Uncovering key small RNAs associated with gametocidal action in wheat

Uncovering key small RNAs associated with gametocidal action in wheat Abstract Gametocidal (Gc) chromosomes can kill gametes that lack them by causing chromosomal breakage to ensure their preferential transmission, and they have been exploited in genetic breeding. The present study investigated the possible roles of small RNAs (sRNAs) in Gc action. By sequencing two small RNA libraries from the anthers of Triticum aestivum cv. Chinese Spring (CS) and the Chinese Spring-Gc 3C chromosome monosomic addition line (CS-3C), we identified 239 conserved and 72 putative novel miRNAs, including 135 differentially expressed miRNAs. These miRNAs were predicted to target multiple genes with various molecular functions relevant to the features of Gc action, including sterility and genome instability. The transgenic overexpression of miRNA, which was up-regulated in CS-3C, reduced rice fertility. The CS-3C line exhibited a genome-wide reduction in 24 nt siRNAs compared with that of the CS line, particularly in transposable element (TE) and repetitive DNA sequences. Corresponding to this reduction, the bisulfite sequencing analysis of four retro-TE sequences showed a decrease in CHH methylation, typical of RNA-directed DNA methylation (RdDM). These results demonstrate that both miRNA-directed regulation of gene expression and siRNA-directed DNA methylation of target TE loci could play a role in Gc action. Chromosomal breakage, DNA methylation, fertility, gametocidal action, microRNA, siRNA, Triticum aestivum Introduction Gametocidal (Gc) genes, which are introduced into common wheat from related Aegilops species, are strong segregation distorters that affect plant fertility (Endo and Tsunewaki, 1975; Maan, 1975; Endo, 1990). Wheat lines that are heterozygous or hemizygous for a Gc chromosome can produce two types of gametes, one with and the other without the Gc chromosome, and only those with the Gc chromosome are functional, ensuring their preferential transmission (Endo and Tsunewaki, 1975; Maan, 1975; Endo, 1990). The gametes without the Gc chromosome are killed due to chromosomal aberrations induced by Gc chromosomes in the G1 phase prior to DNA synthesis of the first post-meiotic mitosis, including chromatid fragments, dicentric chromosomes, and chromatin bridges (Finch et al., 1984; King and Laurie, 1993; Nasuda et al., 1998). Consequently, the fertility of wheat lines with heterozygous or hemizygous Gc chromosomes declines. This genetic phenomenon is of great interest to breeders, who use the Gc chromosome as an effective breeding tool. Breeders have used the Gc chromosome to produce chromosome deletion stocks in common wheat and to induce rearrangement of alien chromosomes added to common wheat, which are highly significant for mapping genes and molecular markers on wheat chromosomes and for breeding high-quality wheat using the genetic resources of wheat and its related species (Endo et al., 1994; Ogihara et al., 1994; Endo and Gill, 1996; Shi and Endo, 1997). However, currently, very little is known about the molecular mechanisms underlying the action of Gc chromosomes. Several hypotheses have been proposed for the mode of action of the Gc locus, including the dual-function model, in which the Gc genes have both ‘breaking’ and ‘protecting’ functions (Endo, 1990; Tsujimoto, 2005). Tsujimoto (2005) proposed that the Gc genes produce both restriction enzymes (REs) and modification enzymes (MEs), such as methylase. The REs cleave specific restriction sites unless the sites are modified by the MEs (Endo, 1990; Tsujimoto, 2005). This model explains the dual function of Gc chromosomes. However, the study did not provide compelling experimental evidence to support it. Furthermore, studies have reported that DNA methylation can repress chromosome breakages induced by the Gc factors (de Las Heras et al., 2001; Su et al., 2013). Recently, small non-coding RNAs have emerged as important regulators of gene expression and genome stability in eukaryotes that can direct both transcriptional and post-transcriptional gene silencing (Carthew and Sontheimer, 2009). Plant endogenous small RNAs (sRNAs) can be categorized into several classes, with the most prominent being the miRNAs and siRNAs (Eamens et al., 2008). Most plant miRNAs are derived from primary miRNA transcripts (pri-miRNA) transcribed by RNA polymerase II, which form short stem–loop structures that are processed by Dicer-like 1 to generate mature 20–24 nt miRNA (Lee et al., 2004; Kim, 2005; Kurihara et al., 2006; Eamens et al., 2008; Voinnet, 2009). The mature miRNAs are loaded into the protein factor Argonaute to form RNA-induced silencing complexes in order to cleave target mRNA molecules or repress their translation (Baumberger and Baulcombe, 2005; Brodersen et al., 2008; Tang et al., 2012). The miRNAs participate in various aspects of plant development and in a series of biological processes, including RNA-directed DNA methylation (RdDM) (Rhoades et al., 2002; Liu et al., 2008; Wu et al., 2010; Zhang and Wang, 2015). Increasing evidence suggests that miRNAs may also participate in the development of the anther to regulate male sterility, as reported in wheat, rice, cotton, and maize (Shen et al., 2011; Tang et al., 2012; Wei et al., 2013; Yan et al., 2015; Zhai et al., 2015). In animals, the miRNAs have been implicated in regulating the DNA damage response (Pothof et al., 2009a; Hu and Gatti, 2010; Wan et al., 2011; Wang and Taniguchi, 2013). For example, miR-138 and miR-24 in human tumor cells modulate the DNA damage response by suppressing the expression of histone H2AX (Lal et al., 2009; Wang et al., 2011). The miRNA can also modulate the UV-induced DNA damage response and DNA repair gene expression in HeLa cells under hypoxic stress (Crosby et al., 2009; Pothof et al., 2009b). Wilting et al. (2013) reported that altered expression of miRNAs in a human cervical carcinoma cell line is associated with frequent chromosomal alterations (Wilting et al., 2013). Moreover, miR-34a can increase the frequency of chromosome breaks and disturb mitotic progression in irradiated cells (Kofman et al., 2013). However, the role of miRNAs in DNA damage repair in plants remains unclear. Among the several classes of siRNAs, the 24 nt size class is unique as it directs RdDM, a plant-specific transcriptional gene silencing mechanism (Haag and Pikaard, 2011; Zhang and Zhu, 2011; Ng et al., 2012). Therefore, the 24 nt siRNAs play an important role in genome reconstruction and stabilization by maintaining transcriptional silencing of transposable elements (TEs) and repetitive sequences in the genome (Ha et al., 2009; Ghani et al., 2014). Furthermore, the majority of endogenous plant siRNAs are 24 nt long, and are primarily produced from the TEs and DNA repeats (Kasschau et al., 2007; Liu et al., 2014). In the newly synthesized hexaploid wheat, the number of 24 nt siRNAs corresponding to the TEs was significantly reduced compared with its parental tetraploid and diploid wheat, and this was accompanied by a decrease in CpG methylation, which may have contributed to genomic instability in the allopolyploids during the early stage of their formation (Kenan-Eichler et al., 2011). Similar to that of the 24 nt siRNAs in plants, piRNAs in Drosophila may participate in hybrid dysgenesis (Brennecke et al., 2008). The function of sRNAs in genome stability and gene expression made us assume that they might also participate in Gc action. To investigate this hypothesis, two sRNA libraries were constructed from Triticum aestivum cv. Chinese Spring (CS) and the Chinese Spring-Gc 3C chromosome monosomic addition line (CS-3C), and sequenced by the Illumina high-throughput sequencing technology. The bioinformatic analysis of the sequencing data, followed by quantitative real-time PCR (qRT-PCR) analysis of miRNA expression and bisulfite sequencing analysis of DNA methylation uncovered both miRNA-directed regulation of target gene expression and siRNA-directed DNA methylation of target TE loci that could play a role in Gc action. The present study has opened up a new avenue for uncovering the underlying molecular mechanism of Gc action during the development of the wheat anther. Materials and methods Plant materials and growth conditions Triticum aestivum cv. Chinese Spring (CS, AABBDD, 2n=42) and the Chinese Spring-gametocidal 3C chromosome monosomic addition line (CS-3C, AABBDD+3C I, 2n=43) that carries a gametocidal chromosome 3C derived from Aegilops triuncialis were used. Seed of the Chinese Spring-gametocidal 3C chromosome disomic addition line (CS-3C3C, AABBDD+3C II, 2n=44) that carries two gametocidal chromosome 3Cs was kindly provided by Endo (National BioResource Project-Wheat, Japan), and seeds of CS-3C were obtained from crosses between CS (female parent) and CS-3C3C (male parent). Seeds of CS and CS-3C were planted in a 1:1:1 (w/w/w) mixture of peat, sand, and soil, and were kept in a greenhouse at 18–20 °C with a 16 h/8 h light/dark photoperiod. Cytological observations and sample collection Anthers whose pollen was in the interphase prior to the first mitotic cell division were selected after staining with 1% acetocarmine. Slides were prepared using the acetocarmine squash method and they were viewed with a Leica DM 750 microscope. For RNA and DNA analysis, anthers were immediately frozen in liquid nitrogen after collection, and stored at –80 °C. RNA isolation and Illumina sequencing Both samples were harvested from two independent biological replicates. Total RNA was isolated from pooled anthers using TRIZOL reagent (Invitrogen, CA, USA) according to the manufacturer’s instructions, and the quantity and