OrangerV1, Pmc, Corpus, bibRecord, 000F43

Comparative Analysis of miRNAs and Their Target Transcripts between a Spontaneous Late-Ripening Sweet Orange Mutant and Its Wild-Type Using Small RNA and Degradome Sequencing

Identifieur interne : 000F43 ( Pmc/Corpus ); précédent : 000F42; suivant : 000F44

Comparative Analysis of miRNAs and Their Target Transcripts between a Spontaneous Late-Ripening Sweet Orange Mutant and Its Wild-Type Using Small RNA and Degradome Sequencing

Auteurs : Juxun Wu ; Saisai Zheng ; Guizhi Feng ; Hualin Yi

Source :

Frontiers in Plant Science [ 1664-462X ] ; 2016.

RBID : PMC:5030777

Abstract

Fruit ripening in citrus is not well-understood at the molecular level. Knowledge of the regulatory mechanism of citrus fruit ripening at the post-transcriptional level in particular is lacking. Here, we comparatively analyzed the miRNAs and their target genes in a spontaneous late-ripening mutant, “Fengwan” sweet orange (MT) (Citrus sinensis L. Osbeck), and its wild-type counterpart (“Fengjie 72-1,” WT). Using high-throughput sequencing of small RNAs and RNA degradome tags, we identified 107 known and 21 novel miRNAs, as well as 225 target genes. A total of 24 miRNAs (16 known miRNAs and 8 novel miRNAs) were shown to be differentially expressed between MT and WT. The expression pattern of several key miRNAs and their target genes during citrus fruit development and ripening stages was examined. Csi-miR156k, csi-miR159, and csi-miR166d suppressed specific transcription factors (GAMYBs, SPLs, and ATHBs) that are supposed to be important regulators involved in citrus fruit development and ripening. In the present study, miRNA-mediated silencing of target genes was found under complicated and sensitive regulation in citrus fruit. The identification of miRNAs and their target genes provide new clues for future investigation of mechanisms that regulate citrus fruit ripening.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5030777

