOI: 10.1021/acs.jafc.5b00522 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

Article

Journal of Agricultural and Food Chemistry

differential expression analysis. With the selection of p value < 0.05, 27 miRNAs were identified to be differentially expressed between the four libraries; 17 miRNAs were upregulated, and 10 miRNAs were downregulated (Table 1). Of the 15 Z. maysspecific miRNAs, 9 miRNAs were upregulated and 6 miRNAs were downregulated. Zma-miR319a-3p_R+1, the miRNA that had the highest expression abundance in the four libraries, was upregulated when the sweet corn seed vigor was down. A Brachypodium distachyon-specific miRNA, bdi-miR159-3p, was downregulated when the sweet corn seed vigor was down. A total of 11 candidate miRNAs were differentially expressed between the four libraries: 8 were upregulated, and 3 were downregulated. To validate conserved miRNAs identified and novel miRNAs predicted, we selected 10 differential expressed miRNAs for stem-loop qRT-PCR. The expression trends of these miRNAs were consistent with the high-throughout sequencing results. Figure 3 showed that the 10 miRNA expression profiles by qRT-PCR were consistent with those by deep sequencing, indicating the reliability of these methods. Targets of miRNAs in Seeds of Sweet Corn. To understand the function of the novel miRNAs from seeds of sweet corn, targets of novel miRNAs were predicted using recently developed degradome sequencing technology. In total, 13 621 118 (99.47%) mappable reads were obtained, including 3 986 470 unique mappable reads, while 7 343 113 (99.46%) transcript mapped reads, including 2 282 963 unique transcript mappable reads, were obtained (see Table S9-1 of the Supporting Information). The target transcripts were sorted into five categories according to the relative abundance of the tags at the target mRNA sites. Briefly, category “0” is defined as >1 raw read at the position, with abundance at a position equal to the maximum on the transcript and with only one maximum on the transcript. In category “1”, the expected cleavage signature was equal to the maximum on the transcript and more than one maximum position on the transcript. For category “2”, abundance at the position was less than the maximum but higher than the median for the transcript. The abundance at the position equal to or less than the median for the transcript was grouped into category “3”, and category “4” showed only one raw read at the position. Figure 4 showed the typical five categories of the target transcripts. A total of 1142 targets that were potentially cleaved by 131 miRNAs were identified (see Table S9-2 of the Supporting Information). Meanwhile, 26 genes were identified to be targeted by 9 differentially expressed miRNAs in both degradome analysis and bioinformatics analysis (see Table S9-3 of the Supporting Information). Notably, the targets of miR319, miR156, miR160, and miR167 were identified as transcription factors, such as MYB, TCP, SBP, and ARF, respectively, which have been experimentally validated by the previous studies.19−21 This demonstrated the high reliability of target identification in the present research. Gene Ontology (GO) Analysis and Pathway Analysis of Targets. To gain a better understanding of the functional roles of the miRNAs, we did GO and KEGG pathway analysis on putative targets. The targets were annotated using the GO annotations available from B73 RefGen_v2. GO analysis is commonly used to describe the function of genes and gene products, while the KEGG analysis is used to provide the pathway of annotated targets. Figure 5 showed the GO functional classification of novel miRNA targets in the sweet corn seeds. Novel targets covered a broad range of functional categories. Obviously, targets of ATP-binding, regulation of

Figure 2. Length distribution of unique sRNAs of sequ-seqs type in four libraries.