quality of total RNA were measured using an Agilent 2100 Bioanalyzer. RNA samples were sent to BGI (BGI-Shenzhen, Shenzhen, China) for sRNA sequencing using an Illumina Genome Analyzer. Preliminary processing of sequencing reads Raw sequence reads were processed into clean reads after filtering out low-quality reads, trimming the adaptor sequences, and removing other noise reads. To identify miRNAs, the reads that were mapped to the known non-coding RNAs (rRNA, tRNA, snRNA, etc.) in the Rfam database were discarded. The remaining reads were mapped to the A, B, and D genome using the BOWTIE2 software with the Triticum_aestivum.IWGSC2.26 (http://ensemblgenomes.org/) as the reference genome. Mapped reads were further analyzed to identify conserved and novel miRNAs. For siRNAs, all the 24 nt sRNA reads were extracted for further analysis. Identification of conserved and novel miRNAs To identify conserved miRNAs, mapping reads were retrieved and aligned using BLASTN against known plant mature miRNA sequences in miRBase V21 (Kozomara and Griffiths-Jones, 2014), and sRNA reads showing identical sequences to the known miRNAs were selected as the conserved miRNAs. For identification of novel miRNAs, the remaining reads were aligned to the wheat reference genome. Flanking sequences of perfectly matched reads plus the sRNA sequence were extracted using BOWTIE2 and MIREAP, and pre-miRNA-like secondary structures were predicted using RNAFOLD (Denman, 1993). If the sequences were predicted to form a stem–loop structure, the sRNAs were considered as putative novel miRNAs. miRNA abundance was evaluated and normalized using the tags per million reads (TPM) method, based on BLASTN mapping results. The miRNA expression fold changes between CS-3C and CS (CS-3C/CS) were computed and the miRNAs with fold change (TPM ratios) ≥1.5 or ≤0.67 were screened as differentially expressed miRNAs. Identification and differential expression analysis of siRNA After removing the 24 nt miRNAs from the 24 nt reads, the remaining 24 nt sRNA reads were mapped to the A, B, and D genomes using BOWTIE2 with the Triticum_aestivum.IWGSC2.26 as the reference genome. We then used the mapped siRNA reads within a 1 kb sliding genome window to examine the distribution and the densities of siRNA. Differential siRNA sliding windows (clustered siRNA locus) were then identified that contained a minimum of 20 siRNA reads in a 1 kb region in either library with fold change (FC) ≥1.5 or ≤0.67 between CS-3C and CS. Annotation of genomic loci containing differential siRNAs was carried out by aligning the siRNA-associated sequences with the Triticum_aestivum.IWGSC2.26.chromosome.gff3 database. The distribution patterns of siRNAs in the 3 kb up- and downstream regions as well as the transcribed regions of genes were studied. Gene Ontology (GO) analysis was then conducted on genes with differentially expressed siRNAs in the transcribed and 1 kb upstream regions (with a minimum of 10 siRNA reads in either library and FC ≥1.5 or ≤0.67), using the web-based tool and database AgriGO (http://bioinfo.cau.edu.cn/agriGO/). GO terms with corrected false discovery rate (FDR) <0.05 were considered significantly enriched. TaqMan assay for miRNA validation We used the Applied Biosystems TaqMan miRNA assays to detect and quantify the miRNAs according to the manufacturer’s protocol, with three technical replicates and two independent biological replicates for each plant line (Chen et al., 2005). The small nuclear RNA U6 was used as the internal reference gene for normalization. Briefly, 5.0 μl of total RNA was incubated with 7.0 μl of RT-MIX including 1.5 μl of 10× Reverse Transcription buffer, 0.15 μl of deoxyribonucleotide triphosphates (100 mM, with dTTP), 0.19 μl of RNase inhibitor (20 U μl−1), 1 μl of MultiScribe™ reverse transcriptase, and 4.16 μl of nuclease-free water, then 3 μl of 5× stem–loop reverse transcription primer (Applied Biosystems) was added to the mix to set up a 15 μl reaction mixture. Real-time PCR for each assay contained 1 μl of cDNA, 10 μl of TaqMan® 2× Universal PCR master mix II (No AmpErase® UNG), 1 μl of 20× TaqMan MicroRNA assay mix including miRNA-specific primers and the TaqMan probe, and 8 μl of nuclease-free water. The reaction was performed in an Applied Biosystems (USA) 7500Fast Real-Time PCR System on 96-well plates at 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 60 s. Target mRNA prediction and GO function analysis of targets The web-based psRNATarget program (http://plantgrn.noble.org/psRNATarget/) was used to identify putative targets for differentially expressed miRNAs with default parameters. The analysis was performed using two different transcript libraries for target search: Triticum aestivum unigenes from DFCI Gene Index (TAGI) version 12 and T. aestivum cDNA from EbsemblPlants. GO analysis of the putative targets predicted from the wheat cDNA from EbsemblPlants was conducted using AgriGO, with a corrected FDR <0.05 as an indication of significant enrichment. Validation of predicted target genes by real-time reverse transcription–PCR (RT–PCR) Briefly, first-strand cDNA synthesis was performed by using a ReverTra Ace qPCR RT kit (Toyobo Life Science, Shanghai, China) according to the manufacturer’s instructions. Real-time PCR was carried out using SYBR Green and monitored on an ABI 7300 Real-Time PCR System (Applied Biosystems). TaGAPDH was used as a reference gene for each sample, and the real-time PCR mixture was performed in technical triplicate. For each qRT-PCR, 1.4 μl of cDNA was used in a 20.0 μl PCR system, containing 0.6 μl of each primer (10 μM), 7.0 μl of dH2O, 10.0 μl of SYBR® qPCR Mix, and 0.4 μl of 50× ROX reference dye (Toyobo Life Science), and a dissociation stage was generated by the software to analyze the melting curve. Validation of miRNA-guided cleavage of target mRNA by 5' RACE Target mRNA was validated by a modified version of 5' RACE (Llave et al., 2002) using the 5'-Full RACE Kit (Takara, Dalian, China). By using the 5' RACE primer provided in the kit, and gene-specific primers (Supplementary Table S9 at JXB online) designed by primer 5.0, the cleavage products were detected by nested PCR. The cleavage products were cloned into the pMD-18T vector (Takara) and sequenced. Preparation of the Ubi:pre-miR9657b-3p construct and rice transformation A 207 bp genomic sequence containing the pre-miR9657b-3p fold-back region was amplified from wheat genomic DNA by PCR. A modified binary vector, pCAMBIA-1305.1 (pOx) (provided by Yaoguang Liu), which carries a maize ubiquitin (Ubi) promoter, was used to generate a construct for overexpression of pre-miR9657b-3p. Subsequently, the recombinant expression plasmids were introduced into Agrobacterium tumefaciens strain EHA105 and used for rice (Oryza sativa spp. japonica var. Nipponbare) transformation. Putative transgenic lines were selected for further confirmation by PCR. A highly sensitive real-time stem–loop RT–PCR procedure was used to investigate the expression of the mature miR9657b-3p. qRT-PCR was also performed to measure the expression abundance of the target gene (TC492017) of miR9657b-3p in rice. RNA isolation and quantitative real-time RT–PCR were conducted in the same way as described above. In order to study the effect of tae-miR9657b-3p overexpression on plant fertility, we analyzed morphological traits of transgenic rice plants. The number of spikelets per panicle, seed setting percentage, and filled grain number were measured. The pollen germination rate was determined by visualization using a Leica DM 750 microscope (Leica). The collected fresh mature pollen was incubated at 28–30 °C. A liquid germination medium [20% sucrose, 10% PEG4000, 3.0 mmol l−1 Ca (NO3)2, 3.0 mg l−1 vitamin B1, and 40 mg l−1 H3BO3] was used for rice pollen germination using an optimized procedure with 30–40 min culture time. Bisulfite sequencing analysis Wheat DNA was extracted using the DNAsecure Plant Kit (TIANGEN, Beijing, China). DNA bisulfite modification was performed by using the EZ DNA Methylation-Gold™ kit (Zymo Research, CA, USA) according to the manufacturer’s instructions. PCR and nested PCR primers (degenerate primers) (Supplementarty Table S9) were designed by Primer 5.0, and the online software methprimer (http://www.urogene.org/cgi-bin/methprimer/methprimer.cgi) was used to predict CpG islands. PCR products were purified and directly sequenced as described in Le et al. (2014). Trace file data of the sequenced PCR products were opened using the BioEdit software (http://www.mbio.ncsu.edu/bioedit/bioedit.html) and exported to Microsoft Excel by using the ‘Export trace values (tab-delimited text)’ feature. The relative peak heights of cytosines and thymines were calculated to indicate the relative degree of methylation at each cytosine location, and the quality of bisulfite conversion was indicated by the PCR product of the wheat chloroplast psaA gene. Accession number All the sequencing data were uploaded to the Sequence Read Archive (SRA) database with accession no. SRP102555. Results Sequencing of sRNA libraries and data analysis Two sRNA libraries were constructed using the total RNA from CS and CS-3C wheat lines and were sequenced. After removing the adaptor sequences, poor-quality reads and those with inserts smaller than 18 nucleotides, 13481233 and 12699141 high-quality clean reads were obtained from CS and CS-3C, respectively (Supplementary Table S1). These included 70.11% reads that were common to both the samples, and 16.27% and 13.62% that were specific to CS and CS-3C, respectively (Supplementary Fig. S1a). Consistent with the findings of previous studies (Moxon et al., 2008; Hsieh et al., 2009), 24 nt (62.2%) and 21 nt (18.3%) sRNAs