DOI: 10.3389/fpls.2016.01416
PubMed: 27708662
PubMed Central: 5030777

Links to Exploration step

PMC:5030777

Le document en format XML

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<author><name sortKey="Wu, Juxun" sort="Wu, Juxun" uniqKey="Wu J" first="Juxun" last="Wu">Juxun Wu</name>
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<author><name sortKey="Zheng, Saisai" sort="Zheng, Saisai" uniqKey="Zheng S" first="Saisai" last="Zheng">Saisai Zheng</name>
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<author><name sortKey="Feng, Guizhi" sort="Feng, Guizhi" uniqKey="Feng G" first="Guizhi" last="Feng">Guizhi Feng</name>
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<author><name sortKey="Yi, Hualin" sort="Yi, Hualin" uniqKey="Yi H" first="Hualin" last="Yi">Hualin Yi</name>
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<author><name sortKey="Feng, Guizhi" sort="Feng, Guizhi" uniqKey="Feng G" first="Guizhi" last="Feng">Guizhi Feng</name>
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<front><div type="abstract" xml:lang="en"><p>Fruit ripening in citrus is not well-understood at the molecular level. Knowledge of the regulatory mechanism of citrus fruit ripening at the post-transcriptional level in particular is lacking. Here, we comparatively analyzed the miRNAs and their target genes in a spontaneous late-ripening mutant, “Fengwan” sweet orange (MT) (<italic>Citrus sinensis</italic>
 L. Osbeck), and its wild-type counterpart (“Fengjie 72-1,” WT). Using high-throughput sequencing of small RNAs and RNA degradome tags, we identified 107 known and 21 novel miRNAs, as well as 225 target genes. A total of 24 miRNAs (16 known miRNAs and 8 novel miRNAs) were shown to be differentially expressed between MT and WT. The expression pattern of several key miRNAs and their target genes during citrus fruit development and ripening stages was examined. Csi-miR156k, csi-miR159, and csi-miR166d suppressed specific transcription factors (<italic>GAMYBs, SPLs</italic>
, and <italic>ATHBs</italic>
) that are supposed to be important regulators involved in citrus fruit development and ripening. In the present study, miRNA-mediated silencing of target genes was found under complicated and sensitive regulation in citrus fruit. The identification of miRNAs and their target genes provide new clues for future investigation of mechanisms that regulate citrus fruit ripening.</p>
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</author>
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</author>
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</author>
<author><name sortKey="Fu, D Q" uniqKey="Fu D">D. Q. Fu</name>
</author>
<author><name sortKey="Zhu, Y" uniqKey="Zhu Y">Y. Zhu</name>
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<author><name sortKey="Chi, L H" uniqKey="Chi L">L. H. Chi</name>
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</listBibl>
</div1>
</back>
</TEI>
<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
  <front><journal-meta><journal-id journal-id-type="nlm-ta">Front Plant Sci</journal-id>
<journal-id journal-id-type="iso-abbrev">Front Plant Sci</journal-id>
<journal-id journal-id-type="publisher-id">Front. Plant Sci.</journal-id>
<journal-title-group><journal-title>Frontiers in Plant Science</journal-title>
</journal-title-group>
<issn pub-type="epub">1664-462X</issn>
<publisher><publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">27708662</article-id>
<article-id pub-id-type="pmc">5030777</article-id>
<article-id pub-id-type="doi">10.3389/fpls.2016.01416</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Plant Science</subject>
<subj-group><subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group><article-title>Comparative Analysis of miRNAs and Their Target Transcripts between a Spontaneous Late-Ripening Sweet Orange Mutant and Its Wild-Type Using Small RNA and Degradome Sequencing</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Wu</surname>
<given-names>Juxun</given-names>
</name>
<uri xlink:type="simple" xlink:href="http://loop.frontiersin.org/people/218665/overview"></uri>
</contrib>
<contrib contrib-type="author"><name><surname>Zheng</surname>
<given-names>Saisai</given-names>
</name>
</contrib>
<contrib contrib-type="author"><name><surname>Feng</surname>
<given-names>Guizhi</given-names>
</name>
</contrib>
<contrib contrib-type="author"><name><surname>Yi</surname>
<given-names>Hualin</given-names>
</name>
<xref ref-type="author-notes" rid="fn001"><sup>*</sup>
</xref>
</contrib>
</contrib-group>
<aff><institution>Key Laboratory of Horticultural Plant Biology (Ministry of Education), College of Horticulture and Forestry Science, Huazhong Agricultural University</institution>
<country>Wuhan, China</country>
</aff>
<author-notes><fn fn-type="edited-by"><p>Edited by: Claudio Bonghi, University of Padua, Italy</p>
</fn>
<fn fn-type="edited-by"><p>Reviewed by: Andrea Schubert, University of Turin, Italy; Benzhong Zhu, China Agricultural University, China</p>
</fn>
<corresp id="fn001">*Correspondence: Hualin Yi <email xlink:type="simple">yihualin@mail.hzau.edu.cn</email>
</corresp>
<fn fn-type="other" id="fn002"><p>This article was submitted to Crop Science and Horticulture, a section of the journal Frontiers in Plant Science</p>
</fn>
</author-notes>
<pub-date pub-type="epub"><day>21</day>
<month>9</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="collection"><year>2016</year>
</pub-date>
<volume>7</volume>
<elocation-id>1416</elocation-id>
<history><date date-type="received"><day>03</day>
<month>6</month>
<year>2016</year>
</date>
<date date-type="accepted"><day>06</day>
<month>9</month>
<year>2016</year>
</date>
</history>
<permissions><copyright-statement>Copyright © 2016 Wu, Zheng, Feng and Yi.</copyright-statement>
<copyright-year>2016</copyright-year>
<copyright-holder>Wu, Zheng, Feng and Yi</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p>
</license>
</permissions>
<abstract><p>Fruit ripening in citrus is not well-understood at the molecular level. Knowledge of the regulatory mechanism of citrus fruit ripening at the post-transcriptional level in particular is lacking. Here, we comparatively analyzed the miRNAs and their target genes in a spontaneous late-ripening mutant, “Fengwan” sweet orange (MT) (<italic>Citrus sinensis</italic>
 L. Osbeck), and its wild-type counterpart (“Fengjie 72-1,” WT). Using high-throughput sequencing of small RNAs and RNA degradome tags, we identified 107 known and 21 novel miRNAs, as well as 225 target genes. A total of 24 miRNAs (16 known miRNAs and 8 novel miRNAs) were shown to be differentially expressed between MT and WT. The expression pattern of several key miRNAs and their target genes during citrus fruit development and ripening stages was examined. Csi-miR156k, csi-miR159, and csi-miR166d suppressed specific transcription factors (<italic>GAMYBs, SPLs</italic>
, and <italic>ATHBs</italic>
) that are supposed to be important regulators involved in citrus fruit development and ripening. In the present study, miRNA-mediated silencing of target genes was found under complicated and sensitive regulation in citrus fruit. The identification of miRNAs and their target genes provide new clues for future investigation of mechanisms that regulate citrus fruit ripening.</p>
</abstract>
<kwd-group><kwd>citrus</kwd>
<kwd>fruit ripening</kwd>
<kwd>miRNA</kwd>
<kwd>small RNA sequencing</kwd>
<kwd>degradome sequencing</kwd>
</kwd-group>
<counts><fig-count count="7"></fig-count>
<table-count count="4"></table-count>
<equation-count count="0"></equation-count>
<ref-count count="78"></ref-count>
<page-count count="17"></page-count>
<word-count count="10617"></word-count>
</counts>
</article-meta>
</front>
<body><sec sec-type="intro" id="s1"><title>Introduction</title>
<p>Citrus is one of the most widely grown fruit tree crops in the world, with a high economic value. The maturity time of citrus fruits varies among different varieties. The ripening stage is accompanied by the synthesis of numerous proteins and the transcription of many genes. Carotenoids, sugars, and other soluble compounds accumulate; organic acid contents and chlorophyll are reduced; the cell wall is extensively modified; and the concentration of a number of volatiles increases (Katz et al., <xref rid="B28" ref-type="bibr">2011</xref>
; Yu et al., <xref rid="B72" ref-type="bibr">2012</xref>
). The elucidation of citrus fruit ripening regulatory pathways and networks is important for the improvement of citrus varieties.</p>
<p>MicroRNAs (miRNAs), which are typically 20–24 nucleotides (nt) in length, represent an important class of regulatory molecules found in plants and animals. Derived from stem loop hairpin primary miRNA transcripts (pri-miRNAs), miRNAs negatively regulate target genes through homology-directed cleavage or translation inhibition of mRNAs (Bartel, <xref rid="B6" ref-type="bibr">2004</xref>
; Mallory and Vaucheret, <xref rid="B37" ref-type="bibr">2006</xref>
). The cleavage of target genes by some 22 nt miRNAs, which are generated from perfect duplexes comprising a 22 nt miRNA and a 22 nt miRNA<sup>*</sup>
 by DCL2, can trigger the biogenesis of another class of sRNAs (Chen et al., <xref rid="B10" ref-type="bibr">2010</xref>
); an RNA-dependent RNA polymerase (RDR6) is recruited to convert an upstream or downstream cleaved fragment into double-stranded RNA that is subsequently cleaved by DICER-LIKE 4 into 21 nt sRNAs (Axtell et al., <xref rid="B4" ref-type="bibr">2006</xref>
; Allen and Howell, <xref rid="B3" ref-type="bibr">2010</xref>
). These sRNAs are named phased secondary small interfering RNAs (phasiRNA), some of which function in <italic>trans</italic>
 (<italic>trans</italic>
-acting siRNA, tasiRNA) or <italic>cis</italic>
 (Fei et al., <xref rid="B18" ref-type="bibr">2013</xref>
). In dicots, phasiRNAs have been found to be generated from large and conserved gene families and presumably to regulate large and conserved gene families, including those encoding nucleotide binding leucine-rich repeat proteins (NB-LRR genes), MYB transcription factors and pentatricopeptide repeat proteins (PPR genes; Fei et al., <xref rid="B18" ref-type="bibr">2013</xref>
; Xia et al., <xref rid="B61" ref-type="bibr">2015a</xref>
,<xref rid="B62" ref-type="bibr">b</xref>
).</p>
<p>miRNAs are important regulators in transcriptional and post-transcriptional silencing of genes in plant development (Debat and Ducasse, <xref rid="B17" ref-type="bibr">2014</xref>
). During the past decade, many miRNAs have been shown to play an important role in regulating development and ripening of fruit (Moxon et al., <xref rid="B43" ref-type="bibr">2008</xref>
; Zuo et al., <xref rid="B78" ref-type="bibr">2012</xref>
, <xref rid="B77" ref-type="bibr">2013</xref>
; Liu Y. et al., <xref rid="B35" ref-type="bibr">2014</xref>
; Bi et al., <xref rid="B7" ref-type="bibr">2015</xref>
; Chen et al., <xref rid="B12" ref-type="bibr">2015</xref>
). For example, over-expression of an <italic>AtMIR156b</italic>
 precursor generated abnormal flower and fruit morphologies in tomato (Silva et al., <xref rid="B49" ref-type="bibr">2014</xref>
). miR156 and miR172 coordinately regulate the transition from the juvenile to the adult phase of shoot development in plants, and miR156/157 and miR172 affect the ripening process of tomatoes by regulating the known ripening regulators <italic>CNR</italic>
 and <italic>SlAP2a</italic>
 (Chen et al., <xref rid="B12" ref-type="bibr">2015</xref>
). miR159 was shown to be involved in strawberry fruit ripening by regulating <italic>FaGAMYB</italic>
 which plays a central role in the transition of the strawberry receptacle from development to ripening (Csukasi et al., <xref rid="B14" ref-type="bibr">2012</xref>
; Vallarino et al., <xref rid="B55" ref-type="bibr">2015</xref>
).</p>
<p>In citrus, many miRNAs have been identified in different tissues, such as the leaf, flower, fruit, and callus (Xu et al., <xref rid="B68" ref-type="bibr">2010</xref>
; Zhang et al., <xref rid="B74" ref-type="bibr">2012</xref>
; Liu Y. et al., <xref rid="B35" ref-type="bibr">2014</xref>
; Wu et al., <xref rid="B60" ref-type="bibr">2015</xref>
). However, the miRNAs involved in the citrus fruit ripening process remain largely unknown. To gain a better understanding the role of miRNAs in citrus fruit ripening, small RNA and degradome sequencing were combined to identify miRNAs and their target genes in “Fengjie 72-1” navel orange and its spontaneous late-ripening mutant “Fengwan.” In our previous study (Wu et al., <xref rid="B59" ref-type="bibr">2014b</xref>
), the physiological changes (including sucrose, fructose, glucose, citric acid, quinic acid, malic acid, and abscisic acid) of fruits were different between “Fengjie 72-1” and “Fengwan” during fruit ripening. And the 170 DAF (days after flowering) stage was found to be the turning point at which the fruit of “Fengwan” diverged in its development from that of the wild type. In this study, the differentially expressed miRNAs between “Fengjie 72-1” and “Fengwan” were comparatively analyzed, and the role of miRNAs in the regulation of fruit ripening was also explored, contributing to the regulatory network of citrus fruit ripening.</p>
</sec>
<sec sec-type="materials and methods" id="s2"><title>Materials and methods</title>
<sec><title>Plant materials and illumina sequencing</title>
<p>The “Fengjie 72-1” navel orange (<italic>Citrus sinensis</italic>
 L. Osbeck) (WT) and its spontaneous late-ripening mutant “Fengwan” (MT) were cultivated in the same orchard located in Fengjie, Chongqing City, China (N31°03′35′, E109°35′25′). Fruit samples of WT and MT used in sRNAome and degradome sequencing were collected at 170 days after flowering (DAF) in 2013. The pulps of fruit samples (from six trees, three trees represented one biological replicate) of WT and MT were used for sRNAome sequencing, respectively. And the pulps of fruit samples from WT and MT were mixed as a pool for degradome sequencing. To detect the expression pattern of key miRNAs and target genes in fruit development, the fruit samples (from nine trees, three trees represented one biological replicate) were collected in 2015 at different developmental stages, including 50 DAF, 80 DAF, 120 DAF, 155 DAF, 180 DAF, and 220 DAF. Fruit samples were separated into peel and pulp after collection. Pulp was used in all analyses in this study. All samples were frozen in liquid nitrogen immediately after collection and kept at −80°C until use. Total RNA was extracted according to Xu et al. (<xref rid="B68" ref-type="bibr">2010</xref>
). Four small RNA libraries (MT_bio1, MT_bio2, WT_bio1, and WT_bio2) and one degradome library (uniform mixture of total RNA extracted from WT and MT) were constructed (Addo-Quaye et al., <xref rid="B2" ref-type="bibr">2008</xref>
; Hafner et al., <xref rid="B24" ref-type="bibr">2008</xref>
) and sequenced using an Illumina HiSeq™2000 at Beijing Genomics Institute (BGI; Shenzhen, China). The sequencing data were deposited at NCBI Gene Expression Omnibus (GEO) under the accession number <ext-link ext-link-type="NCBI:geo" xlink:href="GSE84191">GSE84191</ext-link>