correspond to 321 mature miRNAs of Z. mays in miRBase. In the present research, there were 146 pre-miRNAs corresponding to 130 mature miRNAs detected, 33 new mature 5′- or 3′miRNAs corresponding to Z. mays pre-miRNAs detected for the first time, and 93 mature miRNAs corresponding to Z. mays pre-miRNAs detected confirming miRNA sequences in miRBase but different sequences in our study (see Table S4 of the Supporting Information). Both libraries identified 27 conversed miRNA families, which were 3 conversed miRNA families more than that in dry and imbibed maize seeds, including miR395, miR2118, and miR2275.4 There were 12 novel pre-miRNAs corresponding to 18 mature miRNAs originating from other species but can be mapped to the Z. mays genome. Among the 18 mature miRNAs, 13 new mature 5′- or 3′-miRNAs were detected in Z. mays for the first time and 4 mature miRNAs were detected confirming miRNA sequences in miRBase but different sequences in our study (see Table S5 of the Supporting Information). There were 31 novel miRNAs originating from 31 pre-miRNAs that could not be mapped to the Z. mays genome; these were mapped to other plant species genomes, and the extended sequences at the mapped positions of the genome potentially form hairpins (see Table S6 of the Supporting Information). Another 4 novel miRNAs identified to originate from pre-miRNAs that could not be mapped to the Z. mays genome but were mapped to other plant genomes failed in hairpin structure prediction for extended sequences at the mapped positions (see Table S7 of the Supporting Information). Identification of New Candidate miRNAs in Seeds of Sweet Corn. Reads that cannot be mapped to the miRBase were used to query the Rfam, the mRNA database, and the repeat Repbase. After removal of mapped reads, the rest of the reads were predicted for potential stem-loop structure. In total, 141 pre-miRNAs corresponding to 137 unique miRNAs were first identified in our study (see Table S8 of the Supporting Information). The 137 candidate miRNAs were 18−24 nucleotides long, with 69.34% of them 24 nucleotides long. Most of the candidate miRNAs were expressed at a low level. However, 6 of the candidate miRNAs were expressed at a high level. Those miRNAs were PC-5p-142680_20, PC-5p6996_405, PC-3p-144850_17, PC-5p-608_3876, and PC-3p78961_35; they had more than 20 reads in four libraries. miRNAs That Related with Sweet Corn Seed Vigor. To identify miRNAs related with sweet corn seed vigor, the reads obtained from the high-throughout sequencing were used for C

DOI: 10.1021/acs.jafc.5b00522 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

miR_name

PC-5p-236947_9 zma-miR398a-3p zma-miR398a-3p PC-3p-260626_8 PC-3p-477790_3 PC-3p-618145_3 PC-5p-670045_3 zma-miR319a-3p_R+1 zma-miR319a-3p_R+1 zma-miR319a-3p_R+1 zma-miR319a-3p_R+1 PC-3p-582028_5 zma-MIR319b-p5 zma-MIR171m-p3 zma-MIR319d-p5 PC-3p-38366_70 PC-3p-38366_70 zma-miR156i-3p bdi-miR159−3p zma-miR156l-3p PC-3p-144850_17 PC-5p-213179_17 zma-miR168b-3p_R+1 zma-miR156d-3p_1ss7TG zma-miR156d-3p_1ss7TG zma-miR156d-3p_1ss7TG PC-3p-136373_22

index

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

ACTCGTTCCTTGTGTGCCACTGGC TGTGTTCTCAGGTCGCCCCCG TGTGTTCTCAGGTCGCCCCCG TTGTGAGGGGCTGCAAACAGC TTGATTGCACGCTTGCACCAGGT ATTCTCTGTCCACCGATCAGCTCT ATGGAGTGGATTGAGGGGGCTAGA TTGGACTGAAGGGTGCTCCCT TTGGACTGAAGGGTGCTCCCT TTGGACTGAAGGGTGCTCCCT TTGGACTGAAGGGTGCTCCCT AAATAAGTTTATTTTCGGCGGCCT AGCTGCCGACTCATTCACCCA CTTGGATTGAGCCGCGTCAAT AGCTGCCGACTCATTCACCCA ATCCGGAACAACCGAACAGGGCCT ATCCGGAACAACCGAACAGGGCCT GCTCACTGCTCTATCTGTCATC CTTGGATTGAAGGGAGCTCT GCTCACTGCTCTATCTGTCACC GCCTGCGACGTCGCGAGAA ACACCCCAACGTTGACTCCTCT CCCGCCTTGCATCAAGTGAAT GCTCACGTCTCTTTCTGTCAGC GCTCACGTCTCTTTCTGTCAGC GCTCACGTCTCTTTCTGTCAGC TTTCGGGCTCCCAAACTAGCC

miR_seq 1 65 65 3 0 0 0 2314.08 2314.08 2314.08 2314.08 2 31 1 31 7 7 9.50 14 116.50 49 9 739.50 2 2 2 13