were the two most abundant sRNA size classes in the total clean sRNA reads (Supplementary Fig. S1b). Furthermore, the 24 nt class (76.4%) was the dominant species among the unique sRNA reads (SupplementaryFig. S1c). Collectively, the two libraries contained 9170210 unique reads (Supplementary Table S1). For 24 nt siRNAs, all reads were extracted, resulting in 8605226 and 7673544 high-quality clean 24 nt reads of the CS and CS-3C lines, respectively (Supplementary Table S1; Supplementary Fig. S1b). To identify the miRNAs, we first removed the sRNAs matching known non-coding RNAs (rRNA, tRNA, snRNA, snoRNA, and other ncRNAs); 366833 and 563185 total sRNAs of CS and CS-3C libraries were removed, respectively (Supplementary Table S1). The remaining sRNAs were mapped against the wheat reference genomic sequences (Triticum_aestivum.IWGSC2.26) using BOWTIE2, and the matching sRNA reads were analyzed to identify conserved and novel miRNAs. Identification of miRNAs To identify the miRNAs in the sequencing data, we downloaded miRNA sequences from the miRBase (version 21) (http://www.mirbase.org/) and conducted a local BLASTN search of known plant miRNAs with the sRNA reads matching the wheat reference genomic sequences. We identified 239 miRNAs, belonging to 115 miRNA families as described previously in other plant species (Supplementary Table S2). The read number for the majority of these miRNAs was relatively small in the sequencing data, possibly due to the low expression levels, tissue-specific expression, or relatively low coverage of the sequencing data. However, the expression levels of several miRNA families, such as miR167, miR156, and miR9863, were significantly high (Supplementary Table S2). The relative abundance of different members of miRNA families, such as the miR167 and miR156 families, also varied significantly (Supplementary Table S2). To identify novel miRNAs (nov-miRNAs), we removed the known miRNA reads from the wheat reference genome matching sRNAs, aligned the remaining unannotated reads to the wheat reference genomic sequences, and extracted the flanking genomic sequences of the perfectly matched reads using miRDEEP-P (Yang and Li, 2011). The sRNA sequences plus the extracted surrounding sequences were then used to predict the miRNA precursor-like stem–loop structures and putative miRNAs using the Mireap program with default plant parameters. Finally, 72 putative nov-miRNAs were predicted from the two libraries (Supplementary Table S3). Differential expression of conserved and novel miRNAs between CS-3C and CS Among the 311 miRNAs (including 239 conserved miRNAs and 72 nov-miRNAs), 135 miRNAs (43.4%) (including 31 nov-miRNAs) exhibited differential accumulation between CS-3C and CS, with a CS-3C to CS FC of ≥1.5 (up-regulation, 74 miRNAs) or ≤0.67 (down-regulation, 61 miRNAs; Table 1; Fig. 1a). The remaining 176 miRNAs (56.6%) were equally expressed between the two samples (Fig. 1a). Table 1. The differentially expressed miRNAs in gametocidal action miRNA family Name Sequences (5'–3') TPMa Log2 FCb Typical homology in miRBase CS CS-3C miR1120 miR1120a ACAUUCUUAUAUUAUGAGACGGAG 0.67 0.16 –2.08 tae-miR1120a miR1133 miR1133 CAUAUACUCCCUCCGUCCGAAA 0.82 1.50 0.87 tae-miR1133 miR1139 miR1139 GAGUAACAUACACUAGUAACA 0.89 1.57 0.82 bdi-miR1139 miR1508 miR1508c UAGAAAGGGAAAUAGCAGUUG 1.19 2.05 0.79 gma-miR1508c miR156 miR156 UUUGACAGAAGAUAGAGAGCAC 0.96 1.89 0.97 bcy-miR156 miR156s UGACAGAAGAGAGUGAGCACU 0.45 1.26 1.50 gma-miR156s miR156j UGACAGAAGAGGGUGAGCAC 0.37 1.26 1.76 mtr-miR156j miR159 miR159b-3p UUUGGAUUGAAGGGAGCUCUU 2.15 4.88 1.18 aly-miR159b-3p miR159a-3p CUUGGAUUGAAGGGAGCUCU 1.56 3.70 1.25 bdi-miR159a-3p miR159a UUUGGAUUGAAGGGAGCUCUG 147.24 283.80 0.95 tae-miR159a miR160 miR160c-3p GCGUGCAAGGAGCCAAGCAUG 0.89 2.13 1.26 ata-miR160c-3p miR160 UGCCUGGCUCCCUGUAUGCCA 3.04 0.63 –2.27 tae-miR160 miR160a-3p GCGUGCGAGGAGCCAAGCAUG 0.22 1.18 2.41 bdi-miR160a-3p miR164 miR164a-5p UGGAGAAGCAGGGCACGUGCU 3.86 1.97 –0.97 ata-miR164a-5p miR166 miR166d-5p GGAAUGUUGUCUGGCUCGGGG 5.19 10.71 1.04 ata-miR166d-5p miR166e-5p GGAAUGUUGUCUGGUUGGAGA 0.37 2.28 2.62 ata-miR166e-5p miR166e-3p CUCGGACCAGGCUUCAUUCCC 0.74 0.31 –1.24 bdi-miR166e-3p miR166i UUGGACCAGGCUUCAUUCCCC 0.96 1.50 0.63 mes-miR166i miR166e-3p UCGAACCAGGCUUCAUUCCCC 0.37 0.87 1.22 osa-miR166e-3p miR167 miR167b-3p AGGUCAUGCUGGAGUUUCAUC 0.52 2.36 2.19 ata-miR167b-3p miR167d-5p UGAAGCUGCCAGCAUGAUCUGA 7317.95 4706.54 –0.64 ata-miR167d-5p miR167e-5p UGAAGCUGCCAGCAUGAUCUA 2585.59 1681.22 –0.62 ata-miR167e-5p miR167f-3p CAGAUCAUGCUGCAGCUUCAU 3.49 1.65 –1.08 ata-miR167f-3p miR167a UGAAGCUGCCAGCAUGAUCUAA 6.38 3.78 –0.76 tae-miR167a miR167c UGAAGCUGCCAGCAUGAUCUU 38.94 23.70 –0.72 cpa-miR167c miR167j UGAAGCUGCCAGCAUGAUCUUA 2.15 0.94 –1.19 mdm-miR167j miR167c UGAAGCUGCCAGCAUGAUCUGG 2.15 0.71 –1.60 nta-miR167c miR167b-5p UAAAGCUGCCAGCAUGAUCUGG 1.11 0.71 –0.65 sly-miR167b-5p miR167c UGAAGCUGCCAGCAUGAUCUC 2.74 1.34 –1.04 vvi-miR167c miR168 miR168-3p CCCGCCUUGCACCAAGUGAAU 1.26 5.83 2.21 ata-miR168-3p miR169 miR169d-5p CAGCCAAGGAUGACUUGCCGG 3.34 1.50 –1.16 ata-miR169d-5p miR169g-3p GGCGAGUUGUUCUUGGCUACA 0.89 0.47 –0.91 ata-miR169g-3p miR171 miR171c-5p CGGUAUUGGUGCGGUUCAAUC 0.89 4.09 2.20 ata-miR171c-5p miR171d-5p UGUUGGCUCGACUCACUCAGA 0.74 2.52 1.76 ata-miR171d-5p miR171a UUGAGCCGUGCCAAUAUCACU 0.67 1.18 0.82 smo-miR171a miR172 miR172b-3p AGAAUCUUGAUGAUGCUGCAU 32.12 19.69 –0.71 ata-miR172b-3p miR172b-5p GCAGCACCACCAAGAUUCACA 0.01 0.94 6.56 ata-miR172b-5p miR172b GGGAAUCUUGAUGAUGCUGCA 0.15 0.63 2.09 cpa-miR172b miR1878 miR1878-3p AUUUGUAGUGUUCAGAUUGAGUUU 12.02 7.95 –0.60 bdi-miR1878-3p miR2118 miR2118b-3p GGGAAUGGGAACAUGGAGGAA 0.30 0.55 0.89 ata-miR2118b-3p miR2118b-5p UUCCCGAUGCCUCCCAUUCCUA 1.34 5.43 2.02 ata-miR2118b-5p miR2118d-3p UUCCUGAUGCCUCCCAUGCCUA 0.37 0.63 0.76 ata-miR2118d-3p miR2118b UUCCUGAUGCCUCCCAUUCCUA 0.59 2.83 2.26 bdi-miR2118b miR2118q UUCCCGAUGCCUCCUAUUCCUA 0.52 2.52 2.28 osa-miR2118q miR2118e UUCCUGAUGUCUCCCAUUCCUA 0.82 1.57 0.95 zma-miR2118e miR2120 miR2120 AAAGAUCUUUAGUCCCGGUUGUUC 2.30 0.87 –1.41 osa-miR2120 miR2275 miR2275b UUCAGUUUCUUCUAAUAUCUCA 4.23 8.66 1.03 bdi-miR2275b miR2275-3p UUUGGUUUCCUCCAAUAUCUCG 1.93 0.79 –1.29 tae-miR2275-3p miR319 miR319-3p ACUGGAUGACGCGGGAGCUAA 125.58 209.15 0.74 ata-miR319-3p miR319b-3p UUGGACUGAAGGGUGCUCCCU 3.34 0.55 –2.60 bdi-miR319b-3p miR3446 miR3446-5p CUCGGAAGCUAGACGUGUGGCAGG 13.35 27.80 1.06 aly-miR3446-5p miR3711 miR3711 UGGCGCUAGAAGGAGGGCCU 1.11 2.76 1.31 pab-miR3711 miR393 miR393-5p UUCCAAAGGGAUCGCAUUGAU 164.82 95.36 –0.79 ata-miR393-5p miR393b-3p UCAGUGCAAUCCCUUUGGAAU 5.79 3.23 –0.84 bdi-miR393b-3p miR3947 miR3947-5p UUAUUUCAGUAGACGACGUCACA 0.52 0.24 –1.14 csi-miR3947-5p miR395 miR395b UGAAGUGUUUGGGGGAACUC 0.67 0.08 –3.08 tae-miR395b miR396 miR396b-5p UCCACAGGCUUUCUUGAACUG 126.18 71.66 –0.82 ata-miR396b-5p miR396c-5p UUCCACAGCUUUCUUGAACUU 23.29 14.25 –0.71 ata-miR396c-5p miR396d-3p GUUCAAGAAAGCCCAUGGAAA 0.07 0.79 3.41 ata-miR396d-3p miR396e-3p GUUCAAUAAAGCUGUGGGAAA 1.11 2.60 1.22 ata-miR396e-3p miR396-5p AACUGUGAACUCGCGGGGAUG 5.49 2.83 –0.95 tae-miR396-5p miR397 miR397b-5p AUUGAGUGCAGCGUUGAUGAA 7.42 4.02 –0.89 bdi-miR397b-5p miR397-5p AUUGAGUGCAGCGUUGAUGAC 0.59 0.31 –0.91 stu-miR397-5p miR4384 miR4384 AAUCAGACACUGCAUUCAAAGACG 2.74 1.50 –0.88 gma-miR4384 miR444 miR444b UGCAGUUGCUGCCUCAAGCUU 6.45 2.05 –1.66 bdi-miR444b miR444b UGCAGUUGCUGUCUCAAGCUU 2.89 0.55 –2.39 hvu-miR444b miR5049 miR5049-3p AAUAUGGAUCGGAGGGAGUAC 3.78 7.40 0.97 tae-miR5049-3p miR5062 miR5062b-3p UGAACCUUAGGGAAAAGCCGCAU 1107.39 674.22 –0.72 ata-miR5062b-3p miR5062b-5p GCGGAUUUUUCACCAAGAUUCAAG 0.74 3.62 2.29 ata-miR5062b-5p miR5062-5p UGAACCUUAGGGAACAGCCGCAU 709.13 384.51 –0.88 tae-miR5062-5p miR5071 miR5071 UCAAGCAUCAUAUCGUGGACA 221.20 135.13 –0.71 osa-miR5071 miR5084 miR5084-3p ACCAUACGGUACUGCAGAGGAUC 35.90 22.52 –0.67 ata-miR5084-3p miR5084-5p AUCCUCUACAGUACUGUACGGUGC 12.68 8.19 –0.63 ata-miR5084-5p miR5169 miR5169b UUUGACCAAGUUUGUAGAACA 7.05 4.65 –0.60 bdi-miR5169b miR5175 miR5175-5p UUCCAAAUUACUCGUCGUGGU 25.15 12.13 –1.05 tae-miR5175-5p miR528 miR528-5p UGGAAGGGGCAUGCAGAGGAG 17.51 9.84 –0.83 ata-miR528-5p miR5387 miR5387b CGUGGCUCUGACCGGUGCUAAAGG 0.30 0.47 0.67 sbi-miR5387b miR6179 miR6179 AACCAGUCGAGGCCAGGGGGUU 0.52 0.94 0.86 hvu-miR6179 miR7714 miR7714-3p CUAAUAUGUAUCGAAGGGAGUAGC 1.34 2.05 0.62 bdi-miR7714-3p miR7757 miR7757-5p.2 CUUCCAUAUCAAAUCAUCUCU 0.22 0.55 1.31 bdi-miR7757-5p.2 miR8028 miR8028-5p UCCUUAUGCUACAAUUGUGAACAA 0.52 0.24 –1.14 stu-miR8028-5p miR8175 miR8175 GAUCCCCGGCAACGGCGCCA 10.53 28.11 1.42 ath-miR8175 miR827 miR827 UUAGAUGACCAUCAGCAAACA 12.76 19.69 0.63 ssp-miR827 miR837 miR837-3p AAACGAACAAAAAACUGAUGG 0.59 0.16 –1.91 ath-miR837-3p miR845 miR845 UGCUCUGAUACCAAUUGUUGG 0.52 0.79 0.60 bdi-miR845 miR894 miR894 CGUUUCACGUCGGGUUCACC 1.41 2.99 1.09 ppt-miR894 miR9653 miR9653b UGGCCAAGGUCUCUUGAGGCU 0.59 0.31 –0.91 tae-miR9653b miR9654 miR9654a-3p UUCUGAAAGGCUUGAAGCGAAU 1.48 0.79 –0.91 tae-miR9654a-3p miR9654b-3p UUCCGAAAGGCUUGAAGCGAAU 3.12 1.57 –0.98 tae-miR9654b-3p miR9655 miR9655-3p CAAGGGAAGGAAGUAGCCAAC 0.30 0.63 1.09 tae-miR9655-3p miR9656 miR9656-3p CUUCGAGACUCUGAACAGCGG 0.89 1.65 0.89 tae-miR9656-3p miR9657 miR9657b-3p CGUGCUUCCUCGUCGAACGGU 0.01 5.83 9.19 tae-miR9657b-3p miR9661 miR9661-5p UGAAGUAGAGCAGGGACCUCA 61.94 37.88 –0.71 tae-miR9661-5p miR9664 miR9664-3p UUGCAGUCCUCGAUGUCGUAG 9.35 5.51 –0.76 tae-miR9664-3p miR9669 miR9669-5p UACUGUGGGCACUUAUUUGAC 2.08 5.43 1.39 tae-miR9669-5p miR9670 miR9670-3p AGGUGGAAUACUUGAAGAAGA 19.06 96.07 2.33 tae-miR9670-3p miR9672 miR9672-5p CUUAAUGACAGUCGUGGUGUC 6.53 1.42 –2.20 ata-miR9672-5p miR9673 miR9673-5p UAAGAAGCAAAUAGCACAUG 14.17 5.83 –1.28 tae-miR9673-5p miR9676 miR9676-5p UGGAUGUCAUCGUGGCCGUACA 11.42 7.09 –0.69 tae-miR9676-5p miR9677 miR9677-5p UUCCACUCUACCAACAGCCACG 