.</p>
</sec>
<sec><title>Deep sequencing data analysis</title>
<p>The raw reads of small RNA libraries were pre-processed to remove low-quality reads, adaptors and contaminants to obtain clean reads. The clean reads were used to search GenBank and the Rfam 11.0 database (<ext-link ext-link-type="uri" xlink:href="http://rfam.sanger.ac.uk/">http://rfam.sanger.ac.uk/</ext-link>
) to annotate rRNAs, tRNAs, snRNAs, and snoRNAs. Reads matching repeat sequences, reads matching the exon and intron of genome sequence of <italic>C. sinensis</italic>
 (<ext-link ext-link-type="uri" xlink:href="http://citrus.hzau.edu.cn/">http://citrus.hzau.edu.cn/</ext-link>
; Xu et al., <xref rid="B67" ref-type="bibr">2013</xref>
) and reads matching the known miRNAs of all plants in the miRBase 20.0 database were also annotated. Based on the following priority rule: rRNAetc (in which Genbank > Rfam) > known miRNA > repeat > exon > intron (Calabrese et al., <xref rid="B8" ref-type="bibr">2007</xref>
), each small RNA was reannotated to obtain a unique category that is summarized in Table <xref ref-type="table" rid="T1">1</xref>
. The reads in the miRNA category were used to identify the known/conserved miRNAs based on the criteria of a previous publication (Meyers et al., <xref rid="B39" ref-type="bibr">2008</xref>
). The unannotated reads (unann category) were used to predict novel/unconserved miRNAs.</p>
<table-wrap id="T1" position="float"><label>Table 1</label>
<caption><p><bold>Data set summary of the sequencing of four (MT_bio1, MT_bio2, WT_bio1, and WT_bio2) small RNA and one degradome libraries</bold>
.</p>
</caption>
<table frame="hsides" rules="groups"><thead><tr><th rowspan="1" colspan="1"></th>
<th rowspan="1" colspan="1"></th>
<th valign="top" align="center" colspan="4" style="border-bottom: thin solid #000000;" rowspan="1"><bold>Unique reads</bold>
</th>
<th valign="top" align="center" colspan="4" style="border-bottom: thin solid #000000;" rowspan="1"><bold>Total reads</bold>
</th>
</tr>
<tr><th rowspan="1" colspan="1"></th>
<th valign="top" align="left" rowspan="1" colspan="1"><bold>Category</bold>
</th>
<th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>MT</bold>
</th>
<th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>WT</bold>
</th>
<th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>MT</bold>
</th>
<th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>WT</bold>
</th>
</tr>
<tr><th rowspan="1" colspan="1"></th>
<th rowspan="1" colspan="1"></th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>bio1</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>bio2</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>bio1</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>bio2</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>bio1</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>bio2</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>bio1</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>bio2</bold>
</th>
</tr>
</thead>
<tbody><tr><td valign="top" align="left" rowspan="1" colspan="1">Small RNA data</td>
<td valign="top" align="left" rowspan="1" colspan="1">Clean reads</td>
<td valign="top" align="center" rowspan="1" colspan="1">9,503,927</td>
<td valign="top" align="center" rowspan="1" colspan="1">8,755,251</td>
<td valign="top" align="center" rowspan="1" colspan="1">8,247,653</td>
<td valign="top" align="center" rowspan="1" colspan="1">7,105,581</td>
<td valign="top" align="center" rowspan="1" colspan="1">34,248,996</td>
<td valign="top" align="center" rowspan="1" colspan="1">31,898,997</td>
<td valign="top" align="center" rowspan="1" colspan="1">30,815,849</td>
<td valign="top" align="center" rowspan="1" colspan="1">23,377,054</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">Match genome</td>
<td valign="top" align="center" rowspan="1" colspan="1">5,220,473<break></break>
(54.93%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">4,813,413<break></break>
(54.98%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">4,432,917<break></break>
(53.75%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">3,640,580<break></break>
(51.24%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">20,653,753<break></break>
(60.3%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">18,950,663<break></break>
(59.41%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">16,724,660<break></break>
(54.27%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">11,820,593<break></break>
(50.56%)</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">miRNA</td>
<td valign="top" align="center" rowspan="1" colspan="1">39,836<break></break>
(0.42%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">38,104<break></break>
(0.44%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">37,762<break></break>
(0.46%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">34,132<break></break>
(0.48%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">967,988<break></break>
(2.83%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">907,006<break></break>
(2.84%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">730,625<break></break>
(2.37%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">615,550<break></break>
(2.63%)</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">Match GenBank/Rfam</td>
<td valign="top" align="center" rowspan="1" colspan="1">83,655<break></break>
(0.88%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">85,614<break></break>
(0.98%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">99,252<break></break>
(1.2%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">67,674<break></break>
(0.96%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">2,526,031<break></break>
(7.37%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">2,650,732<break></break>
(8.31%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">3,446,923<break></break>
(11.18%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">1,438,855<break></break>
(6.15%)</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">unann</td>
<td valign="top" align="center" rowspan="1" colspan="1">8,516,707<break></break>
(89.61%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">7,823,090<break></break>
(89.35%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">7,359,189<break></break>
(89.23%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">6,386,719<break></break>
(89.88%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">25,532,148<break></break>
(74.55%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">23,631,892<break></break>
(74.08%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">22,816,778<break></break>
(74.04%)</td>
<td valign="top" align="center" rowspan="1" colspan="1">18,379,048<break></break>
(78.62%)</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Degradome data</td>
<td valign="top" align="left" rowspan="1" colspan="1">Clean reads</td>
<td valign="top" align="center" colspan="4" rowspan="1">4,443,212</td>
<td valign="top" align="center" colspan="4" rowspan="1">33,690,279</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">Match genome</td>
<td valign="top" align="center" colspan="4" rowspan="1">3,210,105(72.25%)</td>
<td valign="top" align="center" colspan="4" rowspan="1">232,62,347 (69.05%)</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">Match cDNA_sense</td>
<td valign="top" align="center" colspan="4" rowspan="1">3,045,152 (68.53%)</td>
<td valign="top" align="center" colspan="4" rowspan="1">20,441,974 (60.68%)</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">Match cDNA_antisense</td>
<td valign="top" align="center" colspan="4" rowspan="1">34,079 (0.77%)</td>
<td valign="top" align="center" colspan="4" rowspan="1">230,844 (0.69%)</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">Match GenBank/Rfam</td>
<td valign="top" align="center" colspan="4" rowspan="1">13,558 (0.3%)</td>
<td valign="top" align="center" colspan="4" rowspan="1">199,304 (0.59%)</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">polyN</td>
<td valign="top" align="center" colspan="4" rowspan="1">10,019 (0.23%)</td>
<td valign="top" align="center" colspan="4" rowspan="1">41,269 (0.12%)</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">unann</td>
<td valign="top" align="center" colspan="4" rowspan="1">1,340,404 (30.17%)</td>
<td valign="top" align="center" colspan="4" rowspan="1">12,776,888 (37.92%)</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>The raw data of the degradome was trimmed by pre-analysis similar to the sRNAome raw data to obtain clean reads. The GenBank and Rfam 11.0 databases were used to annotate the clean reads to remove rRNAs, snoRNAs, snRNAs, and tRNAs. The reads with a single base over 70% in the sequences were identified as poly N. Then, the reads were reannotated according to the Rfam > GeneBank > poly N rule. The remaining unannotated sequences were mapped to the reference genes (cDNA) of the <italic>C. sinensis</italic>
 genome (Xu et al., <xref rid="B67" ref-type="bibr">2013</xref>
) by SOAP2 (Li et al., <xref rid="B33" ref-type="bibr">2009</xref>
). The reads mapped to cDNA_sense were used to predict cleavage sites by PAREsnip (version 2.3; Folkes et al., <xref rid="B19" ref-type="bibr">2012</xref>
). The cleaved target transcripts were categorized into five classes based on the abundance of degradome reads indicative of miRNA-mediated cleavage. Category 0 comprised the sequences whose abundance at the cleavage site was the only maximum on the transcript; in category 1, the reads abundance at the cleavage site was the maximum but not unique; category 2 consisted of sequences whose abundance at the cleavage site was higher than the median but not the maximum; category 3 included sequences whose abundance at the cleavage site was equal to or below the median; the remaining sequences, which were the only raw reads at the cleavage site, were classified as category 4. When the alignment score was no more than 4.5, the transcripts were considered as miRNA targets. The alignment score was calculated by the Rule-Based Complementarity Search algorithm (Folkes et al., <xref rid="B19" ref-type="bibr">2012</xref>
). The T-Plot figures were generated by VisSR (version 1.0).</p>
</sec>
<sec><title>Identification of known miRNAs and prediction of novel miRNAs</title>
<p>The software tag2miRNA (developed by BGI) was used to identify known miRNAs. We take full account of miRNA conservation. The steps are as following: first, considering the difference among species, align clean reads to the miRNA precursor/mature miRNA of all plants in the miRBase allowing two mismatches and three gaps; second, choose the highest expression miRNA for each mature miRNA family which is regarded as a temporary miRNA database; third, align clean reads to the above temporary miRNA database and the expression of miRNA is generated by summing the count of tags which can align to the temporary miRNA database within two mismatches; fourth, predict the precursor of the identified miRNA, if the precursor of the identified miRNA can't fold into hairpin structure, it will be regarded as pseudo-miRNA. The feasibility of our result can be greatly improved by this verification. The MIREAP pipeline (<ext-link ext-link-type="uri" xlink:href="http://sourceforge.net/projects/mireap/">http://sourceforge.net/projects/mireap/</ext-link>
) was used to identify novel miRNAs with default parameters for plant. Some key conditions are as following: (a) the reads which be used to predict novel miRNA are from the unannotated reads which can match to reference genome, the reads which can align to intron region and the reads which can align to antisense exon region; (b) those genes whose sequences and structures meet these two criteria (i.e., the sequences can fold into hairpin secondary structures and mature miRNAs are present in one arm of the hairpin structures) will be considered as candidate miRNA genes; (c) the mature miRNA strand and its complementary strand (miRNA<sup>*</sup>
) present 2-nucleotide 3′ overhangs; (d) hairpin precursors lack large internal loops or bulges; (e) the secondary structures of the hairpins are steady, with the free energy of hybridization lower than or equal to −18 kcal/mol; (f) the number of mature miRNA with predicted hairpin must be no fewer than 5 in the alignment result. The hairpin structures of pre-miRNA sequences were visualized by VisSR (version 1.0) and selected manually. In addition, considering many of the known miRNAs in model plant species were not found to have miRNAs<sup>*</sup>
 in small RNA sequencing, only novel miRNAs were required to have miRNAs<sup>*</sup>
 in this study (Xie et al., <xref rid="B65" ref-type="bibr">2012</xref>
).</p>
</sec>
<sec><title>miRNA expression profiles and comparison between MT and WT</title>
<p>Raw miRNA counts were normalized to RPM (reads per million) using the equation RPM = (raw count/total raw count) × 1,000,000. According to previous reports (Murakami et al., <xref rid="B44" ref-type="bibr">2006</xref>
; Xie et al., <xref rid="B64" ref-type="bibr">2015</xref>
), original miRNA expression equal 0 in a library was normalized expression to 0.01. The miRNA expression fold change between MT and WT was computed with the formula “Fold change = log<sub>2</sub>
(MT/WT).” The differential expression analysis was performed using the R-package NOISeq (Tarazona et al., <xref rid="B53" ref-type="bibr">2012</xref>
, <xref rid="B52" ref-type="bibr">2015</xref>
). NOISeq method can screen differentially expressed genes between two groups, showing a good performance when comparing it to other differential expression methods, like Fisher's Exact, Test (FET), edgeR, DESeq, and baySeq. NOISeq maintains good True Positive and False Positive rates when increasing sequencing depth, while most other methods show poor performance. What's more, NOISeq models the noise distribution from the actual data, so it can better adapt to the size of the data set, and is more effective in controlling the rate of false discoveries. First, NOISeq uses sample's gene expression in each group to calculate log<sub>2</sub>
(fold change) M and absolute different value D of all pair conditions to build noise distribution model. Second, for gene A, NOISeq computes its avearge expression “Control_avg” in control group and average expression “Treat_avg” in treatment group. Then the foldchange [M<sub>A</sub>
 = log<sub>2</sub>
((Treat_avg)/(Control_avg))] and absolute different value D (D<sub>A</sub>
 = |Congrol_avg-Treat_avg|) will be got. If M<sub>A</sub>
 and D<sub>A</sub>
 diverge from noise distribution model markedly, gene A will be defined as a DEG. There is a probability (Prob.) value to assess how M<sub>A</sub>
 and D<sub>A</sub>
 both diverge from noise distribution model. Fold change and probability (Prob.) were combined to determine the final miRNA expression significance. The value of |log<sub>2</sub>
(fold change)| ≥ 1.0 with a Prob. > 0.8 was defined as a significant difference.</p>
</sec>
<sec><title>Identification of <italic>PHAS</italic>
 genes and phasiRNAs</title>
<p>Ta-si prediction tool (version 2.0), one tool of the UEA sRNA workbench (Stocks et al., <xref rid="B50" ref-type="bibr">2012</xref>