AG0a (raw) 1 62.50 62.50 1.50 1 1 1 2210 2210 2210 2210 1 26.50 1.50 26.50 7 7 8 10.50 90 36 9 562 1.67 1.67 1.67 10

AG0b (raw) 3 142.50 142.50 7.50 5 5 5 3095.50 3095.50 3095.50 3095.50 4 107.50 6 107.50 11 11 0.50 5 40.50 7 2 54 0.67 0.67 0.67 8

AG2a (raw)

Table 1. Profiles of the Differentially Expressed miRNAs Related with Sweet Corn Seed Vigor

4 192.50 192.50 8 5 5 5 3988.50 3988.50 3988.50 3988.50 6 144.50 6 145.50 14 14 1 5 37 0 4 61.50 0.67 0.67 0.67 9

AG2b (raw) 0.91 59.19 59.19 2.73 0 0 0 2107.33 2107.33 2107.33 2107.33 1.82 28.23 0.91 28.23 6.37 6.37 8.65 12.75 106.09 44.62 8.20 673.43 1.82 1.82 1.82 11.84

AG0a (norm) 1.09 68.03 68.03 1.63 1.09 1.09 1.09 2405.42 2405.42 2405.42 2405.42 1.09 28.84 1.63 28.84 7.62 7.62 8.71 11.43 97.96 39.18 9.80 611.69 1.81 1.81 1.81 10.88

AG0b (norm) 3.38 160.45 160.45 8.44 5.63 5.63 5.63 3485.43 3485.43 3485.43 3485.43 4.50 121.04 6.76 121.04 12.39 12.39 0.56 5.63 45.60 7.88 2.25 60.80 0.75 0.75 0.75 9.01

AG2a (norm) 3.66 176.20 176.20 7.32 4.58 4.58 4.58 3650.83 3650.83 3650.83 3650.83 5.49 132.27 5.49 133.18 12.81 12.81 0.92 4.58 33.87 0 3.66 56.29 0.61 0.61 0.61 8.24

AG2b (norm) 1.82 1.40 1.40 1.85 3.23 3.16 3.16 0.66 0.66 0.66 0.66 1.78 2.15 2.27 2.16 0.85 0.85 −3.55 −1.24 −1.36 −3.41 −1.61 −3.46 −1.42 −1.42 −1.42 −0.40

log2(fold_change)

Journal of Agricultural and Food Chemistry Article

D

DOI: 10.1021/acs.jafc.5b00522 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

Article

Journal of Agricultural and Food Chemistry

were only 13 miRNAs found to be involved in pathways, including mitogen-activated protein kinase (MAPK) signaling pathway, endocytosis, spliceosome, and others. Function of Differentially Expressed miRNAs. Although 27 miRNAs were differentially expressed related with sweet corn seed vigor, only 26 genes targeted by 9 miRNAs were identified in both degradome analysis and bioinformatics analysis, including 10 MYB proteins, 2 TCP family transcription factors, 1 membrane-anchored ubiquitin-fold protein 6 precursor (MUB6), 1 heat stable protein, 3 peroxidase superfamily proteins, 1 selenium-binding protein (SBP), and 6 unknown transcription factors. The function of the MYB protein has been previously identified. MYB proteins are key factors in regulatory networks controlling development, metabolism, and responses to biotic and abiotic stresses.37 In Arabidopsis and rice (Oryza sativa), MYB33 and MYB101 targeted by miR159 were ABA-positive regulators, which play a vital role in seed germination. The role of MYB protein in seed vigor is still unknown. In our study, MYB domain proteins 65/ 33/101 were targeted by bdi-miR159-3p and zma-miR319a3p_R+1. This suggests that MYB protein may participate in seed vigor regulation through chromatin binding. Loss of seed vigor brings abnormal physiological and biochemical activities in cells, such as free radical lipid peroxidation, chromosome aberration, and non-enzymatic glycosylation (NEG). Peroxidase (POD) is a scavenger of reactive oxygen, which can maintain the stability and integrity of the membrane of the cells. POD superfamily proteins play roles in the oxidation− reduction process and response to oxidative stress, which protect seeds from oxidative damage during artificial aging. This suggests that PC-5p-213179_17 may prevent sweet corn seeds from free-radical lipid peroxidation by participating in the