1.34 0.63 –1.08 ata-miR9677-5p miR9774 miR9774 CAAGAUAUUGGGUAUUUCUGUC 3.26 1.65 –0.98 tae-miR9774 miR9775 miR9775 UGUGCGCAAUAAGAUUUUGCUA 3.86 1.65 –1.22 tae-miR9775 miR9776 miR9776-5p AGCUUGGACGAGGAUGUGCAA 18.25 34.25 0.91 ata-miR9776-5p miR9863 miR9863b-5p UGUUAUGAUCUGCUUCUCAUC 1.85 0.63 –1.56 ata-miR9863b-5p gc-m0082-3p gc-m0082-3p UGUGAAAACCAGAUCGGACGG 0.89 0.55 –0.69 gc-m0088-3p gc-m0088-3p CGAGAGACUAGUGUAUAAGAA 1.85 2.91 0.65 gc-m0094-3p gc-m0094-3p GUCGAAUGAUGAUCCCAAGUGCA 1.93 0.87 –1.15 gc-m0098-3p gc-m0098-3p ACGUCGAUGAUAGAGAGAGGG 0.67 1.18 0.82 gc-m0187-5p gc-m0187-5p ACAAGGAGGAGGGAGCUUGUG 0.07 0.79 3.41 gc-m0192-5p gc-m0192-5p ACAGAUCAUGAUGUUUGGUAG 2.67 1.50 –0.84 gc-m0298-3p gc-m0298-3p CCAGAUUCAUUGGUACCUCGG 0.59 0.39 –0.59 gc-m0924-5p gc-m0924-5p GCAUCUUGGCUGCGUCGGUGG 0.67 1.42 1.09 gc-m1117-5p gc-m1117-5p CGAGUUGUUGAAAUCCGGCAG 3.04 5.20 0.77 gc-m1136-3p gc-m1136-3p UGGAUAGUUUGAGGUUUUAUUU 5.56 8.90 0.68 gc-m1443-3p gc-m1443-3p AUACCAUCGAAUAGAGCCUAG 0.67 1.34 1.00 gc-m1684-5p gc-m1684-5p UAUUGCAACGGCAGAGAGGAG 6.45 9.92 0.62 gc-m1730-3p gc-m1730-3p GAUUGACGGAUAACAUGUGGC 2.00 5.20 1.38 gc-m1737-5p gc-m1737-5p UGAGUUGAGGAUGUUGCCGCU 0.89 0.55 –0.69 gc-m2007-5p gc-m2007-5p UUAGGAACGGGGCAGCAGACU 1.63 5.43 1.74 gc-m2240-5p gc-m2240-5p AAGGAGGAGGAAGGAGGAAGA 3.12 5.35 0.78 gc-m2445-5p gc-m2445-5p CUGAAAGAGGGACGCCAUUAG 0.74 1.42 0.93 gc-m2668-5p gc-m2668-5p CGGGAGUGGCACGAAUAAGUC 12.91 19.53 0.60 gc-m2726-3p gc-m2726-3p UCUAGUUGGCCUAUGCAGUGA 2.82 6.30 1.16 gc-m3001-3p gc-m3001-3p CUGCCAAUCGAGGAGUUUGG 0.59 0.31 –0.91 gc-m3192-3p gc-m3192-3p UGGUUGGCGUGAGAAGCAGUG 0.67 0.31 –1.08 gc-m3263-5p gc-m3263-5p CCGCGGUGAGGAAUCAGAGGA 2.37 4.02 0.76 gc-m3551-5p gc-m3551-5p CUUGAGCAGACGUAGUAGCAA 20.70 36.70 0.83 gc-m3739-5p gc-m3739-5p AAGAACUCAUCACAUCUAGGC 2.00 3.94 0.98 gc-m3828-3p gc-m3828-3p UCUUCAGUGGUAGGCAGGCGGC 0.30 0.63 1.09 gc-m3845-5p gc-m3845-5p UUGGAAUUUGGAACUGGCUAG 1.93 4.88 1.34 gc-m4070-3p gc-m4070-3p CCACAAUGGUCAGACUCUUAG 2.97 5.12 0.79 gc-m4082-3p gc-m4082-3p UGCAUCCAGUAGUUUUGACCA 0.96 1.65 0.78 gc-m4289-5p gc-m4289-5p AUCGAUACAGAUGAGUUUGAA 0.74 1.18 0.67 gc-m4300-5p gc-m4300-5p UUUCUUUGAAUUUUGGAAGGC 0.52 0.87 0.74 gc-m4309-5p gc-m4309-5p CGGUGGAAGUUGCUCGGACUGA 3.34 5.35 0.68 miRNA family Name Sequences (5'–3') TPMa Log2 FCb Typical homology in miRBase CS CS-3C miR1120 miR1120a ACAUUCUUAUAUUAUGAGACGGAG 0.67 0.16 –2.08 tae-miR1120a miR1133 miR1133 CAUAUACUCCCUCCGUCCGAAA 0.82 1.50 0.87 tae-miR1133 miR1139 miR1139 GAGUAACAUACACUAGUAACA 0.89 1.57 0.82 bdi-miR1139 miR1508 miR1508c UAGAAAGGGAAAUAGCAGUUG 1.19 2.05 0.79 gma-miR1508c miR156 miR156 UUUGACAGAAGAUAGAGAGCAC 0.96 1.89 0.97 bcy-miR156 miR156s UGACAGAAGAGAGUGAGCACU 0.45 1.26 1.50 gma-miR156s miR156j UGACAGAAGAGGGUGAGCAC 0.37 1.26 1.76 mtr-miR156j miR159 miR159b-3p UUUGGAUUGAAGGGAGCUCUU 2.15 4.88 1.18 aly-miR159b-3p miR159a-3p CUUGGAUUGAAGGGAGCUCU 1.56 3.70 1.25 bdi-miR159a-3p miR159a UUUGGAUUGAAGGGAGCUCUG 147.24 283.80 0.95 tae-miR159a miR160 miR160c-3p GCGUGCAAGGAGCCAAGCAUG 0.89 2.13 1.26 ata-miR160c-3p miR160 UGCCUGGCUCCCUGUAUGCCA 3.04 0.63 –2.27 tae-miR160 miR160a-3p GCGUGCGAGGAGCCAAGCAUG 0.22 1.18 2.41 bdi-miR160a-3p miR164 miR164a-5p UGGAGAAGCAGGGCACGUGCU 3.86 1.97 –0.97 ata-miR164a-5p miR166 miR166d-5p GGAAUGUUGUCUGGCUCGGGG 5.19 10.71 1.04 ata-miR166d-5p miR166e-5p GGAAUGUUGUCUGGUUGGAGA 0.37 2.28 2.62 ata-miR166e-5p miR166e-3p CUCGGACCAGGCUUCAUUCCC 0.74 0.31 –1.24 bdi-miR166e-3p miR166i UUGGACCAGGCUUCAUUCCCC 0.96 1.50 0.63 mes-miR166i miR166e-3p UCGAACCAGGCUUCAUUCCCC 0.37 0.87 1.22 osa-miR166e-3p miR167 miR167b-3p AGGUCAUGCUGGAGUUUCAUC 0.52 2.36 2.19 ata-miR167b-3p miR167d-5p UGAAGCUGCCAGCAUGAUCUGA 7317.95 4706.54 –0.64 ata-miR167d-5p miR167e-5p UGAAGCUGCCAGCAUGAUCUA 2585.59 1681.22 –0.62 ata-miR167e-5p miR167f-3p CAGAUCAUGCUGCAGCUUCAU 3.49 1.65 –1.08 ata-miR167f-3p miR167a UGAAGCUGCCAGCAUGAUCUAA 6.38 3.78 –0.76 tae-miR167a miR167c UGAAGCUGCCAGCAUGAUCUU 38.94 23.70 –0.72 cpa-miR167c miR167j UGAAGCUGCCAGCAUGAUCUUA 2.15 0.94 –1.19 mdm-miR167j miR167c UGAAGCUGCCAGCAUGAUCUGG 2.15 0.71 –1.60 nta-miR167c miR167b-5p UAAAGCUGCCAGCAUGAUCUGG 1.11 0.71 –0.65 sly-miR167b-5p miR167c UGAAGCUGCCAGCAUGAUCUC 2.74 1.34 –1.04 vvi-miR167c miR168 miR168-3p CCCGCCUUGCACCAAGUGAAU 1.26 5.83 2.21 ata-miR168-3p miR169 miR169d-5p CAGCCAAGGAUGACUUGCCGG 3.34 1.50 –1.16 ata-miR169d-5p miR169g-3p GGCGAGUUGUUCUUGGCUACA 0.89 0.47 –0.91 ata-miR169g-3p miR171 miR171c-5p CGGUAUUGGUGCGGUUCAAUC 0.89 4.09 2.20 ata-miR171c-5p miR171d-5p UGUUGGCUCGACUCACUCAGA 0.74 2.52 1.76 ata-miR171d-5p miR171a UUGAGCCGUGCCAAUAUCACU 0.67 1.18 0.82 smo-miR171a miR172 miR172b-3p AGAAUCUUGAUGAUGCUGCAU 32.12 19.69 –0.71 ata-miR172b-3p miR172b-5p GCAGCACCACCAAGAUUCACA 0.01 0.94 6.56 ata-miR172b-5p miR172b GGGAAUCUUGAUGAUGCUGCA 0.15 0.63 2.09 cpa-miR172b miR1878 miR1878-3p AUUUGUAGUGUUCAGAUUGAGUUU 12.02 7.95 –0.60 bdi-miR1878-3p miR2118 miR2118b-3p GGGAAUGGGAACAUGGAGGAA 0.30 0.55 0.89 ata-miR2118b-3p miR2118b-5p UUCCCGAUGCCUCCCAUUCCUA 1.34 5.43 2.02 ata-miR2118b-5p miR2118d-3p UUCCUGAUGCCUCCCAUGCCUA 0.37 0.63 0.76 ata-miR2118d-3p miR2118b UUCCUGAUGCCUCCCAUUCCUA 0.59 2.83 2.26 bdi-miR2118b miR2118q UUCCCGAUGCCUCCUAUUCCUA 0.52 2.52 2.28 osa-miR2118q miR2118e UUCCUGAUGUCUCCCAUUCCUA 0.82 1.57 0.95 zma-miR2118e miR2120 miR2120 AAAGAUCUUUAGUCCCGGUUGUUC 2.30 0.87 –1.41 osa-miR2120 miR2275 miR2275b UUCAGUUUCUUCUAAUAUCUCA 4.23 8.66 1.03 bdi-miR2275b miR2275-3p UUUGGUUUCCUCCAAUAUCUCG 1.93 0.79 –1.29 tae-miR2275-3p miR319 miR319-3p ACUGGAUGACGCGGGAGCUAA 125.58 209.15 0.74 ata-miR319-3p miR319b-3p UUGGACUGAAGGGUGCUCCCU 3.34 0.55 –2.60 bdi-miR319b-3p miR3446 miR3446-5p CUCGGAAGCUAGACGUGUGGCAGG 13.35 27.80 1.06 aly-miR3446-5p miR3711 miR3711 UGGCGCUAGAAGGAGGGCCU 1.11 2.76 1.31 pab-miR3711 miR393 miR393-5p UUCCAAAGGGAUCGCAUUGAU 164.82 95.36 –0.79 ata-miR393-5p miR393b-3p UCAGUGCAAUCCCUUUGGAAU 5.79 3.23 –0.84 bdi-miR393b-3p miR3947 miR3947-5p UUAUUUCAGUAGACGACGUCACA 0.52 0.24 –1.14 csi-miR3947-5p miR395 miR395b UGAAGUGUUUGGGGGAACUC 0.67 0.08 –3.08 tae-miR395b miR396 miR396b-5p UCCACAGGCUUUCUUGAACUG 126.18 71.66 –0.82 ata-miR396b-5p miR396c-5p UUCCACAGCUUUCUUGAACUU 23.29 14.25 –0.71 ata-miR396c-5p miR396d-3p GUUCAAGAAAGCCCAUGGAAA 0.07 0.79 3.41 ata-miR396d-3p miR396e-3p GUUCAAUAAAGCUGUGGGAAA 1.11 2.60 1.22 ata-miR396e-3p miR396-5p AACUGUGAACUCGCGGGGAUG 5.49 2.83 –0.95 tae-miR396-5p miR397 miR397b-5p AUUGAGUGCAGCGUUGAUGAA 7.42 4.02 –0.89 bdi-miR397b-5p miR397-5p AUUGAGUGCAGCGUUGAUGAC 0.59 0.31 –0.91 stu-miR397-5p miR4384 miR4384 AAUCAGACACUGCAUUCAAAGACG 2.74 1.50 –0.88 gma-miR4384 miR444 miR444b UGCAGUUGCUGCCUCAAGCUU 6.45 2.05 –1.66 bdi-miR444b miR444b UGCAGUUGCUGUCUCAAGCUU 2.89 0.55 –2.39 hvu-miR444b miR5049 miR5049-3p AAUAUGGAUCGGAGGGAGUAC 3.78 7.40 0.97 tae-miR5049-3p miR5062 miR5062b-3p UGAACCUUAGGGAAAAGCCGCAU 1107.39 674.22 –0.72 ata-miR5062b-3p miR5062b-5p GCGGAUUUUUCACCAAGAUUCAAG 0.74 3.62 2.29 ata-miR5062b-5p miR5062-5p UGAACCUUAGGGAACAGCCGCAU 709.13 384.51 –0.88 tae-miR5062-5p miR5071 miR5071 UCAAGCAUCAUAUCGUGGACA 221.20 135.13 –0.71 osa-miR5071 miR5084 miR5084-3p ACCAUACGGUACUGCAGAGGAUC 35.90 22.52 –0.67 ata-miR5084-3p miR5084-5p AUCCUCUACAGUACUGUACGGUGC 12.68 8.19 –0.63 ata-miR5084-5p miR5169 miR5169b UUUGACCAAGUUUGUAGAACA 7.05 4.65 –0.60 bdi-miR5169b miR5175 miR5175-5p UUCCAAAUUACUCGUCGUGGU 25.15 12.13 –1.05 tae-miR5175-5p miR528 miR528-5p UGGAAGGGGCAUGCAGAGGAG 17.51 9.84 –0.83 ata-miR528-5p miR5387 miR5387b CGUGGCUCUGACCGGUGCUAAAGG 0.30 0.47 0.67 sbi-miR5387b miR6179 miR6179 AACCAGUCGAGGCCAGGGGGUU 0.52 0.94 0.86 hvu-miR6179 miR7714 miR7714-3p CUAAUAUGUAUCGAAGGGAGUAGC 1.34 2.05 0.62 bdi-miR7714-3p miR7757 miR7757-5p.2 CUUCCAUAUCAAAUCAUCUCU 0.22 0.55 1.31 bdi-miR7757-5p.2 miR8028 miR8028-5p UCCUUAUGCUACAAUUGUGAACAA 0.52 0.24 –1.14 stu-miR8028-5p miR8175 miR8175 GAUCCCCGGCAACGGCGCCA 10.53 28.11 1.42 ath-miR8175 miR827 miR827 UUAGAUGACCAUCAGCAAACA 12.76 19.69 0.63 ssp-miR827 miR837 miR837-3p AAACGAACAAAAAACUGAUGG 0.59 0.16 –1.91 ath-miR837-3p miR845 miR845 UGCUCUGAUACCAAUUGUUGG 0.52 0.79 0.60 bdi-miR845 miR894 miR894 CGUUUCACGUCGGGUUCACC 1.41 2.99 1.09 ppt-miR894 miR9653 miR9653b UGGCCAAGGUCUCUUGAGGCU 0.59 0.31 –0.91 tae-miR9653b miR9654 miR9654a-3p UUCUGAAAGGCUUGAAGCGAAU 1.48 0.79 –0.91 tae-miR9654a-3p miR9654b-3p UUCCGAAAGGCUUGAAGCGAAU 3.12 1.57 –0.98 tae-miR9654b-3p miR9655 miR9655-3p CAAGGGAAGGAAGUAGCCAAC 0.30 0.63 1.09 tae-miR9655-3p miR9656 miR9656-3p CUUCGAGACUCUGAACAGCGG 0.89 1.65 0.89 tae-miR9656-3p miR9657 miR9657b-3p CGUGCUUCCUCGUCGAACGGU 0.01 5.83 9.19 tae-miR9657b-3p miR9661 miR9661-5p UGAAGUAGAGCAGGGACCUCA 61.94 37.88 –0.71 tae-miR9661-5p miR9664 miR9664-3p UUGCAGUCCUCGAUGUCGUAG 9.35 5.51 –0.76 tae-miR9664-3p miR9669 miR9669-5p UACUGUGGGCACUUAUUUGAC 2.08 5.43 1.39 tae-miR9669-5p miR9670 miR9670-3p AGGUGGAAUACUUGAAGAAGA 19.06 96.07 2.33 tae-miR9670-3p miR9672 miR9672-5p CUUAAUGACAGUCGUGGUGUC 6.53 1.42 –2.20 ata-miR9672-5p miR9673 miR9673-5p UAAGAAGCAAAUAGCACAUG 14.17 5.83 –1.28 tae-miR9673-5p miR9676 miR9676-5p UGGAUGUCAUCGUGGCCGUACA 11.42 7.09 –0.69 tae-miR9676-5p miR9677 miR9677-5p UUCCACUCUACCAACAGCCACG 1.34 0.63 –1.08 ata-miR9677-5p miR9774 miR9774 CAAGAUAUUGGGUAUUUCUGUC 3.26 1.65 –0.98 tae-miR9774 miR9775 miR9775 UGUGCGCAAUAAGAUUUUGCUA 3.86 1.65 –1.22 tae-miR9775 miR9776 miR9776-5p AGCUUGGACGAGGAUGUGCAA 18.25 34.25 0.91 ata-miR9776-5p miR9863 miR9863b-5p UGUUAUGAUCUGCUUCUCAUC 1.85 0.63 –1.56 ata-miR9863b-5p gc-m0082-3p gc-m0082-3p UGUGAAAACCAGAUCGGACGG 0.89 0.55 –0.69 gc-m0088-3p gc-m0088-3p CGAGAGACUAGUGUAUAAGAA 1.85 2.91 0.65 gc-m0094-3p gc-m0094-3p GUCGAAUGAUGAUCCCAAGUGCA 1.93 0.87 –1.15 gc-m0098-3p gc-m0098-3p ACGUCGAUGAUAGAGAGAGGG 0.67 1.18 0.82 gc-m0187-5p gc-m0187-5p ACAAGGAGGAGGGAGCUUGUG 0.07 0.79 3.41 gc-m0192-5p gc-m0192-5p ACAGAUCAUGAUGUUUGGUAG 2.67 1.50 –0.84 gc-m0298-3p gc-m0298-3p CCAGAUUCAUUGGUACCUCGG 0.59 0.39 –0.59 gc-m0924-5p gc-m0924-5p GCAUCUUGGCUGCGUCGGUGG 