), was used to identify phasiRNAs and <italic>PHAS</italic>
 genes. The clean reads of MT and WT were supplied as sRNA dataset and the <italic>C. sinensis</italic>
 genome (Xu et al., <xref rid="B67" ref-type="bibr">2013</xref>
) was supplied as reference genome. The cutoff <italic>P</italic>
-value calculated by the algorithm described by Chen et al. (<xref rid="B11" ref-type="bibr">2007</xref>
) was set as 1.0E−4. The sRNAs that did not match the genome were discarded, and only 21 nt sRNAs were used in the phasing analysis.</p>
</sec>
<sec><title>Target gene functional annotation</title>
<p>The protein sequences of the identified miRNA target genes were aligned against GO database and the KEGG pathway database using KOBAS 2.0 (<ext-link ext-link-type="uri" xlink:href="http://kobas.cbi.pku.edu.cn/">http://kobas.cbi.pku.edu.cn/</ext-link>
; Xie et al., <xref rid="B63" ref-type="bibr">2011</xref>
) to perform enrichment analysis. The corrected <italic>P</italic>
 < 0.05 was set as cutoff for enrichment. REVIGO (Supek et al., <xref rid="B51" ref-type="bibr">2011</xref>
) was used to visualize and summarize the biological process and molecular function terms that were identified by KOBAS 2.0. In addition, the target genes were also annotated by a BLASTP search against the NR database of NCBI with cutoff <italic>E</italic>
 < 1e−8, and BlastKOALA was used to annotate the KOs of the KEGG ORTHOLOGY database.</p>
</sec>
<sec><title>Validation of miRNA and target gene expression in fruit development and ripening by qRT-PCR</title>
<p>Stem-loop qRT-PCR was performed to validate the expressions of miRNAs with three biological replicates based on a previous method (Chen et al., <xref rid="B9" ref-type="bibr">2005</xref>
; Liu Y. et al., <xref rid="B35" ref-type="bibr">2014</xref>
). The stem-loop reverse transcriptase reaction used 20 μl PCR system: firstly, added 10 mM dNTPs (Promega) and 500 ng purified total RNA into a 200 μl centrifuge tube, slightly centrifuged, then incubated 5 min at 65°C, 2 min on ice; secondly, added 4 μl 5x RT buffer (Invitrogen), 2 μl DTT (Invitrogen, 0.1 M), 0.1 μl RNaseOUT (Invitrogen, 40 U/μl), 0.25 μl SuperScript III (Invitrogen, 200 U/μl), and 1 μl stem-loop RT primer. The thermal cycling program of stem-loop RT PCR was set at 16°C for 30 min, 60 cycles of 30°C for 30 s, 42°C for 30 s, and 50°C for 1 s, then, 85°C for 5 min and 4°C for 30 min. U4 was used as the endogenous reference gene (Kou et al., <xref rid="B30" ref-type="bibr">2012</xref>
). The primers are listed in Table <xref ref-type="supplementary-material" rid="SM14">S8</xref>
. Next, the qRT-PCR was performed according to our previous study (Wu et al., <xref rid="B59" ref-type="bibr">2014b</xref>
), which was briefly described as following.</p>
<p>qRT-PCR was performed to confirm the expression of target genes with three biological replicates according to our previous study (Wu et al., <xref rid="B59" ref-type="bibr">2014b</xref>
). qRT-PCR was performed in ABI 7900HT Fast Real-time system (PE Applied Biosystems, Foster City, CA, USA) with optical 384-well plates. The SYBR Green PCR Master Mix (PE Applied Biosystems) was used in reactions. <italic>CsActin</italic>
 was used as the endogenous reference gene (Wu et al., <xref rid="B58" ref-type="bibr">2014a</xref>
). The primers are listed in Table <xref ref-type="supplementary-material" rid="SM14">S8</xref>
. Ten microlitres of the reaction mixture was added to each well. The thermal cycling program was set at 50°C for 2 min, 95°C for 1 min, and 40 cycles of 95°C for 15 s, and of 60°C for 1 min.</p>
</sec>
</sec>
<sec sec-type="results" id="s3"><title>Results</title>
<sec><title>Deep sequencing of small RNA libraries and the degradome library</title>
<p>In our previous study (Wu et al., <xref rid="B59" ref-type="bibr">2014b</xref>
), we comparatively analyzed the transcriptomes and proteomes of MT and WT during citrus fruit ripening, and the 170 DAF stage was identified as the stage at which the most number of differentially expressed genes and proteins between MT and WT were found. To identify the miRNAs involved in the citrus fruit ripening process, the pulps of MT and WT harvested at 170 DAF were used to construct four sRNA libraries with two biological replicates for each sample, which were then sequenced using Illumina HiSeq™2000. After removing the low-quality reads, clean reads were produced. A total of 34,248,996; 31,898,997; 30,815,849; and 23,377,054 reads were generated from two MT samples and two WT samples, respectively, including 9,503,927; 8,755,251; 8,247,653; and 7,105,581 unique reads, respectively (Table <xref ref-type="table" rid="T1">1</xref>
). Less than 20.65 million (60.3%) redundant reads matched the <italic>C. sinensis</italic>
 genome (Xu et al., <xref rid="B67" ref-type="bibr">2013</xref>
) in each library (Table <xref ref-type="table" rid="T1">1</xref>
). A search of the miRBase database (v20) identified 39,836 (0.42%), 38,104 (0.44%), 37,762 (0.46%), and 34,132 (0.48%) unique reads for the sRNAs of the two MT and the two WT samples, respectively (Table <xref ref-type="table" rid="T1">1</xref>
). For annotation, the small RNAs were grouped into several categories, including GenBank/Rfam (the sum of rRNAs, snRNAs, snoRNAs, and tRNAs) and miRNAs (Table <xref ref-type="table" rid="T1">1</xref>
). The unannotated sRNAs were mapped to the genome and were used to predict novel miRNAs based on the structure and expression criteria (Meyers et al., <xref rid="B39" ref-type="bibr">2008</xref>
). Sequences with lengths between 18 and 28 nt accounted for ~99.9% of all the clean reads and sequences matching the known miRNAs were counted to determine the size distribution (Figure <xref ref-type="fig" rid="F1">1</xref>
). The length distribution patterns of all clean reads (Figure <xref ref-type="fig" rid="F1">1A</xref>
) and the reads matching known miRNAs (Figure <xref ref-type="fig" rid="F1">1B</xref>
) in the four sRNA libraries were similar and did not show significant differences between MT and WT.</p>
<fig id="F1" position="float"><label>Figure 1</label>
<caption><p><bold>Length distribution of the small RNA reads (A) and known miRNAs (B) in the sequencing samples</bold>
.</p>
</caption>
<graphic xlink:href="fpls-07-01416-g0001"></graphic>
</fig>
<p>To identify the target genes of miRNAs, a high-throughput approach, called degradome sequencing, or parallel analysis of RNA ends (PARE; Addo-Quaye et al., <xref rid="B2" ref-type="bibr">2008</xref>
; German et al., <xref rid="B20" ref-type="bibr">2008</xref>
) was performed to experimentally validate target genes of miRNAs by capturing mRNA cleavage segments. This approach can concurrently identify all cleavage products in the genome with a high sensitivity detection, compared with older techniques, such as northern blotting or 5′ RACE (Xia et al., <xref rid="B62" ref-type="bibr">2015b</xref>
). To maximize the identification of miRNA targets, we pooled RNA from the pulps of MT and WT into one library. In total, 33.69 million total reads with 4.44 million unique reads were obtained (Table <xref ref-type="table" rid="T1">1</xref>
). A search of the Rfam and GenBank database was used to remove the structural RNAs (rRNAs, tRNAs, snRNAs, and snoRNAs), which represented 0.3% of the unique reads. Additionally, the polyN reads, representing 0.23% of the unique reads, were also removed. The remaining sequences were mapped to the <italic>C. sinensis</italic>
 genome (Xu et al., <xref rid="B67" ref-type="bibr">2013</xref>
). Finally, 3.21 million (72.25%) unique reads were mapped to the reference genome and 3.05 million (68.53%) unique reads were mapped to the cDNA sequences of the reference genome. All sequences that mapped to the cDNA library (the sequences in the cDNA_sense category in Table <xref ref-type="table" rid="T1">1</xref>
) were analyzed to detect candidate targets of miRNAs.</p>
</sec>
<sec><title>Identification and expression analysis of known and novel miRNAs in the fruits of MT and WT</title>
<p>A total of 107 known miRNAs, belonging to 53 families, and 21 novel miRNAs were identified from the four libraries of MT and WT (Tables <xref ref-type="supplementary-material" rid="SM7">S1</xref>
, <xref ref-type="supplementary-material" rid="SM9">S3</xref>
). Only the miRNAs identified in both biological replicates were retained. As shown in Figure <xref ref-type="supplementary-material" rid="SM1">S1</xref>
, 99 known miRNAs and 17 novel miRNAs were identified in MT, 92 known miRNAs and 16 novel miRNAs were identified in WT. And among these miRNAs 84 known miRNAs and 12 novel miRNAs were identified in both MT and WT. All of the precursors of known and novel miRNAs had regular stem-loop secondary structures, and the sequences of the miRNAs are shown in blue and the sequences of the miRNAs<sup>*</sup>
 are in red (if the miRNA<sup>*</sup>
 was identified; Figures <xref ref-type="supplementary-material" rid="SM3">S3</xref>
, <xref ref-type="supplementary-material" rid="SM4">S4</xref>
). Pre-miRNAs (the precursors) of known and novel miRNAs were not evenly distributed in the citrus genome; there were more pre-miRNAs located in chromosome 2 (chr2) and chromosome 7 (chr7; Figure <xref ref-type="supplementary-material" rid="SM2">S2</xref>
).</p>
<p>The normalized expressions (RPM) of known miRNAs in MT and WT ranged from 0.78 to 2151.12 and 0.34 to 1780.005, respectively (excluding the miRNAs that were not identified; Table <xref ref-type="supplementary-material" rid="SM7">S1</xref>
). The results indicated that the known miRNAs exhibited extensive variation in their abundances. The RPM of most novel miRNAs was low; however, there were two novel miRNAs, csi-miRN03, and csi-miRN11, that were highly expressed in both MT and WT (Table <xref ref-type="supplementary-material" rid="SM8">S2</xref>
). The RPMs of csi-miRN11 and csi-miRN03 were much higher than those of known miRNAs.</p>
<p>An R package, NOISeq, was used to perform differential expression analysis of miRNAs between MT and WT. Based on the criteria of significant difference, |log<sub>2</sub>
(MT/WT)| ≥ 1 and prob. > 0.8, 16 known miRNAs and 8 novel miRNAs were identified as differentially expressed between MT and WT (Figure <xref ref-type="fig" rid="F2">2</xref>
, Tables <xref ref-type="supplementary-material" rid="SM7">S1</xref>
, <xref ref-type="supplementary-material" rid="SM8">S2</xref>
). The results showed that 14.95% (16/107) of the known miRNAs and 38.10% (8/21) of the novel miRNAs were differentially expressed between MT and WT. As shown in Figure <xref ref-type="fig" rid="F2">2</xref>
, there were more up-regulated miRNAs than down-regulated miRNAs. For the known miRNAs, 12 miRNAs were up-regulated and 4 miRNAs were down-regulated. For the novel miRNAs, 5 miRNAs were up-regulated and 3 miRNA were down-regulated. These results were consistent with our previous study (Wu et al., <xref rid="B59" ref-type="bibr">2014b</xref>
), in which there were more down-regulated transcripts than up-regulated transcripts at 170 DAF comparing MT with WT.</p>
<fig id="F2" position="float"><label>Figure 2</label>
<caption><p><bold>The differentially expressed known miRNAs (A) and novel miRNAs (B) between MT and WT</bold>
.</p>
</caption>
<graphic xlink:href="fpls-07-01416-g0002"></graphic>
</fig>
</sec>
<sec><title>Identification and annotation of the targets of miRNAs</title>
<p>From the degradome data, a total of 401 transcripts from 225 genes were predicted to be targeted by 57 miRNAs (47 known miRNAs and 10 novel miRNAs), with 692 miRNA-target pairs (Table <xref ref-type="supplementary-material" rid="SM9">S3</xref>
). These 401 target transcripts were classified into five categories (category 0, 1, 2, 3, and 4) based on the confidence evaluation of the degradome data. Category 0 is the most reliable for the detection of miRNA target genes, in which the miRNA-guided cleavage fragment was the most abundant tag matching the transcript. In category 1 and 2, the miRNA-guided cleavage fragment was not the most abundant tag, but it still formed a clear peak in the T-plot. In category 3 and 4, the miRNA-target pairs are not reliable. There were 188, 15, 133, 59, and 6 target transcripts in category 0, 1, 2, 3, and 4, respectively (Table <xref ref-type="supplementary-material" rid="SM9">S3</xref>
 and Figure <xref ref-type="supplementary-material" rid="SM5">S5</xref>
). The miRNA-target pairs were visualized on the citrus genome by the Circos software package (<ext-link ext-link-type="uri" xlink:href="http://circos.ca/">http://circos.ca/</ext-link>
; Figure <xref ref-type="supplementary-material" rid="SM6">S6</xref>
). We found that the target genes of miRNAs, represented by arrows, were evenly distributed in the citrus genome (Figure <xref ref-type="supplementary-material" rid="SM6">S6</xref>
). The detailed information on these target genes is listed in Table <xref ref-type="supplementary-material" rid="SM10">S4</xref>
.</p>
<p>To further elucidate the roles of miRNAs in fruit ripening, GO-based term classification and KEGG-based pathway enrichment analyses were performed. Under a cutoff of corrected <italic>P</italic>
 < 0.05, the targets of miRNAs were shown to be enriched in 33 biological processes, 14 molecular functions, and 2 cellular components after summarizing the GO terms by removing redundant GO terms by REViGO (Supek et al., <xref rid="B51" ref-type="bibr">2011</xref>
; Table <xref ref-type="supplementary-material" rid="SM11">S5</xref>
). In biological processes, several hubs were significantly enriched, including innate immune response, salicylic acid biosynthetic process, phyllome development, response to stimulus, flavonoid metabolic process, and signaling (Figure <xref ref-type="fig" rid="F3">3A</xref>
). ADP binding, carbohydrate derivative binding, small molecule binding, heterocyclic compound binding, and sequence-specific DNA binding transcription factor activity were significantly enriched in molecular function (Figure <xref ref-type="fig" rid="F3">3B</xref>
). The RNAi effector complex and RISC complex were the enriched cell components (Table <xref ref-type="supplementary-material" rid="SM11">S5</xref>
). However, no enrichment in KEGG pathway was identified, and only 58 genes were annotated to KOs (Table <xref ref-type="supplementary-material" rid="SM10">S4</xref>
).</p>
<fig id="F3" position="float"><label>Figure 3</label>
<caption><p><bold>Biological process (A) and molecular function (B) enrichment analysis of the target genes of miRNAs</bold>
. The bubble color indicates the <italic>P</italic>
-value; the plot size indicates the frequency of the GO term in the underlying GOA database (bubbles of more general terms are larger).</p>
</caption>
<graphic xlink:href="fpls-07-01416-g0003"></graphic>
</fig>
</sec>
<sec><title>Functional analysis of genes targeted by differentially expressed miRNAs between MT and WT</title>
<p>In the present study, 27 target genes of 14 differentially expressed miRNAs were identified (Tables <xref ref-type="table" rid="T2">2</xref>
, <xref ref-type="table" rid="T3">3</xref>