Figure 3. Detection of selected miRNA expression in AG0 and AG2 sweet corn seeds using qRT-PCR. AG0 means non-artificially aged, while AG2 means artificially aged for 2 days. The 5S rRNA was chosen as an endogenous control. The results were obtained from three biological replicates, and the error bars indicate the standard error of the mean.

transcription, DNA-dependent, and nucleus- and membranerelated genes were specifically enriched. This suggested that those genes may play an important role in sweet corn seeds. The pathways of all the miRNAs and targets involved were listed (see Tables S10-1 and S10-2 of the Supporting Information). There were 157 pathways that targets involved, while purine metabolism, cell cycle, and phenylalanine metabolism pathways owned most targets. However, there

Figure 4. Typical categories of the target transcript according to the relative abundance of the tags at the target mRNA sites. E

DOI: 10.1021/acs.jafc.5b00522 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

Article

Journal of Agricultural and Food Chemistry

Figure 5. GO functional classification of novel miRNA targets in the sweet corn seeds.

Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jafc.5b00522.

peroxidase activity, oxidation−reduction process, and response to oxidative stress. The function of MUB6 in seed vigor is still unclear, but researchers have proven that MUB6 expressed in seeds associated with plant structure in Arabidopsis. Seed aging will destroy the integrity of the membrane. Here, we report a MUB6 in sweet corn seeds regulated by PC-3p-477790_3, which may be a new direction in the research of seed vigor. Heat-stable protein also targeted by PC-3p-477790_3 may be involved in ameliorating the effects of artificial aging on seed vigor by repairing protein damage. Therefore, we propose that miRNAs participate in seed vigor of sweet corn by regulating some genes controlling the process.

■

■

AUTHOR INFORMATION

Corresponding Author

*Telephone: 86-571-86914510. E-mail: [email protected]. Author Contributions †

Shumin Gong and Yanfei Ding are co-first authors.

Funding

This study was supported through funding from the National Natural Science Foundation of China (31401299, 31170251, and 31470368) and the Zhejiang Provincial Natural Science Foundation of China (LZ14C020001 and LY13C020002).

ASSOCIATED CONTENT

Notes

S Supporting Information *

The authors declare no competing financial interest.

■

Distribution of counts of sequ-seqs during standard filtering in four libraries (Table S1), distribution of counts of mappable reads in four libraries (Table S2), length distribution of mappable counts and unique sRNAs of sequ-seqs type in four libraries (Table S3), profile of the known microRNAs in sweet corn seeds referred to Z. mays (miRbase20.0) (Table S4), profile of novel microRNAs originating from other plant premiRNAs (miRbase 20.0) that can be mapped to the Z. mays genome (Table S5), profile of novel microRNAs originating from pre-miRNAs that cannot be mapped to the Z. mays genome but novel microRNAs mapped to the genome and extended sequences at the mapped positions of the genome potentially forming hairpins (Table S6), profile of novel microRNAs originating from pre-miRNAs that cannot be mapped to the Z. mays genome but novel microRNAs mapped to the genome and extended sequences at the mapped positions of the genome not potentially forming hairpins (Table S7), profile of candidate microRNAs originating from predicted RNA hairpins (Table S8), distribution of counts of mappable reads in the degradome library (Table S9-1), profile of all miRNA targets by degradome sequencing (Table S9-2), profiles of differentially expressed miRNAs targets by degradome sequencing and bioinformatics analysis (Table S93), KEGG pathway statistics by degradome sequencing (Table S10-1), KEGG-miRNA gene PLUS expression by degradome sequencing (Table S10-2), and stem-loop primer and qRTPCR primer of 10 miRNAs (Table S11) (XLS). The