0.67 1.42 1.09 gc-m1117-5p gc-m1117-5p CGAGUUGUUGAAAUCCGGCAG 3.04 5.20 0.77 gc-m1136-3p gc-m1136-3p UGGAUAGUUUGAGGUUUUAUUU 5.56 8.90 0.68 gc-m1443-3p gc-m1443-3p AUACCAUCGAAUAGAGCCUAG 0.67 1.34 1.00 gc-m1684-5p gc-m1684-5p UAUUGCAACGGCAGAGAGGAG 6.45 9.92 0.62 gc-m1730-3p gc-m1730-3p GAUUGACGGAUAACAUGUGGC 2.00 5.20 1.38 gc-m1737-5p gc-m1737-5p UGAGUUGAGGAUGUUGCCGCU 0.89 0.55 –0.69 gc-m2007-5p gc-m2007-5p UUAGGAACGGGGCAGCAGACU 1.63 5.43 1.74 gc-m2240-5p gc-m2240-5p AAGGAGGAGGAAGGAGGAAGA 3.12 5.35 0.78 gc-m2445-5p gc-m2445-5p CUGAAAGAGGGACGCCAUUAG 0.74 1.42 0.93 gc-m2668-5p gc-m2668-5p CGGGAGUGGCACGAAUAAGUC 12.91 19.53 0.60 gc-m2726-3p gc-m2726-3p UCUAGUUGGCCUAUGCAGUGA 2.82 6.30 1.16 gc-m3001-3p gc-m3001-3p CUGCCAAUCGAGGAGUUUGG 0.59 0.31 –0.91 gc-m3192-3p gc-m3192-3p UGGUUGGCGUGAGAAGCAGUG 0.67 0.31 –1.08 gc-m3263-5p gc-m3263-5p CCGCGGUGAGGAAUCAGAGGA 2.37 4.02 0.76 gc-m3551-5p gc-m3551-5p CUUGAGCAGACGUAGUAGCAA 20.70 36.70 0.83 gc-m3739-5p gc-m3739-5p AAGAACUCAUCACAUCUAGGC 2.00 3.94 0.98 gc-m3828-3p gc-m3828-3p UCUUCAGUGGUAGGCAGGCGGC 0.30 0.63 1.09 gc-m3845-5p gc-m3845-5p UUGGAAUUUGGAACUGGCUAG 1.93 4.88 1.34 gc-m4070-3p gc-m4070-3p CCACAAUGGUCAGACUCUUAG 2.97 5.12 0.79 gc-m4082-3p gc-m4082-3p UGCAUCCAGUAGUUUUGACCA 0.96 1.65 0.78 gc-m4289-5p gc-m4289-5p AUCGAUACAGAUGAGUUUGAA 0.74 1.18 0.67 gc-m4300-5p gc-m4300-5p UUUCUUUGAAUUUUGGAAGGC 0.52 0.87 0.74 gc-m4309-5p gc-m4309-5p CGGUGGAAGUUGCUCGGACUGA 3.34 5.35 0.68 a Figures in columns headed ‘CS’ and ‘CS-3C’ are normalized read counts. Normalized expression (TPM)=count of miRNA/total count of clean sRNAs×106. The signals were considered as differentially expressed with 1.5-fold change values. b FC, fold change (CS-3C/CS) View Large Table 1. The differentially expressed miRNAs in gametocidal action miRNA family Name Sequences (5'–3') TPMa Log2 FCb Typical homology in miRBase CS CS-3C miR1120 miR1120a ACAUUCUUAUAUUAUGAGACGGAG 0.67 0.16 –2.08 tae-miR1120a miR1133 miR1133 CAUAUACUCCCUCCGUCCGAAA 0.82 1.50 0.87 tae-miR1133 miR1139 miR1139 GAGUAACAUACACUAGUAACA 0.89 1.57 0.82 bdi-miR1139 miR1508 miR1508c UAGAAAGGGAAAUAGCAGUUG 1.19 2.05 0.79 gma-miR1508c miR156 miR156 UUUGACAGAAGAUAGAGAGCAC 0.96 1.89 0.97 bcy-miR156 miR156s UGACAGAAGAGAGUGAGCACU 0.45 1.26 1.50 gma-miR156s miR156j UGACAGAAGAGGGUGAGCAC 0.37 1.26 1.76 mtr-miR156j miR159 miR159b-3p UUUGGAUUGAAGGGAGCUCUU 2.15 4.88 1.18 aly-miR159b-3p miR159a-3p CUUGGAUUGAAGGGAGCUCU 1.56 3.70 1.25 bdi-miR159a-3p miR159a UUUGGAUUGAAGGGAGCUCUG 147.24 283.80 0.95 tae-miR159a miR160 miR160c-3p GCGUGCAAGGAGCCAAGCAUG 0.89 2.13 1.26 ata-miR160c-3p miR160 UGCCUGGCUCCCUGUAUGCCA 3.04 0.63 –2.27 tae-miR160 miR160a-3p GCGUGCGAGGAGCCAAGCAUG 0.22 1.18 2.41 bdi-miR160a-3p miR164 miR164a-5p UGGAGAAGCAGGGCACGUGCU 3.86 1.97 –0.97 ata-miR164a-5p miR166 miR166d-5p GGAAUGUUGUCUGGCUCGGGG 5.19 10.71 1.04 ata-miR166d-5p miR166e-5p GGAAUGUUGUCUGGUUGGAGA 0.37 2.28 2.62 ata-miR166e-5p miR166e-3p CUCGGACCAGGCUUCAUUCCC 0.74 0.31 –1.24 bdi-miR166e-3p miR166i UUGGACCAGGCUUCAUUCCCC 0.96 1.50 0.63 mes-miR166i miR166e-3p UCGAACCAGGCUUCAUUCCCC 0.37 0.87 1.22 osa-miR166e-3p miR167 miR167b-3p AGGUCAUGCUGGAGUUUCAUC 0.52 2.36 2.19 ata-miR167b-3p miR167d-5p UGAAGCUGCCAGCAUGAUCUGA 7317.95 4706.54 –0.64 ata-miR167d-5p miR167e-5p UGAAGCUGCCAGCAUGAUCUA 2585.59 1681.22 –0.62 ata-miR167e-5p miR167f-3p CAGAUCAUGCUGCAGCUUCAU 3.49 1.65 –1.08 ata-miR167f-3p miR167a UGAAGCUGCCAGCAUGAUCUAA 6.38 3.78 –0.76 tae-miR167a miR167c UGAAGCUGCCAGCAUGAUCUU 38.94 23.70 –0.72 cpa-miR167c miR167j UGAAGCUGCCAGCAUGAUCUUA 2.15 0.94 –1.19 mdm-miR167j miR167c UGAAGCUGCCAGCAUGAUCUGG 2.15 0.71 –1.60 nta-miR167c miR167b-5p UAAAGCUGCCAGCAUGAUCUGG 1.11 0.71 –0.65 sly-miR167b-5p miR167c UGAAGCUGCCAGCAUGAUCUC 2.74 1.34 –1.04 vvi-miR167c miR168 miR168-3p CCCGCCUUGCACCAAGUGAAU 1.26 5.83 2.21 ata-miR168-3p miR169 miR169d-5p CAGCCAAGGAUGACUUGCCGG 3.34 1.50 –1.16 ata-miR169d-5p miR169g-3p GGCGAGUUGUUCUUGGCUACA 0.89 0.47 –0.91 ata-miR169g-3p miR171 miR171c-5p CGGUAUUGGUGCGGUUCAAUC 0.89 4.09 2.20 ata-miR171c-5p miR171d-5p UGUUGGCUCGACUCACUCAGA 0.74 2.52 1.76 ata-miR171d-5p miR171a UUGAGCCGUGCCAAUAUCACU 0.67 1.18 0.82 smo-miR171a miR172 miR172b-3p AGAAUCUUGAUGAUGCUGCAU 32.12 19.69 –0.71 ata-miR172b-3p miR172b-5p GCAGCACCACCAAGAUUCACA 0.01 0.94 6.56 ata-miR172b-5p miR172b GGGAAUCUUGAUGAUGCUGCA 0.15 0.63 2.09 cpa-miR172b miR1878 miR1878-3p AUUUGUAGUGUUCAGAUUGAGUUU 12.02 7.95 –0.60 bdi-miR1878-3p miR2118 miR2118b-3p GGGAAUGGGAACAUGGAGGAA 0.30 0.55 0.89 ata-miR2118b-3p miR2118b-5p UUCCCGAUGCCUCCCAUUCCUA 1.34 5.43 2.02 ata-miR2118b-5p miR2118d-3p UUCCUGAUGCCUCCCAUGCCUA 0.37 0.63 0.76 ata-miR2118d-3p miR2118b UUCCUGAUGCCUCCCAUUCCUA 0.59 2.83 2.26 bdi-miR2118b miR2118q UUCCCGAUGCCUCCUAUUCCUA 0.52 2.52 2.28 osa-miR2118q miR2118e UUCCUGAUGUCUCCCAUUCCUA 0.82 1.57 0.95 zma-miR2118e miR2120 miR2120 AAAGAUCUUUAGUCCCGGUUGUUC 2.30 0.87 –1.41 osa-miR2120 miR2275 miR2275b UUCAGUUUCUUCUAAUAUCUCA 4.23 8.66 1.03 bdi-miR2275b miR2275-3p UUUGGUUUCCUCCAAUAUCUCG 1.93 0.79 –1.29 tae-miR2275-3p miR319 miR319-3p ACUGGAUGACGCGGGAGCUAA 125.58 209.15 0.74 ata-miR319-3p miR319b-3p UUGGACUGAAGGGUGCUCCCU 3.34 0.55 –2.60 bdi-miR319b-3p miR3446 miR3446-5p CUCGGAAGCUAGACGUGUGGCAGG 13.35 27.80 1.06 aly-miR3446-5p miR3711 miR3711 UGGCGCUAGAAGGAGGGCCU 1.11 2.76 1.31 pab-miR3711 miR393 miR393-5p UUCCAAAGGGAUCGCAUUGAU 164.82 95.36 –0.79 ata-miR393-5p miR393b-3p UCAGUGCAAUCCCUUUGGAAU 5.79 3.23 –0.84 bdi-miR393b-3p miR3947 miR3947-5p UUAUUUCAGUAGACGACGUCACA 0.52 0.24 –1.14 csi-miR3947-5p miR395 miR395b UGAAGUGUUUGGGGGAACUC 0.67 0.08 –3.08 tae-miR395b miR396 miR396b-5p UCCACAGGCUUUCUUGAACUG 126.18 71.66 –0.82 ata-miR396b-5p miR396c-5p UUCCACAGCUUUCUUGAACUU 23.29 14.25 –0.71 ata-miR396c-5p miR396d-3p GUUCAAGAAAGCCCAUGGAAA 0.07 0.79 3.41 ata-miR396d-3p miR396e-3p GUUCAAUAAAGCUGUGGGAAA 1.11 2.60 1.22 ata-miR396e-3p miR396-5p AACUGUGAACUCGCGGGGAUG 5.49 2.83 –0.95 tae-miR396-5p miR397 miR397b-5p AUUGAGUGCAGCGUUGAUGAA 7.42 4.02 –0.89 bdi-miR397b-5p miR397-5p AUUGAGUGCAGCGUUGAUGAC 0.59 0.31 –0.91 stu-miR397-5p miR4384 miR4384 AAUCAGACACUGCAUUCAAAGACG 2.74 1.50 –0.88 gma-miR4384 miR444 miR444b UGCAGUUGCUGCCUCAAGCUU 6.45 2.05 –1.66 bdi-miR444b miR444b UGCAGUUGCUGUCUCAAGCUU 2.89 0.55 –2.39 hvu-miR444b miR5049 miR5049-3p AAUAUGGAUCGGAGGGAGUAC 3.78 7.40 0.97 tae-miR5049-3p miR5062 miR5062b-3p UGAACCUUAGGGAAAAGCCGCAU 1107.39 674.22 –0.72 ata-miR5062b-3p miR5062b-5p GCGGAUUUUUCACCAAGAUUCAAG 0.74 3.62 2.29 ata-miR5062b-5p miR5062-5p UGAACCUUAGGGAACAGCCGCAU 709.13 384.51 –0.88 tae-miR5062-5p miR5071 miR5071 UCAAGCAUCAUAUCGUGGACA 221.20 135.13 –0.71 osa-miR5071 miR5084 miR5084-3p ACCAUACGGUACUGCAGAGGAUC 35.90 22.52 –0.67 ata-miR5084-3p miR5084-5p AUCCUCUACAGUACUGUACGGUGC 12.68 8.19 –0.63 ata-miR5084-5p miR5169 miR5169b UUUGACCAAGUUUGUAGAACA 7.05 4.65 –0.60 bdi-miR5169b miR5175 miR5175-5p UUCCAAAUUACUCGUCGUGGU 25.15 12.13 –1.05 tae-miR5175-5p miR528 miR528-5p UGGAAGGGGCAUGCAGAGGAG 17.51 9.84 –0.83 ata-miR528-5p miR5387 miR5387b CGUGGCUCUGACCGGUGCUAAAGG 0.30 0.47 0.67 sbi-miR5387b miR6179 miR6179 AACCAGUCGAGGCCAGGGGGUU 0.52 0.94 0.86 hvu-miR6179 miR7714 miR7714-3p CUAAUAUGUAUCGAAGGGAGUAGC 1.34 2.05 0.62 bdi-miR7714-3p miR7757 miR7757-5p.2 CUUCCAUAUCAAAUCAUCUCU 0.22 0.55 1.31 bdi-miR7757-5p.2 miR8028 miR8028-5p UCCUUAUGCUACAAUUGUGAACAA 0.52 0.24 –1.14 stu-miR8028-5p miR8175 miR8175 GAUCCCCGGCAACGGCGCCA 10.53 28.11 1.42 ath-miR8175 miR827 miR827 UUAGAUGACCAUCAGCAAACA 12.76 19.69 0.63 ssp-miR827 miR837 miR837-3p AAACGAACAAAAAACUGAUGG 0.59 0.16 –1.91 ath-miR837-3p miR845 miR845 UGCUCUGAUACCAAUUGUUGG 0.52 0.79 0.60 bdi-miR845 miR894 miR894 CGUUUCACGUCGGGUUCACC 1.41 2.99 1.09 ppt-miR894 miR9653 miR9653b UGGCCAAGGUCUCUUGAGGCU 0.59 0.31 –0.91 tae-miR9653b miR9654 miR9654a-3p UUCUGAAAGGCUUGAAGCGAAU 1.48 0.79 –0.91 tae-miR9654a-3p miR9654b-3p UUCCGAAAGGCUUGAAGCGAAU 3.12 1.57 –0.98 tae-miR9654b-3p miR9655 miR9655-3p CAAGGGAAGGAAGUAGCCAAC 0.30 0.63 1.09 tae-miR9655-3p miR9656 miR9656-3p CUUCGAGACUCUGAACAGCGG 0.89 1.65 0.89 tae-miR9656-3p miR9657 miR9657b-3p CGUGCUUCCUCGUCGAACGGU 0.01 5.83 9.19 tae-miR9657b-3p miR9661 miR9661-5p UGAAGUAGAGCAGGGACCUCA 61.94 37.88 –0.71 tae-miR9661-5p miR9664 miR9664-3p UUGCAGUCCUCGAUGUCGUAG 9.35 5.51 –0.76 tae-miR9664-3p miR9669 miR9669-5p UACUGUGGGCACUUAUUUGAC 2.08 5.43 1.39 tae-miR9669-5p miR9670 miR9670-3p AGGUGGAAUACUUGAAGAAGA 19.06 96.07 2.33 tae-miR9670-3p miR9672 miR9672-5p CUUAAUGACAGUCGUGGUGUC 6.53 1.42 –2.20 ata-miR9672-5p miR9673 miR9673-5p UAAGAAGCAAAUAGCACAUG 14.17 5.83 –1.28 tae-miR9673-5p miR9676 miR9676-5p UGGAUGUCAUCGUGGCCGUACA 11.42 7.09 –0.69 tae-miR9676-5p miR9677 miR9677-5p UUCCACUCUACCAACAGCCACG 1.34 0.63 –1.08 ata-miR9677-5p miR9774 miR9774 CAAGAUAUUGGGUAUUUCUGUC 3.26 1.65 –0.98 tae-miR9774 miR9775 miR9775 UGUGCGCAAUAAGAUUUUGCUA 3.86 1.65 –1.22 tae-miR9775 miR9776 miR9776-5p AGCUUGGACGAGGAUGUGCAA 18.25 34.25 0.91 ata-miR9776-5p miR9863 miR9863b-5p UGUUAUGAUCUGCUUCUCAUC 1.85 0.63 –1.56 ata-miR9863b-5p gc-m0082-3p gc-m0082-3p UGUGAAAACCAGAUCGGACGG 0.89 0.55 –0.69 gc-m0088-3p gc-m0088-3p CGAGAGACUAGUGUAUAAGAA 1.85 2.91 0.65 gc-m0094-3p gc-m0094-3p GUCGAAUGAUGAUCCCAAGUGCA 1.93 0.87 –1.15 gc-m0098-3p gc-m0098-3p ACGUCGAUGAUAGAGAGAGGG 0.67 1.18 0.82 gc-m0187-5p gc-m0187-5p ACAAGGAGGAGGGAGCUUGUG 0.07 0.79 3.41 gc-m0192-5p gc-m0192-5p ACAGAUCAUGAUGUUUGGUAG 2.67 1.50 –0.84 gc-m0298-3p gc-m0298-3p CCAGAUUCAUUGGUACCUCGG 0.59 0.39 –0.59 gc-m0924-5p gc-m0924-5p GCAUCUUGGCUGCGUCGGUGG 0.67 1.42 1.09 gc-m1117-5p gc-m1117-5p CGAGUUGUUGAAAUCCGGCAG 3.04 5.20 0.77 gc-m1136-3p gc-m1136-3p UGGAUAGUUUGAGGUUUUAUUU 5.56 8.90 0.68 gc-m1443-3p gc-m1443-3p AUACCAUCGAAUAGAGCCUAG 0.67 1.34 1.00 gc-m1684-5p gc-m1684-5p UAUUGCAACGGCAGAGAGGAG 6.45 9.92 0.62 gc-m1730-3p gc-m1730-3p GAUUGACGGAUAACAUGUGGC 2.00 5.20 1.38 gc-m1737-5p gc-m1737-5p UGAGUUGAGGAUGUUGCCGCU 0.89 0.55 –0.69 gc-m2007-5p gc-m2007-5p UUAGGAACGGGGCAGCAGACU 1.63 5.43 1.74 gc-m2240-5p