). After removing redundant GO terms, the targets of differentially expressed miRNAs were enriched in 13 biological processes and 5 molecular functions (corrected <italic>P</italic>
 < 0.05; Table <xref ref-type="table" rid="T4">4</xref>
). In molecular functions, 11 genes were enriched in sequence-specific DNA binding transcription factor activity (GO:0003700), indicating that transcription factors were the major class of genes targeted by differentially expressed miRNAs; in biological processes, several enriched GO terms were related to fruit development, such as xylem and phloem pattern formation (GO:0010051), developmental maturation (GO:0021700), regulation of growth (GO:0040008), and phyllome development (GO:0048827; Table <xref ref-type="table" rid="T4">4</xref>
).</p>
<table-wrap id="T2" position="float"><label>Table 2</label>
<caption><p><bold>Targets of the known miRNAs identified in MT and WT</bold>
.</p>
</caption>
<table frame="hsides" rules="groups"><thead><tr><th valign="top" align="left" rowspan="1" colspan="1"><bold>miRNA/Target gene</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>Category</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>Cleavage position</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold><italic>P</italic>
-value</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>Fragment abundance</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>Alignment score</bold>
</th>
<th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>RPM</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>Prob</bold>
.</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>log2(MT/WT)</bold>
</th>
<th valign="top" align="left" rowspan="1" colspan="1"><bold>Target annotation</bold>
</th>
</tr>
<tr><th rowspan="1" colspan="1"></th>
<th rowspan="1" colspan="1"></th>
<th rowspan="1" colspan="1"></th>
<th rowspan="1" colspan="1"></th>
<th rowspan="1" colspan="1"></th>
<th rowspan="1" colspan="1"></th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>WT</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>MT</bold>
</th>
<th rowspan="1" colspan="1"></th>
<th rowspan="1" colspan="1"></th>
<th rowspan="1" colspan="1"></th>
</tr>
</thead>
<tbody><tr><td valign="top" align="left" rowspan="1" colspan="1"><bold>csi-miR156k</bold>
</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">16.63</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.01</td>
<td valign="top" align="center" rowspan="1" colspan="1">1.00</td>
<td valign="top" align="center" rowspan="1" colspan="1">−10.70</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs2g23550</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">934</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.01</td>
<td valign="top" align="center" rowspan="1" colspan="1">27</td>
<td valign="top" align="center" rowspan="1" colspan="1">3</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">SPL4</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs7g10830</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">1183</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">15</td>
<td valign="top" align="center" rowspan="1" colspan="1">1</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">SPL2</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs7g10990</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">1547</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">15</td>
<td valign="top" align="center" rowspan="1" colspan="1">2</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">SPL12</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs7g11770</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">1488</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">12</td>
<td valign="top" align="center" rowspan="1" colspan="1">1.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">SPL6</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1"><bold>csi-miR159</bold>
</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">25.11</td>
<td valign="top" align="center" rowspan="1" colspan="1">55.64</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.99</td>
<td valign="top" align="center" rowspan="1" colspan="1">1.15</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs1g03470</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">108</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">22</td>
<td valign="top" align="center" rowspan="1" colspan="1">2</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">GAMYB</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs1g06080</td>
<td valign="top" align="center" rowspan="1" colspan="1">2</td>
<td valign="top" align="center" rowspan="1" colspan="1">589</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.03</td>
<td valign="top" align="center" rowspan="1" colspan="1">3</td>
<td valign="top" align="center" rowspan="1" colspan="1">2.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">NOZZLE</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs3g06390</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">773</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">58</td>
<td valign="top" align="center" rowspan="1" colspan="1">2.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">GAMYB</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs8g05120</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">205</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">32</td>
<td valign="top" align="center" rowspan="1" colspan="1">3</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">Polygalacturonase inhibitor 1</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1"><bold>csi-miR166d</bold>
</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">1.45</td>
<td valign="top" align="center" rowspan="1" colspan="1">3.12</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.87</td>
<td valign="top" align="center" rowspan="1" colspan="1">1.10</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs1g15640</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">1781</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">94</td>
<td valign="top" align="center" rowspan="1" colspan="1">2.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">ATHB15</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs2g09770</td>
<td valign="top" align="center" rowspan="1" colspan="1">1</td>
<td valign="top" align="center" rowspan="1" colspan="1">1069</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">10</td>
<td valign="top" align="center" rowspan="1" colspan="1">2.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">ATHB14</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs4g19310</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">677</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">189</td>
<td valign="top" align="center" rowspan="1" colspan="1">2.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">ATHB8</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs6g14050</td>
<td valign="top" align="center" rowspan="1" colspan="1">2</td>
<td valign="top" align="center" rowspan="1" colspan="1">454</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.03</td>
<td valign="top" align="center" rowspan="1" colspan="1">7</td>
<td valign="top" align="center" rowspan="1" colspan="1">3</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">IAA-amino acid hydrolase ILR1-like 1</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs8g16510</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">1434</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">211</td>
<td valign="top" align="center" rowspan="1" colspan="1">2.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">REVOLUTA</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1"><bold>csi-miR172h-5p</bold>
</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">2.43</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.01</td>
<td valign="top" align="center" rowspan="1" colspan="1">1.00</td>
<td valign="top" align="center" rowspan="1" colspan="1">−7.92</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs6g14320</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">902</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.04</td>
<td valign="top" align="center" rowspan="1" colspan="1">32</td>
<td valign="top" align="center" rowspan="1" colspan="1">4</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">Abhydrolase domain-containing protein 11</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1"><bold>csi-miR394</bold>
</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">0.35</td>
<td valign="top" align="center" rowspan="1" colspan="1">1.72</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.98</td>
<td valign="top" align="center" rowspan="1" colspan="1">2.31</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs7g10850</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">1168</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">85</td>
<td valign="top" align="center" rowspan="1" colspan="1">1</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1"><italic>F</italic>
-box family protein</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">csi-miR399e</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">0.72</td>
<td valign="top" align="center" rowspan="1" colspan="1">2.72</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.99</td>
<td valign="top" align="center" rowspan="1" colspan="1">1.91</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs2g06030</td>
<td valign="top" align="center" rowspan="1" colspan="1">2</td>
<td valign="top" align="center" rowspan="1" colspan="1">1077</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.03</td>
<td valign="top" align="center" rowspan="1" colspan="1">13</td>
<td valign="top" align="center" rowspan="1" colspan="1">3</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">UBC24</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1"><bold>csi-miR5227</bold>
</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">0.01</td>
<td valign="top" align="center" rowspan="1" colspan="1">6.27</td>
<td valign="top" align="center" rowspan="1" colspan="1">1.00</td>
<td valign="top" align="center" rowspan="1" colspan="1">9.29</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs3g22200</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">55</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.02</td>
<td valign="top" align="center" rowspan="1" colspan="1">8</td>
<td valign="top" align="center" rowspan="1" colspan="1">3</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">50S ribosomal protein L18</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1"><bold>csi-miR530b</bold>
</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">1.63</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.79</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.86</td>
<td valign="top" align="center" rowspan="1" colspan="1">−1.05</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs9g08500</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">1784</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">20</td>
<td valign="top" align="center" rowspan="1" colspan="1">1.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">Hypothetical protein VITISV_041073</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">orange1.1t02948</td>
<td valign="top" align="center" rowspan="1" colspan="1">2</td>
<td valign="top" align="center" rowspan="1" colspan="1">2768</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.04</td>
<td valign="top" align="center" rowspan="1" colspan="1">5</td>
<td valign="top" align="center" rowspan="1" colspan="1">2.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">PWD</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1"><bold>csi-miR535</bold>
</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">8.01</td>
<td valign="top" align="center" rowspan="1" colspan="1">17.85</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.96</td>
<td valign="top" align="center" rowspan="1" colspan="1">1.16</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs6g07930</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">1353</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">17</td>
<td valign="top" align="center" rowspan="1" colspan="1">2.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">Serine/Threonine-protein kinase HT1</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="T3" position="float"><label>Table 3</label>
<caption><p><bold>Targets of the novel miRNAs identified in MT and WT</bold>
.</p>
</caption>
<table frame="hsides" rules="groups"><thead><tr><th valign="top" align="left" rowspan="1" colspan="1"><bold>miRNA/Target</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>Category</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>Cleavage</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>Abundance</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>Alignment</bold>
</th>
<th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;" rowspan="1"><bold>RPM</bold>
</th>
<th valign="top" align="left" rowspan="1" colspan="1"><bold>Prob</bold>
.</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>log<sub>2</sub>
 (MT/WT)</bold>
</th>
<th valign="top" align="left" rowspan="1" colspan="1"><bold>Target</bold>
</th>
</tr>
<tr><th valign="top" align="left" rowspan="1" colspan="1"><bold>gene</bold>
</th>
<th rowspan="1" colspan="1"></th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>position</bold>
</th>
<th rowspan="1" colspan="1"></th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>score</bold>
</th>
<th rowspan="1" colspan="1"></th>
<th rowspan="1" colspan="1"></th>
<th rowspan="1" colspan="1"></th>
<th rowspan="1" colspan="1"></th>
<th valign="top" align="left" rowspan="1" colspan="1"><bold>annotation</bold>
</th>
</tr>
<tr><th rowspan="1" colspan="1"></th>
<th rowspan="1" colspan="1"></th>
<th rowspan="1" colspan="1"></th>
<th rowspan="1" colspan="1"></th>
<th rowspan="1" colspan="1"></th>
<th valign="top" align="left" rowspan="1" colspan="1"><bold>WT</bold>
</th>
<th valign="top" align="left" rowspan="1" colspan="1"><bold>MT</bold>
</th>
<th rowspan="1" colspan="1"></th>
<th rowspan="1" colspan="1"></th>
<th rowspan="1" colspan="1"></th>
</tr>
</thead>
<tbody><tr><td valign="top" align="left" rowspan="1" colspan="1"><bold>csi-miRN02</bold>
</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">31.04</td>
<td valign="top" align="center" rowspan="1" colspan="1">26.06</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.41</td>
<td valign="top" align="center" rowspan="1" colspan="1">−0.25</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs9g17500</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">2474</td>
<td valign="top" align="center" rowspan="1" colspan="1">15</td>
<td valign="top" align="center" rowspan="1" colspan="1">4.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">Cleavage and polyadenylation specificity factor subunit 2</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1"><bold>csi-miRN03</bold>
</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">1533</td>
<td valign="top" align="center" rowspan="1" colspan="1">2136</td>