REFERENCES

(1) Bennetzen, J. L.; Hake, S. C. Handbook of Maize: Its Biology; Springer: New York, 2009. (2) Gupta, M. L.; George, D. L.; Parwata, I. G. M. A. Effect of harvest time and drying on supersweet corn seed quality. Seed Sci. Technol. 2005, 33, 167−176. (3) Zhang, L. F.; Chia, J. M.; Kumari, S.; Stein, J. C.; Liu, Z. J.; Narechania, A.; Maher, C. A.; Guill, K.; McMullen, M. D.; Ware, D. A genome-wide characterization of microRNA genes in maize. PLoS Genet. 2009, 5, No. e1000716. (4) Li, D. T.; Wang, L. W.; Liu, X.; Cui, D. Z.; Chen, T. T.; Zhang, H.; Jiang, C.; Xu, C. Y.; Li, P.; Li, S.; Zhao, L.; Chen, H. B. Deep sequencing of maize small RNAs reveals a diverse set of microRNA in dry and imbibed seeds. PLoS One 2013, 8, No. e55107. (5) Kang, M. M.; Zhao, Q.; Zhu, D. Y.; Yu, J. J. Characterization of microRNAs expression during maize seed development. BMC Genomics 2012, 13, 360. (6) Bartel, D. P. MicroRNAs: Genomics, biogenesis, mechanism, and function. Cell 2004, 116, 281−297. (7) Zamore, P. D.; Haley, B. Ribo-gnome: The big world of small RNAs. Science 2005, 309, 1519−1524. (8) Park, W.; Li, J.; Song, R.; Messing, J.; Chen, X. M. CARPEL FACTORY, a Dicer homolog, and HEN1, a novel protein, act in microRNA metabolism in Arabidopsis thaliana. Curr. Biol. 2002, 12, 1484−1495. (9) Jia, L.; Zhang, D. Y.; Qi, X. W.; Ma, B.; Xiang, Z. H.; He, N. J. Identification of the conserved and novel miRNAs in mulberry by high-throughput sequencing. PLoS One 2014, 9, No. e104409.

F

DOI: 10.1021/acs.jafc.5b00522 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

Article

Journal of Agricultural and Food Chemistry (10) Reinhart, B.; Weinstein, E.; Rhoades, M.; Bartel, B.; Bartel, D. MicroRNAs in plants. Genes Dev. 2002, 16, 1616−1626. (11) Xie, Z. X.; Allen, E.; Fahlgren, N.; Calamar, A.; Givan, S.; Carrington, J. Expression of Arabidopsis MIRNA genes. Plant Physiol. 2005, 138, 2145−2154. (12) Yu, B.; Yang, Z. Y.; Li, J. J.; Minakhina, S.; Yang, M.; Padgett, R.; Steward, R.; Chen, X. M. Methylation as a crucial step in plant microRNA biogenesis. Science 2005, 307, 932−935. (13) Bollman, K.; Aukerman, M.; Park, M.; Hunter, C.; Berardini, T.; Poethig, R. HASTY, the Arabidopsis ortholog of exportin 5/MSN5, regulates phase change and morphogenesis. Development 2003, 130, 1493−1504. (14) Park, M.; Wu, G.; Gonzalez-Sulser, A.; Vaucheret, H.; Poethig, R. Nuclear processing and export of microRNAs in Arabidopsis. Proc. Natl. Acad. Sci. U. S. A. 2005, 102, 3691−3696. (15) Baumberger, N.; Baulcombe, D. Arabidopsis ARGONAUTE1 is an RNA slicer that selectively recruits microRNAs and short interfering RNAs. Proc. Natl. Acad. Sci. U. S. A. 2005, 102, 11928− 11933. (16) Bonnet, E.; Van de Peer, Y.; Rouze, P. The small RNA world of plants. New Phytol. 2006, 171, 451−468. (17) Khan, Y.; Yadav, A.; Bonthala, V. S.; Muthamilarasan, M.; Yadav, C. B.; Prasad, M. Comprehensive genome-wide identification and expression profiling of foxtail millet [Setaria italica (L.)] miRNAs in response to abiotic stress and development of miRNA database. Plant Cell, Tissue Organ Cult. 2014, 118, 279−292. (18) Aukerman, M. J.; Sakai, H. Regulation of flowering time and floral organ identity by a microRNA and its APETALA2-like target genes. Plant Cell 2003, 15, 2730−2741. (19) Mallory, A. C.; Vaucheret, H. Functions of microRNAs and related small RNAs in plants. Nat. Genet. 2006, 38, S31−S36. (20) Voinnet, O. Origin, biogenesis, and activity of plant microRNAs. Cell 2009, 136, 669−687. (21) Carrington, J. C.; Ambros, V. Role of microRNAs in plant and animal development. Science 2003, 301, 336−338. (22) Sunkar, R. MicroRNAs with macro-effects on plant stress responses. Semin. Cell Dev. Biol. 2010, 21, 805−811. (23) Khraiwesh, B.; Zhu, J. K.; Zhu, J. H. Role of miRNAs and siRNAs in biotic and abiotic stress responses of plants. Biochim. Biophys. Acta, Gene Regul. Mech. 2012, 1819, 137−148. (24) Nodine, M.; Bartel, D. MicroRNAs prevent precocious gene expression and enable pattern formation during plant embryogenesis. Genes Dev. 2010, 24, 2678−2692. (25) Reyes, J. L.; Chua, N. H. ABA induction of miR159 controls transcript levels of two MYB factors during Arabidopsis seed germination. Plant J. 2007, 49, 592−606. (26) Gutierrez, L.; Bussell, J. D.; Pacurar, D. I.; Schwambach, J.; Pacurar, M.; Bellini, C. Phenotypic plasticity of adventitious rooting in Arabidopsis is controlled by complex regulation of AUXIN RESPONSE FACTOR transcripts and microRNA abundance. Plant Cell 2009, 21, 3119−3132. (27) Yang, J. H.; Han, S. J.; Yoon, E. K.; Lee, W. S. Evidence of an auxin signal pathway, microRNA167-ARF8-GH3, and its response to exogenous auxin in cultured rice cells. Nucleic Acids Res. 2006, 34, 1892−1899. (28) Cheng, H. L. Study on correlation of miRNA and rice (Oryza sativa L.) seed vigor. Master’s Thesis, Hunan Normal University, Changsha, Hunan, China, 2011 (in Chinese). (29) Association of Official Seed Analysts (AOSA). Seed Vigor Testing Handbook; AOSA: Washington, D.C., 1983; Contribution Number 32 to the Handbook of Seed Testing. (30) Tang, Z. H.; Zhang, L. P.; Xu, C. G.; Yuan, S. H.; Zhang, F. T.; Zheng, Y. L.; Zhao, C. P. Uncovering small RNA-mediated responses to cold stress in a wheat thermosensitive genic male-sterile line by deep sequencing. Plant Physiol. 2012, 159, 721−738. (31) Li, Y.; Zhang, Q. Q.; Zhang, J. G.; Wu, L.; Qi, Y. J.; Zhou, J. M. Identification of microRNAs involved in pathogen-associated molecular pattern-triggered plant innate immunity. Plant Physiol. 2010, 152, 2222−2231.