gc-m2240-5p AAGGAGGAGGAAGGAGGAAGA 3.12 5.35 0.78 gc-m2445-5p gc-m2445-5p CUGAAAGAGGGACGCCAUUAG 0.74 1.42 0.93 gc-m2668-5p gc-m2668-5p CGGGAGUGGCACGAAUAAGUC 12.91 19.53 0.60 gc-m2726-3p gc-m2726-3p UCUAGUUGGCCUAUGCAGUGA 2.82 6.30 1.16 gc-m3001-3p gc-m3001-3p CUGCCAAUCGAGGAGUUUGG 0.59 0.31 –0.91 gc-m3192-3p gc-m3192-3p UGGUUGGCGUGAGAAGCAGUG 0.67 0.31 –1.08 gc-m3263-5p gc-m3263-5p CCGCGGUGAGGAAUCAGAGGA 2.37 4.02 0.76 gc-m3551-5p gc-m3551-5p CUUGAGCAGACGUAGUAGCAA 20.70 36.70 0.83 gc-m3739-5p gc-m3739-5p AAGAACUCAUCACAUCUAGGC 2.00 3.94 0.98 gc-m3828-3p gc-m3828-3p UCUUCAGUGGUAGGCAGGCGGC 0.30 0.63 1.09 gc-m3845-5p gc-m3845-5p UUGGAAUUUGGAACUGGCUAG 1.93 4.88 1.34 gc-m4070-3p gc-m4070-3p CCACAAUGGUCAGACUCUUAG 2.97 5.12 0.79 gc-m4082-3p gc-m4082-3p UGCAUCCAGUAGUUUUGACCA 0.96 1.65 0.78 gc-m4289-5p gc-m4289-5p AUCGAUACAGAUGAGUUUGAA 0.74 1.18 0.67 gc-m4300-5p gc-m4300-5p UUUCUUUGAAUUUUGGAAGGC 0.52 0.87 0.74 gc-m4309-5p gc-m4309-5p CGGUGGAAGUUGCUCGGACUGA 3.34 5.35 0.68 miRNA family Name Sequences (5'–3') TPMa Log2 FCb Typical homology in miRBase CS CS-3C miR1120 miR1120a ACAUUCUUAUAUUAUGAGACGGAG 0.67 0.16 –2.08 tae-miR1120a miR1133 miR1133 CAUAUACUCCCUCCGUCCGAAA 0.82 1.50 0.87 tae-miR1133 miR1139 miR1139 GAGUAACAUACACUAGUAACA 0.89 1.57 0.82 bdi-miR1139 miR1508 miR1508c UAGAAAGGGAAAUAGCAGUUG 1.19 2.05 0.79 gma-miR1508c miR156 miR156 UUUGACAGAAGAUAGAGAGCAC 0.96 1.89 0.97 bcy-miR156 miR156s UGACAGAAGAGAGUGAGCACU 0.45 1.26 1.50 gma-miR156s miR156j UGACAGAAGAGGGUGAGCAC 0.37 1.26 1.76 mtr-miR156j miR159 miR159b-3p UUUGGAUUGAAGGGAGCUCUU 2.15 4.88 1.18 aly-miR159b-3p miR159a-3p CUUGGAUUGAAGGGAGCUCU 1.56 3.70 1.25 bdi-miR159a-3p miR159a UUUGGAUUGAAGGGAGCUCUG 147.24 283.80 0.95 tae-miR159a miR160 miR160c-3p GCGUGCAAGGAGCCAAGCAUG 0.89 2.13 1.26 ata-miR160c-3p miR160 UGCCUGGCUCCCUGUAUGCCA 3.04 0.63 –2.27 tae-miR160 miR160a-3p GCGUGCGAGGAGCCAAGCAUG 0.22 1.18 2.41 bdi-miR160a-3p miR164 miR164a-5p UGGAGAAGCAGGGCACGUGCU 3.86 1.97 –0.97 ata-miR164a-5p miR166 miR166d-5p GGAAUGUUGUCUGGCUCGGGG 5.19 10.71 1.04 ata-miR166d-5p miR166e-5p GGAAUGUUGUCUGGUUGGAGA 0.37 2.28 2.62 ata-miR166e-5p miR166e-3p CUCGGACCAGGCUUCAUUCCC 0.74 0.31 –1.24 bdi-miR166e-3p miR166i UUGGACCAGGCUUCAUUCCCC 0.96 1.50 0.63 mes-miR166i miR166e-3p UCGAACCAGGCUUCAUUCCCC 0.37 0.87 1.22 osa-miR166e-3p miR167 miR167b-3p AGGUCAUGCUGGAGUUUCAUC 0.52 2.36 2.19 ata-miR167b-3p miR167d-5p UGAAGCUGCCAGCAUGAUCUGA 7317.95 4706.54 –0.64 ata-miR167d-5p miR167e-5p UGAAGCUGCCAGCAUGAUCUA 2585.59 1681.22 –0.62 ata-miR167e-5p miR167f-3p CAGAUCAUGCUGCAGCUUCAU 3.49 1.65 –1.08 ata-miR167f-3p miR167a UGAAGCUGCCAGCAUGAUCUAA 6.38 3.78 –0.76 tae-miR167a miR167c UGAAGCUGCCAGCAUGAUCUU 38.94 23.70 –0.72 cpa-miR167c miR167j UGAAGCUGCCAGCAUGAUCUUA 2.15 0.94 –1.19 mdm-miR167j miR167c UGAAGCUGCCAGCAUGAUCUGG 2.15 0.71 –1.60 nta-miR167c miR167b-5p UAAAGCUGCCAGCAUGAUCUGG 1.11 0.71 –0.65 sly-miR167b-5p miR167c UGAAGCUGCCAGCAUGAUCUC 2.74 1.34 –1.04 vvi-miR167c miR168 miR168-3p CCCGCCUUGCACCAAGUGAAU 1.26 5.83 2.21 ata-miR168-3p miR169 miR169d-5p CAGCCAAGGAUGACUUGCCGG 3.34 1.50 –1.16 ata-miR169d-5p miR169g-3p GGCGAGUUGUUCUUGGCUACA 0.89 0.47 –0.91 ata-miR169g-3p miR171 miR171c-5p CGGUAUUGGUGCGGUUCAAUC 0.89 4.09 2.20 ata-miR171c-5p miR171d-5p UGUUGGCUCGACUCACUCAGA 0.74 2.52 1.76 ata-miR171d-5p miR171a UUGAGCCGUGCCAAUAUCACU 0.67 1.18 0.82 smo-miR171a miR172 miR172b-3p AGAAUCUUGAUGAUGCUGCAU 32.12 19.69 –0.71 ata-miR172b-3p miR172b-5p GCAGCACCACCAAGAUUCACA 0.01 0.94 6.56 ata-miR172b-5p miR172b GGGAAUCUUGAUGAUGCUGCA 0.15 0.63 2.09 cpa-miR172b miR1878 miR1878-3p AUUUGUAGUGUUCAGAUUGAGUUU 12.02 7.95 –0.60 bdi-miR1878-3p miR2118 miR2118b-3p GGGAAUGGGAACAUGGAGGAA 0.30 0.55 0.89 ata-miR2118b-3p miR2118b-5p UUCCCGAUGCCUCCCAUUCCUA 1.34 5.43 2.02 ata-miR2118b-5p miR2118d-3p UUCCUGAUGCCUCCCAUGCCUA 0.37 0.63 0.76 ata-miR2118d-3p miR2118b UUCCUGAUGCCUCCCAUUCCUA 0.59 2.83 2.26 bdi-miR2118b miR2118q UUCCCGAUGCCUCCUAUUCCUA 0.52 2.52 2.28 osa-miR2118q miR2118e UUCCUGAUGUCUCCCAUUCCUA 0.82 1.57 0.95 zma-miR2118e miR2120 miR2120 AAAGAUCUUUAGUCCCGGUUGUUC 2.30 0.87 –1.41 osa-miR2120 miR2275 miR2275b UUCAGUUUCUUCUAAUAUCUCA 4.23 8.66 1.03 bdi-miR2275b miR2275-3p UUUGGUUUCCUCCAAUAUCUCG 1.93 0.79 –1.29 tae-miR2275-3p miR319 miR319-3p ACUGGAUGACGCGGGAGCUAA 125.58 209.15 0.74 ata-miR319-3p miR319b-3p UUGGACUGAAGGGUGCUCCCU 3.34 0.55 –2.60 bdi-miR319b-3p miR3446 miR3446-5p CUCGGAAGCUAGACGUGUGGCAGG 13.35 27.80 1.06 aly-miR3446-5p miR3711 miR3711 UGGCGCUAGAAGGAGGGCCU 1.11 2.76 1.31 pab-miR3711 miR393 miR393-5p UUCCAAAGGGAUCGCAUUGAU 164.82 95.36 –0.79 ata-miR393-5p miR393b-3p UCAGUGCAAUCCCUUUGGAAU 5.79 3.23 –0.84 bdi-miR393b-3p miR3947 miR3947-5p UUAUUUCAGUAGACGACGUCACA 0.52 0.24 –1.14 csi-miR3947-5p miR395 miR395b UGAAGUGUUUGGGGGAACUC 0.67 0.08 –3.08 tae-miR395b miR396 miR396b-5p UCCACAGGCUUUCUUGAACUG 126.18 71.66 –0.82 ata-miR396b-5p miR396c-5p UUCCACAGCUUUCUUGAACUU 23.29 14.25 –0.71 ata-miR396c-5p miR396d-3p GUUCAAGAAAGCCCAUGGAAA 0.07 0.79 3.41 ata-miR396d-3p miR396e-3p GUUCAAUAAAGCUGUGGGAAA 1.11 2.60 1.22 ata-miR396e-3p miR396-5p AACUGUGAACUCGCGGGGAUG 5.49 2.83 –0.95 tae-miR396-5p miR397 miR397b-5p AUUGAGUGCAGCGUUGAUGAA 7.42 4.02 –0.89 bdi-miR397b-5p miR397-5p AUUGAGUGCAGCGUUGAUGAC 0.59 0.31 –0.91 stu-miR397-5p miR4384 miR4384 AAUCAGACACUGCAUUCAAAGACG 2.74 1.50 –0.88 gma-miR4384 miR444 miR444b UGCAGUUGCUGCCUCAAGCUU 6.45 2.05 –1.66 bdi-miR444b miR444b UGCAGUUGCUGUCUCAAGCUU 2.89 0.55 –2.39 hvu-miR444b miR5049 miR5049-3p AAUAUGGAUCGGAGGGAGUAC 3.78 7.40 0.97 tae-miR5049-3p miR5062 miR5062b-3p UGAACCUUAGGGAAAAGCCGCAU 1107.39 674.22 –0.72 ata-miR5062b-3p miR5062b-5p GCGGAUUUUUCACCAAGAUUCAAG 0.74 3.62 2.29 ata-miR5062b-5p miR5062-5p UGAACCUUAGGGAACAGCCGCAU 709.13 384.51 –0.88 tae-miR5062-5p miR5071 miR5071 UCAAGCAUCAUAUCGUGGACA 221.20 135.13 –0.71 osa-miR5071 miR5084 miR5084-3p ACCAUACGGUACUGCAGAGGAUC 35.90 22.52 –0.67 ata-miR5084-3p miR5084-5p AUCCUCUACAGUACUGUACGGUGC 12.68 8.19 –0.63 ata-miR5084-5p miR5169 miR5169b UUUGACCAAGUUUGUAGAACA 7.05 4.65 –0.60 bdi-miR5169b miR5175 miR5175-5p UUCCAAAUUACUCGUCGUGGU 25.15 12.13 –1.05 tae-miR5175-5p miR528 miR528-5p UGGAAGGGGCAUGCAGAGGAG 17.51 9.84 –0.83 ata-miR528-5p miR5387 miR5387b CGUGGCUCUGACCGGUGCUAAAGG 0.30 0.47 0.67 sbi-miR5387b miR6179 miR6179 AACCAGUCGAGGCCAGGGGGUU 0.52 0.94 0.86 hvu-miR6179 miR7714 miR7714-3p CUAAUAUGUAUCGAAGGGAGUAGC 1.34 2.05 0.62 bdi-miR7714-3p miR7757 miR7757-5p.2 CUUCCAUAUCAAAUCAUCUCU 0.22 0.55 1.31 bdi-miR7757-5p.2 miR8028 miR8028-5p UCCUUAUGCUACAAUUGUGAACAA 0.52 0.24 –1.14 stu-miR8028-5p miR8175 miR8175 GAUCCCCGGCAACGGCGCCA 10.53 28.11 1.42 ath-miR8175 miR827 miR827 UUAGAUGACCAUCAGCAAACA 12.76 19.69 0.63 ssp-miR827 miR837 miR837-3p AAACGAACAAAAAACUGAUGG 0.59 0.16 –1.91 ath-miR837-3p miR845 miR845 UGCUCUGAUACCAAUUGUUGG 0.52 0.79 0.60 bdi-miR845 miR894 miR894 CGUUUCACGUCGGGUUCACC 1.41 2.99 1.09 ppt-miR894 miR9653 miR9653b UGGCCAAGGUCUCUUGAGGCU 0.59 0.31 –0.91 tae-miR9653b miR9654 miR9654a-3p UUCUGAAAGGCUUGAAGCGAAU 1.48 0.79 –0.91 tae-miR9654a-3p miR9654b-3p UUCCGAAAGGCUUGAAGCGAAU 3.12 1.57 –0.98 tae-miR9654b-3p miR9655 miR9655-3p CAAGGGAAGGAAGUAGCCAAC 0.30 0.63 1.09 tae-miR9655-3p miR9656 miR9656-3p CUUCGAGACUCUGAACAGCGG 0.89 1.65 0.89 tae-miR9656-3p miR9657 miR9657b-3p CGUGCUUCCUCGUCGAACGGU 0.01 5.83 9.19 tae-miR9657b-3p miR9661 miR9661-5p UGAAGUAGAGCAGGGACCUCA 61.94 37.88 –0.71 tae-miR9661-5p miR9664 miR9664-3p UUGCAGUCCUCGAUGUCGUAG 9.35 5.51 –0.76 tae-miR9664-3p miR9669 miR9669-5p UACUGUGGGCACUUAUUUGAC 2.08 5.43 1.39 tae-miR9669-5p miR9670 miR9670-3p AGGUGGAAUACUUGAAGAAGA 19.06 96.07 2.33 tae-miR9670-3p miR9672 miR9672-5p CUUAAUGACAGUCGUGGUGUC 6.53 1.42 –2.20 ata-miR9672-5p miR9673 miR9673-5p UAAGAAGCAAAUAGCACAUG 14.17 5.83 –1.28 tae-miR9673-5p miR9676 miR9676-5p UGGAUGUCAUCGUGGCCGUACA 11.42 7.09 –0.69 tae-miR9676-5p miR9677 miR9677-5p UUCCACUCUACCAACAGCCACG 1.34 0.63 –1.08 ata-miR9677-5p miR9774 miR9774 CAAGAUAUUGGGUAUUUCUGUC 3.26 1.65 –0.98 tae-miR9774 miR9775 miR9775 UGUGCGCAAUAAGAUUUUGCUA 3.86 1.65 –1.22 tae-miR9775 miR9776 miR9776-5p AGCUUGGACGAGGAUGUGCAA 18.25 34.25 0.91 ata-miR9776-5p miR9863 miR9863b-5p UGUUAUGAUCUGCUUCUCAUC 1.85 0.63 –1.56 ata-miR9863b-5p gc-m0082-3p gc-m0082-3p UGUGAAAACCAGAUCGGACGG 0.89 0.55 –0.69 gc-m0088-3p gc-m0088-3p CGAGAGACUAGUGUAUAAGAA 1.85 2.91 0.65 gc-m0094-3p gc-m0094-3p GUCGAAUGAUGAUCCCAAGUGCA 1.93 0.87 –1.15 gc-m0098-3p gc-m0098-3p ACGUCGAUGAUAGAGAGAGGG 0.67 1.18 0.82 gc-m0187-5p gc-m0187-5p ACAAGGAGGAGGGAGCUUGUG 0.07 0.79 3.41 gc-m0192-5p gc-m0192-5p ACAGAUCAUGAUGUUUGGUAG 2.67 1.50 –0.84 gc-m0298-3p gc-m0298-3p CCAGAUUCAUUGGUACCUCGG 0.59 0.39 –0.59 gc-m0924-5p gc-m0924-5p GCAUCUUGGCUGCGUCGGUGG 0.67 1.42 1.09 gc-m1117-5p gc-m1117-5p CGAGUUGUUGAAAUCCGGCAG 3.04 5.20 0.77 gc-m1136-3p gc-m1136-3p UGGAUAGUUUGAGGUUUUAUUU 5.56 8.90 0.68 gc-m1443-3p gc-m1443-3p AUACCAUCGAAUAGAGCCUAG 0.67 1.34 1.00 gc-m1684-5p gc-m1684-5p UAUUGCAACGGCAGAGAGGAG 6.45 9.92 0.62 gc-m1730-3p gc-m1730-3p GAUUGACGGAUAACAUGUGGC 2.00 5.20 1.38 gc-m1737-5p gc-m1737-5p UGAGUUGAGGAUGUUGCCGCU 0.89 0.55 –0.69 gc-m2007-5p gc-m2007-5p UUAGGAACGGGGCAGCAGACU 1.63 5.43 1.74 gc-m2240-5p gc-m2240-5p AAGGAGGAGGAAGGAGGAAGA 3.12 5.35 0.78 gc-m2445-5p gc-m2445-5p CUGAAAGAGGGACGCCAUUAG 0.74 1.42 0.93 gc-m2668-5p gc-m2668-5p CGGGAGUGGCACGAAUAAGUC 12.91 19.53 0.60 gc-m2726-3p gc-m2726-3p UCUAGUUGGCCUAUGCAGUGA 2.82 6.30 1.16 gc-m3001-3p gc-m3001-3p CUGCCAAUCGAGGAGUUUGG 0.59 0.31 –0.91 gc-m3192-3p gc-m3192-3p UGGUUGGCGUGAGAAGCAGUG 0.67 0.31 –1.08 gc-m3263-5p gc-m3263-5p CCGCGGUGAGGAAUCAGAGGA 2.37 4.02 0.76 gc-m3551-5p gc-m3551-5p CUUGAGCAGACGUAGUAGCAA 20.70 36.70 0.83 gc-m3739-5p gc-m3739-5p AAGAACUCAUCACAUCUAGGC 2.00 3.94 0.98 gc-m3828-3p gc-m3828-3p UCUUCAGUGGUAGGCAGGCGGC 0.30 0.63 1.09 gc-m3845-5p gc-m3845-5p UUGGAAUUUGGAACUGGCUAG 1.93 4.88 1.34 gc-m4070-3p gc-m4070-3p CCACAAUGGUCAGACUCUUAG 2.97 5.12 0.79 gc-m4082-3p gc-m4082-3p UGCAUCCAGUAGUUUUGACCA 0.96 1.65 0.78 gc-m4289-5p