<td valign="top" align="center" rowspan="1" colspan="1">1.00</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.48</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs8g13560</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">302</td>
<td valign="top" align="center" rowspan="1" colspan="1">434</td>
<td valign="top" align="center" rowspan="1" colspan="1">2</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">Unknown protein</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1"><bold>csi-miRN04</bold>
</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">2.89</td>
<td valign="top" align="center" rowspan="1" colspan="1">3.56</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.33</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.30</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs5g05430</td>
<td valign="top" align="center" rowspan="1" colspan="1">2</td>
<td valign="top" align="center" rowspan="1" colspan="1">549</td>
<td valign="top" align="center" rowspan="1" colspan="1">22</td>
<td valign="top" align="center" rowspan="1" colspan="1">3</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">Peroxisomal membrane protein PEX14</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1"><bold>csi-miRN06</bold>
</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">71.74</td>
<td valign="top" align="center" rowspan="1" colspan="1">46.56</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.86</td>
<td valign="top" align="center" rowspan="1" colspan="1">−0.62</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs4g20380</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">2650</td>
<td valign="top" align="center" rowspan="1" colspan="1">71</td>
<td valign="top" align="center" rowspan="1" colspan="1">4.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">Transcriptional corepressor LEUNIG</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1"><bold>csi-miRN12</bold>
</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">0.01</td>
<td valign="top" align="center" rowspan="1" colspan="1">54.57</td>
<td valign="top" align="center" rowspan="1" colspan="1">1.00</td>
<td valign="top" align="center" rowspan="1" colspan="1">12.41</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs3g22150</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">1030</td>
<td valign="top" align="center" rowspan="1" colspan="1">15</td>
<td valign="top" align="center" rowspan="1" colspan="1">4.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">Oxidoreductase, putative</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs1g11750</td>
<td valign="top" align="center" rowspan="1" colspan="1">1</td>
<td valign="top" align="center" rowspan="1" colspan="1">1782</td>
<td valign="top" align="center" rowspan="1" colspan="1">8</td>
<td valign="top" align="center" rowspan="1" colspan="1">4.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">30S ribosomal protein S15</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1"><bold>csi-miRN13</bold>
</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">0.01</td>
<td valign="top" align="center" rowspan="1" colspan="1">40.54</td>
<td valign="top" align="center" rowspan="1" colspan="1">1.00</td>
<td valign="top" align="center" rowspan="1" colspan="1">11.98</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">orange1.1t03349</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">1447</td>
<td valign="top" align="center" rowspan="1" colspan="1">25</td>
<td valign="top" align="center" rowspan="1" colspan="1">4.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">ABC transporter-like [<italic>Arabidopsis thaliana</italic>
]</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1"><bold>csi-miRN14</bold>
</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">20.01</td>
<td valign="top" align="center" rowspan="1" colspan="1">14.91</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.55</td>
<td valign="top" align="center" rowspan="1" colspan="1">−0.42</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs7g10090</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">31</td>
<td valign="top" align="center" rowspan="1" colspan="1">3</td>
<td valign="top" align="center" rowspan="1" colspan="1">3.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">Unknown protein</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1"><bold>csi-miRN18</bold>
</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">37.36</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.01</td>
<td valign="top" align="center" rowspan="1" colspan="1">1.00</td>
<td valign="top" align="center" rowspan="1" colspan="1">−11.87</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs4g07790</td>
<td valign="top" align="center" rowspan="1" colspan="1">3</td>
<td valign="top" align="center" rowspan="1" colspan="1">773</td>
<td valign="top" align="center" rowspan="1" colspan="1">5</td>
<td valign="top" align="center" rowspan="1" colspan="1">4.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">Probable amino acid permease 7 [Vitis vinifera]</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1"><bold>csi-miRN19</bold>
</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">0.99</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.01</td>
<td valign="top" align="center" rowspan="1" colspan="1">NA</td>
<td valign="top" align="center" rowspan="1" colspan="1">−6.63</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">orange1.1t00200</td>
<td valign="top" align="center" rowspan="1" colspan="1">2</td>
<td valign="top" align="center" rowspan="1" colspan="1">1514</td>
<td valign="top" align="center" rowspan="1" colspan="1">174</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">SCL6 [Citrus trifoliata]</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">orange1.1t00199</td>
<td valign="top" align="center" rowspan="1" colspan="1">2</td>
<td valign="top" align="center" rowspan="1" colspan="1">1615</td>
<td valign="top" align="center" rowspan="1" colspan="1">174</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">SCL6 [Citrus trifoliata]</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs5g08980</td>
<td valign="top" align="center" rowspan="1" colspan="1">0</td>
<td valign="top" align="center" rowspan="1" colspan="1">1700</td>
<td valign="top" align="center" rowspan="1" colspan="1">110</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.5</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">SCL15</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1"><bold>csi-miRN21</bold>
</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="center" rowspan="1" colspan="1">3.29</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.01</td>
<td valign="top" align="center" rowspan="1" colspan="1">1.00</td>
<td valign="top" align="center" rowspan="1" colspan="1">−8.36</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs6g02130</td>
<td valign="top" align="center" rowspan="1" colspan="1">2</td>
<td valign="top" align="center" rowspan="1" colspan="1">1054</td>
<td valign="top" align="center" rowspan="1" colspan="1">6</td>
<td valign="top" align="center" rowspan="1" colspan="1">2</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">cc-nbs-lrr resistance protein [<italic>Populus trichocarpa</italic>
]</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs6g02120</td>
<td valign="top" align="center" rowspan="1" colspan="1">2</td>
<td valign="top" align="center" rowspan="1" colspan="1">913</td>
<td valign="top" align="center" rowspan="1" colspan="1">6</td>
<td valign="top" align="center" rowspan="1" colspan="1">2</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">Disease resistance protein RFL1, putative</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Cs6g02100</td>
<td valign="top" align="center" rowspan="1" colspan="1">2</td>
<td valign="top" align="center" rowspan="1" colspan="1">1054</td>
<td valign="top" align="center" rowspan="1" colspan="1">6</td>
<td valign="top" align="center" rowspan="1" colspan="1">2</td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"></td>
<td valign="top" align="left" rowspan="1" colspan="1">PREDICTED: disease resistance protein At4g27190-like [Vitis vinifera]</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="T4" position="float"><label>Table 4</label>
<caption><p><bold>Gene Ontology enrichment analysis of the target genes of miRNAs differentially expressed between MT and WT</bold>
.</p>
</caption>
<table frame="hsides" rules="groups"><thead><tr><th valign="top" align="left" rowspan="1" colspan="1"><bold>Gen ontology (GO) terms</bold>
</th>
<th valign="top" align="left" rowspan="1" colspan="1"><bold>Term ID</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>Corrected <italic>P</italic>
-value</bold>
</th>
<th valign="top" align="center" rowspan="1" colspan="1"><bold>Gene number</bold>
</th>
<th valign="top" align="left" rowspan="1" colspan="1"><bold>Gene list</bold>
</th>
</tr>
</thead>
<tbody><tr><td valign="top" align="left" colspan="5" style="background-color:#bbbdc0" rowspan="1"><bold>BIOLOGICAL PROCESS</bold>
</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Xylem and phloem pattern formation</td>
<td valign="top" align="left" rowspan="1" colspan="1">GO:0010051</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.0019</td>
<td valign="top" align="center" rowspan="1" colspan="1">4</td>
<td valign="top" align="left" rowspan="1" colspan="1">Cs2g09770 |Cs7g10850 |Cs8g16510 |Cs1g15640</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Regulation of actin filament-based process</td>
<td valign="top" align="left" rowspan="1" colspan="1">GO:0032970</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.0019</td>
<td valign="top" align="center" rowspan="1" colspan="1">4</td>
<td valign="top" align="left" rowspan="1" colspan="1">Cs2g09770 |Cs4g19310 |Cs8g16510 |Cs1g15640</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Positive regulation of cellular component biogenesis</td>
<td valign="top" align="left" rowspan="1" colspan="1">GO:0044089</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.0019</td>
<td valign="top" align="center" rowspan="1" colspan="1">4</td>
<td valign="top" align="left" rowspan="1" colspan="1">Cs2g09770 |Cs4g19310 |Cs8g16510 |Cs1g15640</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Regulation of cytoskeleton organization</td>
<td valign="top" align="left" rowspan="1" colspan="1">GO:0051493</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.0020</td>
<td valign="top" align="center" rowspan="1" colspan="1">4</td>
<td valign="top" align="left" rowspan="1" colspan="1">Cs2g09770 |Cs4g19310 |Cs8g16510 |Cs1g15640</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Integument development</td>
<td valign="top" align="left" rowspan="1" colspan="1">GO:0080060</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.0023</td>
<td valign="top" align="center" rowspan="1" colspan="1">2</td>
<td valign="top" align="left" rowspan="1" colspan="1">Cs2g09770 |Cs1g15640</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Actin filament-based process</td>
<td valign="top" align="left" rowspan="1" colspan="1">GO:0030029</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.0023</td>
<td valign="top" align="center" rowspan="1" colspan="1">5</td>
<td valign="top" align="left" rowspan="1" colspan="1">Cs6g07930 |Cs2g09770 |Cs4g19310 |Cs8g16510 |Cs1g15640</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Embryonic meristem development</td>
<td valign="top" align="left" rowspan="1" colspan="1">GO:0048508</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.0034</td>
<td valign="top" align="center" rowspan="1" colspan="1">3</td>
<td valign="top" align="left" rowspan="1" colspan="1">Cs2g09770 |Cs4g19310 |Cs8g16510</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Regulation of cellular component biogenesis</td>
<td valign="top" align="left" rowspan="1" colspan="1">GO:0044087</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.0035</td>
<td valign="top" align="center" rowspan="1" colspan="1">4</td>
<td valign="top" align="left" rowspan="1" colspan="1">Cs2g09770 |Cs4g19310 |Cs8g16510 |Cs1g15640</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Trichome morphogenesis</td>
<td valign="top" align="left" rowspan="1" colspan="1">GO:0010090</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.0035</td>
<td valign="top" align="center" rowspan="1" colspan="1">4</td>
<td valign="top" align="left" rowspan="1" colspan="1">Cs2g09770 |Cs4g19310 |Cs8g16510 |Cs1g15640</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Regulation of anatomical structure size</td>
<td valign="top" align="left" rowspan="1" colspan="1">GO:0090066</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.0050</td>
<td valign="top" align="center" rowspan="1" colspan="1">4</td>
<td valign="top" align="left" rowspan="1" colspan="1">Cs2g09770 |Cs4g19310 |Cs8g16510 |Cs1g15640</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Phyllome development</td>
<td valign="top" align="left" rowspan="1" colspan="1">GO:0048827</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.0050</td>
<td valign="top" align="center" rowspan="1" colspan="1">8</td>
<td valign="top" align="left" rowspan="1" colspan="1">Cs7g10990 |Cs1g15640 |Cs7g10830 |Cs1g11750 |Cs2g09770 |Cs1g06080 |Cs7g10850 |Cs8g16510</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Developmental maturation</td>
<td valign="top" align="left" rowspan="1" colspan="1">GO:0021700</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.0416</td>
<td valign="top" align="center" rowspan="1" colspan="1">4</td>
<td valign="top" align="left" rowspan="1" colspan="1">Cs2g09770 |Cs4g19310 |Cs8g16510 |Cs1g15640</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Regulation of growth</td>
<td valign="top" align="left" rowspan="1" colspan="1">GO:0040008</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.0448</td>
<td valign="top" align="center" rowspan="1" colspan="1">4</td>
<td valign="top" align="left" rowspan="1" colspan="1">Cs2g09770 |Cs3g06390 |Cs8g16510 |Cs1g15640</td>
</tr>
<tr><td valign="top" align="left" colspan="5" style="background-color:#bbbdc0" rowspan="1"><bold>MOLECULAR FUNCTION</bold>
</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Nucleic acid binding transcription factor activity</td>
<td valign="top" align="left" rowspan="1" colspan="1">GO:0001071</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.0061</td>
<td valign="top" align="center" rowspan="1" colspan="1">11</td>
<td valign="top" align="left" rowspan="1" colspan="1">Cs7g11770 |Cs7g10990 |Cs1g03470 |Cs1g15640 |Cs7g10830 |Cs2g09770 |Cs1g06080 |Cs4g19310 |Cs3g06390 |Cs2g23550 |Cs8g16510</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Sequence-specific DNA binding transcription factor activity</td>
<td valign="top" align="left" rowspan="1" colspan="1">GO:0003700</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.0061</td>
<td valign="top" align="center" rowspan="1" colspan="1">11</td>
<td valign="top" align="left" rowspan="1" colspan="1">Cs7g11770 |Cs7g10990 |Cs1g03470 |Cs1g15640 |Cs7g10830 |Cs2g09770 |Cs1g06080 |Cs4g19310 |Cs3g06390 |Cs2g23550 |Cs8g16510</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">ADP binding</td>
<td valign="top" align="left" rowspan="1" colspan="1">GO:0043531</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.0355</td>