(32) Addo-Quaye, C.; Eshoo, T. W.; Bartel, D. P.; Axtell, M. J. Endogenous siRNA and miRNA targets identified by sequencing of the Arabidopsis degradome. Curr. Biol. 2008, 18, 758−762. (33) Addo-Quaye, C.; Snyder, J. A.; Park, Y. B.; Li, Y. F.; Sunkar, R.; Axtell, M. J. Sliced microRNA targets and precise loop-first processing of MIR319 hairpins revealed by analysis of the Physcomitrella patens degradome. RNA 2009, 15, 2112−2121. (34) Jiao, Y. P.; Song, W. B.; Zhang, M.; Lai, J. S. Identification of novel maize miRNAs by measuring the precision of precursor processing. BMC Plant Biol. 2011, 11, 141. (35) Szittya, G.; Moxon, S.; Santos, D. M.; Jing, R.; Fevereiro, M. P. S.; Moulton, V.; Dalmay, T. High-throughput sequencing of Medicago truncatula short RNAs identifies eight new miRNA families. BMC Genomics 2008, 9, 593. (36) Rajagopalan, R.; Vaucheret, H.; Trejo, J.; Bartel, D. P. A diverse and evolutionarily fluid set of microRNAs in Arabidopsis thaliana. Genes Dev. 2006, 20, 3407−3425. (37) Dubos, C.; Stracke, R.; Grotewold, E.; Weisshaar, B.; Martin, C.; Lepiniec, L. MYB transcription factors in Arabidopsis. Trends Plant Sci. 2010, 15, 573−581.

G

DOI: 10.1021/acs.jafc.5b00522 J. Agric. Food Chem. XXXX, XXX, XXX−XXX