gc-m4289-5p AUCGAUACAGAUGAGUUUGAA 0.74 1.18 0.67 gc-m4300-5p gc-m4300-5p UUUCUUUGAAUUUUGGAAGGC 0.52 0.87 0.74 gc-m4309-5p gc-m4309-5p CGGUGGAAGUUGCUCGGACUGA 3.34 5.35 0.68 a Figures in columns headed ‘CS’ and ‘CS-3C’ are normalized read counts. Normalized expression (TPM)=count of miRNA/total count of clean sRNAs×106. The signals were considered as differentially expressed with 1.5-fold change values. b FC, fold change (CS-3C/CS) View Large Fig. 1. View largeDownload slide Comparison of miRNA expression between CS and CS-3C. (a) Scatter plot of miRNAs from CS and CS-3C (normalized expression). (b–e) qRT-PCR validation of miRNA expression in CS and CS-3C. The error bars represent the SDs. Fig. 1. View largeDownload slide Comparison of miRNA expression between CS and CS-3C. (a) Scatter plot of miRNAs from CS and CS-3C (normalized expression). (b–e) qRT-PCR validation of miRNA expression in CS and CS-3C. The error bars represent the SDs. To verify the differential miRNA expression, we randomly selected four differentially expressed miRNAs: down-regulated miR167d-5p and miR167e-5p, and up-regulated tae-miR159a and tae-miR9657b-3p. We performed TaqMan qRT-PCR using the RNA samples from the anthers of CS-3C and CS (Supplementary Table S4). The expression profiles derived were consistent with those based on the number of sRNA reads (Fig. 1b–e). The differences in expression detected by qRT-PCR were significant for the four miRNAs (whose FCs are highlighted in red, Fig. 1b–e). To examine if the differential miRNA expression patterns were specific to the anther tissues, we also performed qRT-PCR analysis of the four miRNAs in the leaf tissues. The results (Supplementary Fig. S2) showed no significant differential expression between CS and CS-3C, suggesting that the differential miRNA expression occurs specifically during anther development. Dynamic changes of 24 nt siRNAs By removing the 24 nt miRNA reads from the total high-quality clean 24 nt sRNA reads, we obtained 7395431 and 6550830 in total of 24 nt siRNA reads for CS and CS-3C wheat lines, respectively. These reads were mapped to the A, B, and D genomes (Supplementary Table S5). It is noteworthy that some of these siRNAs were mapped to two or all three of these genomes. Therefore the numbers of the A, B, and D genome-matching siRNAs were collectively higher than the total sRNA number (Supplementary Table S5). Approximately 40–44% of the A, B, and D genome-matching 24 nt sRNAs were unique to CS or CS-3C, whereas the remaining sRNAs were common to both the wheat lines (Supplementary Table S5). This suggests that numerous 24 nt siRNAs are generated from the genomic sequences that are different between CS and CS-3C. To compare further the 24 nt siRNA profiles of CS-3C and CS, we surveyed the siRNA density along the 1A–7D chromosomes of the two wheat genotypes. We calculated the density of 24 nt siRNAs using the total siRNA reads (already normalized for the slightly different total sRNA read number between the two libraries) within 1 kb sliding windows. We used the value to compare the difference between CS-3C and CS in the distribution of 24 nt siRNAs on the corresponding chromosomes. Supplementary Table S6 lists the number of differential siRNA sliding windows (clustered siRNA locus) for the wheat chromosomes, which were defined to have a minimum of 20 siRNA reads within a 1 kb sliding window in at least one line with FC (CS-3C:CS) ≥1.5 or ≤0.67. Consistent with the 24 nt siRNAs targeting mainly TEs and repetitive sequences, most of the siRNAs in the differentially expressed siRNA sliding windows (~90%) corresponded to the repeat sequences of the chromosomes, with a few corresponding to protein-coding genes and non-coding RNA genes (Supplementary Table S7). These repeats generally had a lower level of total 24 nt siRNA reads in CS-3C than in CS, with 75.4% and 10.5% of them showing down-regulation (FC ≤0.67) and up-regulation (FC ≥1.5) of the 24 nt siRNAs, respectively, and 14.1% exhibiting equal amounts between CS-3C and CS (with a minimum of 10 siRNA reads in at least one line). Of the 64964 repeats in the two wheat lines, 11558 corresponded to the known types of TEs (Supplementary Fig. S3). Among the 24154 repeats with differential 24 nt siRNAs (with a minimum of 10 siRNA reads in at least one line), 5136 corresponded to the known types of TEs (Supplementary Fig. S3). Similar to that of the total repeat population, 75.2% of these sequences of known TEs showed down-regulation of 24 nt siRNAs, with only 11.7% showing siRNA up-regulation in CS-3C compared with that in CS. These results indicate that repeat-associated 24 nt siRNAs are generally down-regulated in the CS-3C line. We also examined the distribution of siRNAs within the differential siRNA sliding windows in protein-coding genes, including both transcribed and 3 kb upstream and downstream flanking regions. As expected from previous studies (Gent et al., 2013), the highest density of 24 nt siRNAs occurred within 1 kb upstream and downstream of the transcribed regions in both the lines (Fig. 2). The distribution of siRNA around genes largely overlapped between CS and CS-3C, with some subtle variations. The siRNA levels in the transcribed regions were slightly higher in CS-3C than in CS for all three genomes. Furthermore, for genomes B and D, the siRNA levels at the siRNA peak at ~500 bp upstream of the transcription start site increased in CS-3C. However, for genome D, the siRNA levels immediately upstream of the transcription start site decreased in CS-3C. Fig. 2. View largeDownload slide Distribution of 24 nt siRNAs around protein-coding genes in CS and CS-3C. (a–d) siRNA densities (1 kb sliding window) in upstream (3 kb), transcribed, and downstream (3 kb) regions of CS and CS-3C on the A genome, B genome, D genome, and the whole genome. Fig. 2. View largeDownload slide Distribution of 24 nt siRNAs around protein-coding genes in CS and CS-3C. (a–d) siRNA densities (1 kb sliding window) in upstream (3 kb), transcribed, and downstream (3 kb) regions of CS and CS-3C on the A genome, B genome, D genome, and the whole genome. As siRNAs at the gene-flanking regions can potentially regulate gene expression, a GO analysis was performed on genes associated with differentially expressed siRNAs within the transcribed and 1 kb upstream regions. The result showed that these genes were enriched for the molecular functions catalytic (GO:0003824) and binding (GO:0005488) activities (Fig. 3). Fig. 3. View largeDownload slide GO enrichment analysis of genes with differentially expressed siRNAs in the transcribed and 1 kb upstream regions between CS and CS-3C. GO terms with corrected FDR <0.05 were considered significantly enriched. (a) Graphical results of GO category of Molecular Function of genes with differentially expressed siRNAs in the transcribed and 1 kb upstream regions. (b) GO flash chart of GO categories of Biological Process, Cellular Component, and Molecular Function of genes with differentially expressed siRNAs in the transcribed and 1 kb upstream regions. Fig. 3. View largeDownload slide GO enrichment analysis of genes with differentially expressed siRNAs in the transcribed and 1 kb upstream regions between CS and CS-3C. GO terms with corrected FDR <0.05 were considered significantly enriched. (a) Graphical results of GO category of Molecular Function of genes with differentially expressed siRNAs in the transcribed and 1 kb upstream regions. (b) GO flash chart of GO categories of Biological Process, Cellular Component, and Molecular Function of genes with differentially expressed siRNAs in the transcribed and 1 kb upstream regions. Target prediction and function analysis of miRNAs To understand the potential biological functions of the 135 differentially expressed miRNAs, we predicted their putative targets using the web-based psRNATarget program with default parameters (http://plantgrn.noble.org/psRNATarget/). The analysis was performed using two different transcript libraries for target search: T. aestivum unigenes from DFCI Gene Index (TAGI) version 12 and T. aestivum cDNA from EbsemblPlants. The putative targets included the essential transcription factors (such as MYB, GRF, ARF, and NAC), DNA-binding protein family-like, DNA ligase, nucleosome/chromatin assembly factor C, and telomere-binding protein. These target genes are known to play crucial roles in the growth and developmental processes of plants (Supplementary Table S8). However, many of these putative target genes had no functional description, which might be attributed to insufficient wheat sequence data, but many of them had conserved functional domains, and might be associated with Gc action (Supplementary Table S8). The GO analysis of the putative target genes predicted using wheat cDNA from EbsemblPlants revealed that most of these genes were in the binding category (GO: 0005488; Fig. 4). Fig. 4. View largeDownload slide GO enrichment analysis for the targets of the differentially expressed miRNAs. (a) Graphical results of GO category of Biological Process for the targets of the differentially expressed miRNAs. (b) GO flash chart of GO categories of Biological Process, Cellular Component, and Molecular Function for the targets of the differentially expressed miRNAs. Fig. 4. View largeDownload slide GO enrichment analysis for the targets of the differentially expressed miRNAs. (a) Graphical results of GO category of Biological Process for the targets of the differentially expressed miRNAs. (b) GO flash chart of GO categories of Biological Process, Cellular Component, and Molecular Function for the targets of the differentially expressed miRNAs. Expression patterns of putative miRNA target genes To examine the functional