<td valign="top" align="center" rowspan="1" colspan="1">3</td>
<td valign="top" align="left" rowspan="1" colspan="1">Cs6g02120 |Cs6g02100 |Cs6g02130</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">Lipid binding</td>
<td valign="top" align="left" rowspan="1" colspan="1">GO:0008289</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.0368</td>
<td valign="top" align="center" rowspan="1" colspan="1">4</td>
<td valign="top" align="left" rowspan="1" colspan="1">Cs2g09770 |Cs4g19310 |Cs8g16510 |Cs1g15640</td>
</tr>
<tr><td valign="top" align="left" rowspan="1" colspan="1">DNA binding</td>
<td valign="top" align="left" rowspan="1" colspan="1">GO:0003677</td>
<td valign="top" align="center" rowspan="1" colspan="1">0.0422</td>
<td valign="top" align="center" rowspan="1" colspan="1">11</td>
<td valign="top" align="left" rowspan="1" colspan="1">Cs7g11770 |Cs7g10990 |Cs1g03470 |Cs1g15640 |Cs7g10830 |Cs2g09770 |Cs4g19310 |Cs3g06390 |Cs2g23550 |Cs8g16510 |Cs9g08500</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>As shown in Table <xref ref-type="table" rid="T2">2</xref>
, csi-miR156k was strongly down-regulated in MT. The targets of miR156k are four <italic>SPL</italic>
 genes, including <italic>SPL4</italic>
 (Cs2g23550), <italic>SPL2</italic>
 (Cs7g10830), <italic>SPL6</italic>
 (Cs7g11770), and <italic>SPL12</italic>
 (Cs7g10990), which are important transcription factors during plant development (Manning et al., <xref rid="B38" ref-type="bibr">2006</xref>
; Wang et al., <xref rid="B57" ref-type="bibr">2009</xref>
; Miura et al., <xref rid="B42" ref-type="bibr">2010</xref>
; Gou et al., <xref rid="B22" ref-type="bibr">2011</xref>
). Csi-miR159 also targeted four genes, including two <italic>GAMYBs</italic>
 (Cs1g03470 and Cs3g06390) which play a central role during fruit ripening of strawberry (Vallarino et al., <xref rid="B55" ref-type="bibr">2015</xref>
). <italic>ATHB8/14/15</italic>
 (Cs4g19310, Cs2g09770, and Cs1g15640) were the targets of csi-miR166d and are involved in vascular development and auxin signaling (Ohashi-Ito and Fukuda, <xref rid="B46" ref-type="bibr">2003</xref>
; Baima et al., <xref rid="B5" ref-type="bibr">2014</xref>
). Csi-miRN21 targeted three disease resistance proteins. These results indicated that the targets of one miRNA had similar function (Table <xref ref-type="table" rid="T3">3</xref>
).</p>
</sec>
<sec><title>Identification of phasiRNAs and <italic>PHAS</italic>
 genes</title>
<p>From the four deep sequencing sRNA libraries of MT and WT, we were able to identify 205 <italic>PHAS</italic>
 genes (176 <italic>PHAS</italic>
 genes from MT and 154 <italic>PHAS</italic>
 genes from WT), which were capable of secondary siRNA production with a <italic>P</italic>
 ≤ 0.0001 (Table <xref ref-type="supplementary-material" rid="SM12">S6</xref>
). Most of these 205 <italic>PHAS</italic>
 genes encoded resistance proteins, and the number of phasiRNAs produced from each <italic>PHAS</italic>
 gene ranged from 3 to 22 (Table <xref ref-type="supplementary-material" rid="SM12">S6</xref>
). From the degradome data, 66 <italic>PHAS</italic>
 genes targeted by 9 miRNAs were identified (Table <xref ref-type="supplementary-material" rid="SM13">S7</xref>
). As shown in Table <xref ref-type="supplementary-material" rid="SM13">S7</xref>
, the miR482 family (csi-miR472, csi-miR482a-3p, csi-miR482b, and csi-miR482c) targeted 53 <italic>PHAS</italic>
 genes to generate phasiRNAs, and most of these target <italic>PHAS</italic>
 genes were <italic>NB-LRR</italic>
 (nucleotide binding leucine-rich repeat) genes that encode disease resistance proteins, which was consistent with previous studies (Wu et al., <xref rid="B60" ref-type="bibr">2015</xref>
; Xia et al., <xref rid="B61" ref-type="bibr">2015a</xref>
). In addition, csi-miR3950 was predicted to trigger NAC domain-containing proteins, csi-miR393h triggered two TIR/AFB auxin receptor proteins, csi-miR167a triggered <italic>ARF8</italic>
 and csi-miR1515 triggered two dicer-like proteins.</p>
</sec>
<sec><title>Comparison of distinct expression patterns of miRNAs and their targets during fruit development of MT and WT</title>
<p>The expression patterns of the miRNAs (including four known miRNAs and four novel miRNAs) that showed differential expression between MT and WT or high expression levels during fruit ripening were validated and compared by stem-loop qRT-PCR in the fruit pulp of MT and WT during six developmental stages (50, 80, 120, 155, 180, and 220 DAF). As shown in Figure <xref ref-type="fig" rid="F4">4</xref>
, these eight miRNAs all showed differential expression patterns between MT and WT. Csi-miR156k was remarkably up-regulated after the 155 DAF stage in WT; however, it was dramatically down-regulated in MT after 155 DAF. The expression patterns of csi-miR159 differed in MT and WT before the 120 DAF stage. Notably, csi-miR166d displayed opposing expression in MT and WT. Csi-miRN03 and csi-miRN11, two novel miRNAs, had low CT values (data not shown) and were highly expressed in the fruits of WT and MT (Table <xref ref-type="supplementary-material" rid="SM8">S2</xref>
). They also showed distinct expression patterns between MT and WT.</p>
<fig id="F4" position="float"><label>Figure 4</label>
<caption><p><bold>Expression patterns of several candidate miRNAs in MT and WT during fruit development and ripening</bold>
. The relative expression levels were obtained from stem-loop qRT-PCR.</p>
</caption>
<graphic xlink:href="fpls-07-01416-g0004"></graphic>
</fig>
<p>To validate the regulatory function of the miRNAs, the expression patterns of 18 target genes of several candidate miRNAs were also analyzed by qRT-PCR in the fruit pulp of MT and WT during six developmental stages (Figures <xref ref-type="fig" rid="F5">5</xref>
, <xref ref-type="fig" rid="F6">6</xref>
). In our results, 14 of the18 target genes displayed different expression patterns between MT and WT; <italic>SPL6, GAMYB</italic>
 (Cs3g06390), <italic>ATHB14</italic>
, and <italic>ATHB8</italic>
 showed similar expression patterns between MT and WT; and the expressions of most target genes were negatively correlated with their miRNAs. These results indicated that the miRNAs had significant effects on the expression of their target genes, which were also regulated by other factors, such as transcription factor. As shown in Figure <xref ref-type="fig" rid="F5">5A</xref>
, the four <italic>SPL</italic>
 genes targeted by csi-miR156k showed different expression patterns during citrus fruit development; in WT, the expressions of <italic>SPL4</italic>
 and <italic>SPL6</italic>
 were negatively correlated with that of csi-miR156k. However, in MT, the expression of csi-miR156k was negatively correlated with those of <italic>SPL2</italic>
 and <italic>SPL12</italic>
, indicating csi-miR156k regulated the expression of different genes in MT and WT. This phenomenon was also observed in other miRNA-targets groups (Figures <xref ref-type="fig" rid="F5">5</xref>
, <xref ref-type="fig" rid="F6">6</xref>
). These results indicated that miRNA-mediated silencing of target genes was under sophisticated regulation and within limits in citrus fruit.</p>
<fig id="F5" position="float"><label>Figure 5</label>
<caption><p><bold>Expression patterns of known miRNAs and their target genes in MT and WT during fruit development and ripening</bold>
. <bold>(A)</bold>
, csi-miR156k; <bold>(B)</bold>
, csi-miR159; <bold>(C)</bold>
, csi-166d.</p>
</caption>
<graphic xlink:href="fpls-07-01416-g0005"></graphic>
</fig>
<fig id="F6" position="float"><label>Figure 6</label>
<caption><p><bold>Expression patterns of novel miRNAs and their target genes in MT and WT during fruit development and ripening</bold>
. <bold>(A)</bold>
, csi-miRN12; <bold>(B)</bold>
, csi-miRN21.</p>
</caption>
<graphic xlink:href="fpls-07-01416-g0006"></graphic>
</fig>
</sec>
</sec>
<sec sec-type="discussion" id="s4"><title>Discussion</title>
<sec><title>Identification and characterization of miRNAs and their targets in citrus fruits</title>
<p>In recent years, more and more miRNAs have been excavated in citrus (Xu et al., <xref rid="B68" ref-type="bibr">2010</xref>
; Zhang et al., <xref rid="B74" ref-type="bibr">2012</xref>
; Liu Y. et al., <xref rid="B35" ref-type="bibr">2014</xref>
; Wu et al., <xref rid="B60" ref-type="bibr">2015</xref>
). To date, 60 miRNAs from <italic>C. sinensis</italic>
 have been annotated in the miRBase database (release 21.0). However, there are 325 and 592 miRNAs uploaded to the miRBase database (release 21.0) from <italic>Arabidopsis</italic>
 and rice, respectively. Therefore, there are still a large number of novel miRNAs that needed to be identified in citrus. In this study, we identified 107 known miRNAs belonging to 53 families and 21 novel miRNAs from the fruit of navel orange (Tables <xref ref-type="supplementary-material" rid="SM7">S1</xref>
, <xref ref-type="supplementary-material" rid="SM8">S2</xref>
). Our study predominantly focused on the annotation of miRNAs in citrus fruit based on a later-ripening mutant (“Fengwan”) and its wild-type (“Fengjie 72-1”), with the aim of broadening our understanding of the regulation networks of miRNAs in citrus fruit ripening. To obtain high confidence miRNAs, two biological replicates of each sample were submitted for deep sequencing, and only the miRNAs detected in both two biological replicates were used. According to the results of distribution of different size smallRNAs (Figure <xref ref-type="fig" rid="F1">1</xref>
) and amount of known and novel miRNAs identified in MT and WT (Figure <xref ref-type="supplementary-material" rid="SM1">S1</xref>
), we found that the kinds of miRNAs did not show significant difference between MT and WT. Seventy-five percent (96/128) miRNAs were the same in MT and WT. Interestingly, two novel miRNAs (csi-miRN03 and csi-miRN11) were expressed at much higher levels than the known miRNAs in both MT and WT, indicating their importance in fruit ripening (Table <xref ref-type="supplementary-material" rid="SM8">S2</xref>
).</p>
<p>Among the identified miRNAs, we found several differentially expressed miRNAs between MT and WT under a criterion (|log<sub>2</sub>
(MT/WT)| ≥ 1 and prob. > 0.8). Only 14.95% (16/107) of the known miRNAs were differentially expressed between MT and WT; however, the ratio of differentially expressed novel miRNAs (38.10%) was much higher than that of known miRNAs. These results suggested that the known/conserved miRNAs were probably responsible for controlling the basic cellular and developmental processes, while the novel/non-conserved miRNAs were involved in the regulation of the specific or species-specific regulatory pathways and functions (Glazov et al., <xref rid="B21" ref-type="bibr">2008</xref>
).</p>
<p>Degradome sequencing or PARE has been applied to identify miRNA targets in many plants (Addo-Quaye et al., <xref rid="B2" ref-type="bibr">2008</xref>
; German et al., <xref rid="B20" ref-type="bibr">2008</xref>
; Liu, N. A. et al., <xref rid="B34" ref-type="bibr">2014</xref>
; Wu et al., <xref rid="B60" ref-type="bibr">2015</xref>
; Xia et al., <xref rid="B62" ref-type="bibr">2015b</xref>
). This method identifies authentic miRNA targets in a high-throughput manner. The RLM-RACE was generally used to evaluate the degradome results in many studies (Yang, X. et al., <xref rid="B71" ref-type="bibr">2013</xref>
; Liu, N. A. et al., <xref rid="B34" ref-type="bibr">2014</xref>
; Liu Y. et al., <xref rid="B35" ref-type="bibr">2014</xref>
) and showed that the miRNA targets identified by degradome sequencing were reliable. In addition, this approach can concurrently identify all cleavage products in the genome with a high sensitivity, compared with older techniques such as northern blotting or 5′ RACE (Xia et al., <xref rid="B62" ref-type="bibr">2015b</xref>
). In this study, we performed degradome sequencing and identified 225 target genes of 57 miRNAs (47 known miRNAs and 10 novel miRNAs; Table <xref ref-type="supplementary-material" rid="SM9">S3</xref>
). However, there are still 60 known miRNAs and 11 novel miRNAs without identified targets in our results. There are two possible reasons for this result. For the miRNAs with low expression, the abundance of their cleaved targets might be too low to detect; for those miRNAs with high expression, they may primarily function by repressing translation of their targets.</p>
<p>Based on the results of this study, we generated an interaction diagram (Figure <xref ref-type="fig" rid="F7">7</xref>
), which showed that the miRNAs may play important roles during citrus fruit development and ripening. Among these, csi-miR159-<italic>GAMYBs</italic>
 module may play a central role in citrus fruit ripening, and this regulation may function in combination with plant hormones, including ABA, gibberellin (GA), and ethylene (ETH). These data depict a picture of the regulation network involved in citrus fruit ripening at post-transcription level.</p>
<fig id="F7" position="float"><label>Figure 7</label>
<caption><p><bold>A schematic model with the proposed roles of miRNAs involved in citrus fruit development and ripening</bold>
. ABA, abscisic acid; GA, gibberellic acid; ETH, ethylene.</p>
</caption>
<graphic xlink:href="fpls-07-01416-g0007"></graphic>
</fig>
</sec>
<sec><title>The stress response may play a significant role during citrus fruit ripening</title>
<p>According to the GO enrichment analysis of the targets of miRNAs, the biological processes related to the stress response were significant enriched, such as the innate immune response, salicylic acid biosynthetic process, response to stimulus, and response to stress (Figure <xref ref-type="fig" rid="F3">3</xref>
 and Table <xref ref-type="supplementary-material" rid="SM11">S5</xref>
). In addition, most of identified <italic>PHAS</italic>
 genes encoded resistance proteins (Table <xref ref-type="supplementary-material" rid="SM12">S6</xref>
). The abundantly expressed miR482 family triggered a massive release of phasiRNAs from transcripts of disease resistance proteins which included many <italic>NB-LRR</italic>
 genes (Table <xref ref-type="supplementary-material" rid="SM13">S7</xref>
). <italic>NB-LRR</italic>
 genes are one of the first lines of defense against pathogen infection (Dangl and Jones, <xref rid="B16" ref-type="bibr">2001</xref>
; Meyers et al., <xref rid="B40" ref-type="bibr">2005</xref>
; Yang,S. H. et al., <xref rid="B70" ref-type="bibr">2013</xref>
). The miR482/miR2118 superfamily, a significant trigger of the <italic>NB-LRR</italic>
 phasiRNAs, serves as the master regulator of <italic>NB-LRR</italic>
 genes in many eudicots by targeting the region coding for the critical P-loop motif in the highly conserved NBS (nucleotide-binding site) domain (Zhai et al., <xref rid="B73" ref-type="bibr">2011</xref>
; Li et al., <xref rid="B32" ref-type="bibr">2012</xref>
). This miRNA mediated regulation spawns secondary phasiRNAs, a layer of control with potential roles in defense or symbiosis (Zhai et al., <xref rid="B73" ref-type="bibr">2011</xref>
). In our previous studies (Wu et al., <xref rid="B59" ref-type="bibr">2014b</xref>
; Zhang et al., <xref rid="B76" ref-type="bibr">2014</xref>
), several differentially expressed genes and proteins related to the stress response were also identified during citrus fruit ripening. For instance, seven differential abundance heat shock proteins and 11 differential expression heat shock protein transcripts were identified between “Fengjie 72-1” and “Fengwan” during fruit ripening in proteomic data and transcriptomic data, respectively (Wu et al., <xref rid="B59" ref-type="bibr">2014b</xref>
). In addition, several genes related to ascorbate and aldarate metabolism and jasmonic acid metabolism, which are involved in the stress response, were also identified to be differential expressed between citrus wild type and late-ripening mutant and/or among different development stages of citrus fruit (Wu et al., <xref rid="B59" ref-type="bibr">2014b</xref>
; Zhang et al., <xref rid="B76" ref-type="bibr">2014</xref>
). ABA was shown to be a significant regulator during fruit ripening in both climacteric and non-climacteric fruits and is also a significant regulator of the stress response (Zhang et al., <xref rid="B75" ref-type="bibr">2009</xref>
; Jia et al., <xref rid="B25" ref-type="bibr">2011</xref>
, <xref rid="B26" ref-type="bibr">2013</xref>
; Leng et al., <xref rid="B31" ref-type="bibr">2014</xref>
; Luo et al., <xref rid="B36" ref-type="bibr">2014</xref>
). Therefore, we proposed that the stress response process may play an important role during citrus fruit ripening, in which ABA may act as a junction between the stress response and the ripening process (Figure <xref ref-type="fig" rid="F7">7</xref>
).</p>
</sec>
<sec><title>Regulation networks of miRNAs in the ripening of citrus fruit</title>
<p>miR156 is a highly conserved and expressed miRNA family in the plant kingdom and has been shown to take part in the regulation of flower and fruit development by targeting the SQUAMOSA-promoter binding-like (<italic>SPL</italic>
) family (Xing et al., <xref rid="B66" ref-type="bibr">2013</xref>
; Silva et al., <xref rid="B49" ref-type="bibr">2014</xref>
; Wang, <xref rid="B56" ref-type="bibr">2014</xref>
). In the present study, the miR156 family was highly expressed in citrus fruit, and csi-miR156k was significantly different between MT and WT (Figure <xref ref-type="fig" rid="F4">4</xref>
 and Table <xref ref-type="supplementary-material" rid="SM7">S1</xref>
). In <italic>Arabidopsis</italic>
, miR156-targeted <italic>SPL3</italic>
 positively and directly regulates the MADS box genes <italic>AP1, FUL</italic>
, and the central regulator of flowering <italic>LEAFY</italic>
 (Yamaguchi et al., <xref rid="B69" ref-type="bibr">2009</xref>
). Interestingly, <italic>FUL</italic>
 is a well-characterized regulator of cell differentiation during the early stages of <italic>Arabidopsis</italic>
 fruit development and <italic>FUL-like</italic>
 genes appear to play a role in fruit development in two basal eudicot <italic>Papaveraceae</italic>
 species by promoting normal development of the fruit wall during fruit maturation (Gu et al., <xref rid="B23" ref-type="bibr">1998</xref>
; Pabón-Mora et al., <xref rid="B47" ref-type="bibr">2012</xref>
). In tomato, the miR156-targeted <italic>SlySBP</italic>
 gene <italic>CNR</italic>
 (a <italic>SPL</italic>
 family member) acts as a crucial factor controlling fruit ripening (Manning et al., <xref rid="B38" ref-type="bibr">2006</xref>
). Over-expression of the <italic>AtMIR156b</italic>
 precursor in tomato cv. Micro-Tom led to the down-regulation of most <italic>SlySBP</italic>
 genes in the developing ovaries and an alteration of morphology, with fruits characterized extra carpels and ectopic structures (Silva et al., <xref rid="B49" ref-type="bibr">2014</xref>
). In the present study, four csi-miR156k-targeted <italic>SPL</italic>
 genes were identified, and the expression patterns of these genes were different between MT and WT during fruit development and ripening (Figure <xref ref-type="fig" rid="F5">5A</xref>
). These results indicated that miR156 might play an important role in citrus fruit ripening (Figure <xref ref-type="fig" rid="F7">7</xref>
).</p>
<p>In previous studies, miR159 was characterized to regulate the expressions of <italic>GAMYB-like</italic>
 genes at the post-transcriptional level and play significant roles in leaf, flower and seed maturation (Millar and Gubler, <xref rid="B41" ref-type="bibr">2005</xref>
; Tsuji et al., <xref rid="B54" ref-type="bibr">2006</xref>
; Reyes and Chua, <xref rid="B48" ref-type="bibr">2007</xref>
). In strawberry (<italic>Fragaria</italic>
 × <italic>ananassa</italic>
), transient silencing of <italic>FaGAMYB</italic>
 using RNAi caused an arrest in the ripening of the receptacle and inhibited color formation; in addition, a reduction of ABA biosynthesis and sucrose content was also caused by silencing <italic>FaGAMYB</italic>
 which act upstream of the known regulator ABA (Vallarino et al., <xref rid="B55" ref-type="bibr">2015</xref>
). During <italic>Arabidopsis</italic>
 seed germination, ABA induces the accumulation of miR159, and over-expression of miR159 renders plants hyposensitive to ABA (Reyes and Chua, <xref rid="B48" ref-type="bibr">2007</xref>
). The <italic>GAMYB</italic>
 gene in barley is upregulated by the GA transduction pathway in both anthers and seeds (Murray et al., <xref rid="B45" ref-type="bibr">2003</xref>
) and over-expression of miR159 or disruption of the GA biosynthesis pathway delays flowering and reduces fertility (Achard et al., <xref rid="B1" ref-type="bibr">2004</xref>
; Cheng et al., <xref rid="B13" ref-type="bibr">2004</xref>
). In tomato fruit, Zuo et al. (<xref rid="B78" ref-type="bibr">2012</xref>
) reported that the expression of miR159 is efficiently repressed by ethylene (ETH) treatment. Therefore, miR159, which regulates the spatiotemporal expression pattern of <italic>GAMYB</italic>
 genes, constitutes a major connection among at least three hormones—namely GA, ABA, and ETH (Curaba et al., <xref rid="B15" ref-type="bibr">2014</xref>
). In the present study, csi-miR159 was differentially expressed between MT and WT, and the targets of csi-miR159, including two <italic>GAMYBs</italic>
, Polygalacturonase inhibitor 1 and <italic>NOZZLE</italic>
, were also differentially expressed between MT and WT during fruit development and ripening (Table <xref ref-type="supplementary-material" rid="SM8">2</xref>
, Figures <xref ref-type="fig" rid="F4">4</xref>
, <xref ref-type="fig" rid="F5">5</xref>
). These results indicated that csi-miR159 may be an important regulator of citrus fruit development and ripening and may play a significant role in the formation of later-ripening trait in MT (Figure <xref ref-type="fig" rid="F7">7</xref>
).</p>
<p>Moreover, in the present study, csi-miR166d and its targets were all differentially expressed between MT and WT (Figures <xref ref-type="fig" rid="F4">4</xref>
, <xref ref-type="fig" rid="F5">5</xref>
). The targets of csi-miR166d included three <italic>ATHBs</italic>
 genes and one HD-ZIP III protein REVOLUTA which are involved in regulation of auxin signaling (Baima et al., <xref rid="B5" ref-type="bibr">2014</xref>
). In previous studies, miR166 was reported to be a critical factor for vascular development (Kim et al., <xref rid="B29" ref-type="bibr">2005</xref>
) and played a role in regulating SAM formation and floral development (Jung and Park, <xref rid="B27" ref-type="bibr">2007</xref>
). Here, csi-miR166d might be involved in the regulation of citrus fruit development through interacting with auxin signaling pathway (Figure <xref ref-type="fig" rid="F7">7</xref>
).</p>
<p>In conclusion, numerous miRNAs are expressed during citrus fruit ripening and are differentially regulated between MT and WT. Several significant miRNAs and targets were identified in this study, such as csi-miR156k, csi-miR159, csi-miR166d, csi-miRN21, <italic>GAMYBs, SPLs</italic>
, and <italic>ATHBs</italic>
. The identification of miRNAs and their target genes provide new clues for future investigation of the mechanisms that regulate citrus fruit ripening.</p>
</sec>
</sec>
<sec id="s5"><title>Author contributions</title>
<p>Conceived and designed the experiments: HY, JW. Performed the experiments: JW, SZ, and GF. Analyzed the data: JW. Contributed reagents/materials/analysis tools: HY. Wrote the paper: JW, HY.</p>
<sec><title>Conflict of interest statement</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
</sec>
</body>
<back><ack><p>This research was supported by the National Modern Citrus Industry System (CARS-27), the Ministry of Education Innovation Team (IRT13065), the National Science and Technology Support Project (2013BAD021302), the China Postdoctoral Science Foundation (2015M582242), and the Special Project on the Integration of Industry, Education and Research of Guangdong Province (2012B091100169). We thank the Faculty of Navel Orange Bureau of Fengjie, Chongqin of China for material collection.</p>
</ack>
<sec sec-type="supplementary-material" id="s6"><title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="http://journal.frontiersin.org/article/10.3389/fpls.2016.01416">http://journal.frontiersin.org/article/10.3389/fpls.2016.01416</ext-link>
</p>
<supplementary-material content-type="local-data" id="SM1"><label>Figure S1</label>
<caption><p><bold>The venn diagram of the number of known miRNAs (A) and novel miRNAs (B) identified in MT and WT</bold>
.</p>
</caption>
<media xlink:href="Image1.TIF"><caption><p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
<supplementary-material content-type="local-data" id="SM2"><label>Figure S2</label>
<caption><p><bold>The distribution of pre-miRNAs in the <italic><bold>Citrus sinensis</bold>
</italic>
 genome. Precursors of novel miRNAs are shown in pink</bold>
. The chromosome/scaffold scale is shown at the left of the figure. The chromosomal locations of the pre-miRNAs are indicated.</p>
</caption>
<media xlink:href="Image2.TIF"><caption><p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
<supplementary-material content-type="local-data" id="SM3"><label>Figure S3</label>
<caption><p><bold>Predicted secondary structures of the known miRNAs</bold>
. The mature miRNA sequences are highlighted in blue and the miRNA<sup>*</sup>
 sequences are highlighted in red.</p>
</caption>
<media xlink:href="Image3.PDF"><caption><p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
<supplementary-material content-type="local-data" id="SM4"><label>Figure S4</label>
<caption><p><bold>Predicted secondary structures of the novel miRNAs</bold>
. The mature miRNA sequences are highlighted in blue and the miRNA<sup>*</sup>
 sequences are highlighted in red.</p>
</caption>
<media xlink:href="Image4.PDF"><caption><p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
<supplementary-material content-type="local-data" id="SM5"><label>Figure S5</label>
<caption><p><bold>T-plots of the miRNA targets</bold>
.</p>
</caption>
<media xlink:href="Image5.PDF"><caption><p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
<supplementary-material content-type="local-data" id="SM6"><label>Figure S6</label>
<caption><p><bold>Distribution of the identified miRNAs and their targets in the <italic><bold>Citrus sinensis</bold>
</italic>
 genome</bold>
. The arrows indicate target genes.</p>
</caption>
<media xlink:href="Image6.TIF"><caption><p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
<supplementary-material content-type="local-data" id="SM7"><label>Table S1</label>
<caption><p><bold>Summary of the known miRNAs in WT and MT</bold>
.</p>
</caption>
<media xlink:href="Table1.XLSX"><caption><p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
<supplementary-material content-type="local-data" id="SM8"><label>Table S2</label>
<caption><p><bold>Summary of the novel miRNAs in WT and MT</bold>
.</p>
</caption>
<media xlink:href="Table2.XLSX"><caption><p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
<supplementary-material content-type="local-data" id="SM9"><label>Table S3</label>
<caption><p><bold>Targets of the miRNAs identified using degradome sequencing</bold>
.</p>
</caption>
<media xlink:href="Table3.XLSX"><caption><p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
<supplementary-material content-type="local-data" id="SM10"><label>Table S4</label>
<caption><p><bold>Annotations of all target genes</bold>
.</p>
</caption>
<media xlink:href="Table4.XLSX"><caption><p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
<supplementary-material content-type="local-data" id="SM11"><label>Table S5</label>
<caption><p><bold>Gene ontology classification of miRNA targets</bold>
.</p>
</caption>
<media xlink:href="Table5.XLSX"><caption><p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
<supplementary-material content-type="local-data" id="SM12"><label>Table S6</label>
<caption><p><bold><italic><bold>PHAS</bold>
</italic>
 genes and phasiRNAs identified in MT and WT</bold>
.</p>
</caption>
<media xlink:href="Table6.XLSX"><caption><p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
<supplementary-material content-type="local-data" id="SM13"><label>Table S7</label>
<caption><p><bold>Identified <italic><bold>PHAS</bold>
</italic>
 genes in citrus fruit</bold>
.</p>
</caption>
<media xlink:href="Table7.docx"><caption><p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
<supplementary-material content-type="local-data" id="SM14"><label>Table S8</label>
<caption><p><bold>Primers used in miRNA stem-loop RT-PCR and mRNA qRT-PCR</bold>
.</p>
</caption>
<media xlink:href="Table8.xlsx"><caption><p>Click here for additional data file.</p>
</caption>
</media>
</supplementary-material>
</sec>
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<glossary><def-list><title>Abbreviations</title>
<def-item><term>DAF</term>
<def><p>Days after flowering</p>
</def>
</def-item>
<def-item><term>MT_bio1/MT_bio2</term>
<def><p>Biological replicate 1/2 of MT</p>
</def>
</def-item>
<def-item><term>WT_bio1/WT_bio2</term>
<def><p>Biological replicate 1/2 of WT</p>
</def>
</def-item>
<def-item><term>phasiRNA</term>
<def><p>Phased secondary small interfering RNA</p>
</def>
</def-item>
<def-item><term><italic>PHAS</italic>
 gene</term>
<def><p>The gene targeted by miRNAs to generate phasiRNAs</p>
</def>
</def-item>
<def-item><term>pre-miRNA</term>
<def><p>Precursor of miRNA</p>
</def>
</def-item>
<def-item><term>tasiRNA</term>
<def><p><italic>Trans</italic>
-acting siRNA</p>
</def>
</def-item>
<def-item><term>qRT-PCR</term>
<def><p>Quantitative real time transcription-polymerase chain reaction.</p>
</def>
</def-item>
</def-list>
</glossary>
</back>
</pmc>
</record>

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Comparative Analysis of miRNAs and Their Target Transcripts between a Spontaneous Late-Ripening Sweet Orange Mutant and Its Wild-Type Using Small RNA and Degradome Sequencing

Comparative Analysis of miRNAs and Their Target Transcripts between a Spontaneous Late-Ripening Sweet Orange Mutant and Its Wild-Type Using Small RNA and Degradome Sequencing

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