relevance of the predicted target genes and the corresponding miRNAs, we analyzed the expression of six putative target genes for the four differentially expressed miRNAs, namely tae-miR159a, tae-miR9657b-3p, tae-miR167d-5p, and tae-miR167e-5p, by qRT-PCR. The target genes included TC421314 (MYB3) and TaGAMYB1 for miR159a; TC422650 (DNA-binding protein family), TC419713 (nucleosome/chromatin assembly factor C), and TC451300 for miR9657b-3p; and TC402576 (with the probable chromatin-remodeling complex ATPase chain functional conserved domains) for miR167d-5p and miR167e-5p. Previous studies have indicated that the Myb transcription factors could play a role in anther development or affect male fertility. For instance, TaGAMYB1 and TaGAMYB2 have been verified as targets of tae-miR159, and overexpressing tae-miR159 in rice can reduce fertility and delay flowering time (Li et al., 2007; Wang et al., 2012). MiR9657b-3p has been predicted to target the DNA-binding protein family (TC422650) and nucleosome/chromatin assembly factor C (TC419713) genes. A putative DNA-binding motif, SAP, has been reported to play a role in chromosomal organization (Aravind and Koonin, 2000). Further, CAF-1 has a role in maintaining genomic and epigenetic stability during cell division and development (Huang and Jiao, 2012). Thus, the down-regulation of these two targets could cause abnormal chromosomal organization and chromatin assembly. The target gene of tae-miR167d-5p and tae-miR167e-5p, TC402576, has the probable chromatin-remodeling complex ATPase chain functionally conserved domains. The Arabidopsis SWR1 chromatin-remodeling complex has been reported to be important for DNA repair, somatic recombination, and meiosis, and mutations in At-SWR1 can cause reduced DNA repair capacity, reduced fertility, and irregular gametogenesis (Rosa et al., 2013). The sRNA sequencing data and TaqMan qRT-PCR results indicated that miR159a and miR9657b-3p were up-regulated, whereas miR167d-5p and miR167e-5p were down-regulated in CS-3C when compared with CS (Fig. 1). Consistent with the miRNA expression pattern, the targets of miR159a (TC421314 and TaGAMYB1) and miR9657b-3p (TC422650 and TC419713) were down-regulated in CS-3C, whereas one target of miR167d-5p and miR167e-5p (TC402576) was up-regulated in CS-3C (Fig. 5). The inverse expression correlation between the miRNAs and their putative targets suggests that these genes are genuine targets of the miRNAs. However, the expression level of the target gene TC451300 did not exhibit an inverse correlation with the abundance of miR9657b-3p (Fig. 5). Fig. 5. View largeDownload slide Confirmation of predicted miRNA target genes. (a–f) Quantitative real-time PCR analysis of the relative expression of miRNA targets in the CS and CS-3C line. GAPDH was used as the endogenous control. The error bars represent the SDs. (g) 5' RACE confirmation of the predicted miRNA target site. The arrow indicates the cleavage site, and numbers in parenthesis indicate the proportion of clones analyzed that are mapped to the miR159a cleavage position. Fig. 5. View largeDownload slide Confirmation of predicted miRNA target genes. (a–f) Quantitative real-time PCR analysis of the relative expression of miRNA targets in the CS and CS-3C line. GAPDH was used as the endogenous control. The error bars represent the SDs. (g) 5' RACE confirmation of the predicted miRNA target site. The arrow indicates the cleavage site, and numbers in parenthesis indicate the proportion of clones analyzed that are mapped to the miR159a cleavage position. Validation of miRNA-guided cleavage of target mRNA using 5' RACE The 5' RACE was performed to detect the predicted cleavage product of miR159a for the target gene TC421314, encoding the MYB3 transcription factor, which was down-regulated in CS-3C compared with that of CS (Fig. 5b). Sequencing of seven RACE-PCR clones detected a single cleavage product with the predicted cleavage site corresponding to nucleotide 11 of miR159a (Fig. 5g). This result confirmed miR159a-mediated cleavage of the target gene TC421314 in wheat anther. Overexpression of miR9657b-3p in transgenic rice reduced fertility To test the potential role of the differentially expressed miRNAs in Gc action, we prepared a miR9657b-3p overexpression construct in which the miR9657b-3p precursor sequence (Supplementary Fig. S4a) was driven by the maize ubiquitin promoter (Fig. 6a). We transformed this construct into rice, a model cereal that is more amenable to genetic transformation than wheat. A number of transgenic lines were obtained (Supplementary Fig. S4b). Among them, three lines (Line4, Line8, and Line10) exhibited high levels of miR9657b-3p expression (Fig. 6b), which correlated with the down-regulation of the rice gene TC492017 (Fig. 6c), an ortholog of the predicted wheat target gene TC419713 encoding nucleosome/chromatin assembly factor C. Fig. 6. View largeDownload slide Overexpression of tae-miR9657b-3p in transgenic rice results in reduced fertility. (a) A DNA fragment, including the tae-miR9657b-3p precursor, was cloned into vector pOx to generate the overexpression construct. (b) Quantitative real-time PCR analysis of mature miR9657b-3p in transgenic plants. Data represent the mean of three biological replicates. (c) Quantitative real-time PCR analysis of the target gene of miR9657b-3p, TC492017, in transgenic and wild-type plants. Data represent the mean of three biological replicates. (d) Effects of overexpression of miR9657b-3p on rice fertility (with no seed setting or shriveled seeds). The white arrow and black arrows indicate fertile and sterile spikelets, respectively. (e) Transgenic miR9657b-3p overexpression plants show a reduced seed setting rate compared with wild-type plants. (f) The filled grain number of miR9657b-3p overexpression transgenic plants is reduced compared with wild-type plants. (g) The pollen germination rate of the miR9657b-3p overexpression transgenic plants is lower than that of the wild-type plants. WT, wild-type plants; Line4, 8, 10, transgenic plant lines 4, 8, and 10. Fig. 6. View largeDownload slide Overexpression of tae-miR9657b-3p in transgenic rice results in reduced fertility. (a) A DNA fragment, including the tae-miR9657b-3p precursor, was cloned into vector pOx to generate the overexpression construct. (b) Quantitative real-time PCR analysis of mature miR9657b-3p in transgenic plants. Data represent the mean of three biological replicates. (c) Quantitative real-time PCR analysis of the target gene of miR9657b-3p, TC492017, in transgenic and wild-type plants. Data represent the mean of three biological replicates. (d) Effects of overexpression of miR9657b-3p on rice fertility (with no seed setting or shriveled seeds). The white arrow and black arrows indicate fertile and sterile spikelets, respectively. (e) Transgenic miR9657b-3p overexpression plants show a reduced seed setting rate compared with wild-type plants. (f) The filled grain number of miR9657b-3p overexpression transgenic plants is reduced compared with wild-type plants. (g) The pollen germination rate of the miR9657b-3p overexpression transgenic plants is lower than that of the wild-type plants. WT, wild-type plants; Line4, 8, 10, transgenic plant lines 4, 8, and 10. The phenotypic analysis of the miR9657b-3p-overexpressing transgenic rice lines showed that the fertility was reduced (Fig. 6d, e). The numbers of spikelets per panicle and filled grains were also reduced (Fig. 6f). Additionally, relatively poor pollen germination was observed in the transgenic plants (Fig. 6g). These results indicate that the overaccumulation of miR9657b-3p can decrease plant fertility. Down-regulation of siRNAs corresponding to the LINE/L1 retrotransposon is associated with reduced CHH methylation The 24 nt siRNAs are inducers of RdDM, leading to de novo cytosine methylation at all cytosine contexts, including CG, CHG, and CHH (where H=A, C, or T). The CG and CHG methylation can be maintained during DNA replication by methyltransferase 1 (MET1) and chromomethylase 3 (CMT3), respectively. However, the maintenance of CHH methylation depends on continuous RdDM. The primary function of RdDM is to maintain genome stability by silencing TEs and repetitive DNA. The activation of LINE/L1 retrotransposons has been implicated in genome instability in the cells of head and neck squamous cell carcinoma (HNSCC) (Martínez et al., 2012). Therefore, we were interested to understand if the differential accumulation of 24 nt siRNAs from such TEs was associated with differential DNA methylation. The bisulfite sequencing-based methylation analysis was performed on 10 regions of four TEs (designated as 3A176442068, 3B119165020, 7D195776517, and 7D237973401). The results revealed reduced 24 nt siRNA accumulation in the anthers of CS-3C. All DNA samples were efficiently converted using the bisulfite conversion reagent, as indicated by a complete lack of cytosines in the bisulfite PCR product of the wheat chloroplast psaA gene (Supplementary Fig. S5a). The bisulfite sequencing result showed that CHH methylation, but not CG and CHG methylation, was down-regulated in five regions of all the four TEs in CS-3C compared with that in CS, with at least one region of each TE showing reduced CHH methylation (regions 2 and 3 of TE 3A176442068, region 1 of TE 3B119165020, region 1 of TE 7D195776517, and region 2 of TE 7D237973401). No significant difference in methylation was observed in the remaining five regions of TEs analyzed (Fig. 7; Supplementary Figs S5, S6). This reduced CHH methylation correlated with the down-regulation of 24 nt siRNAs in CS-3C, suggesting that the differential accumulation of 24 nt siRNAs is associated with differential CHH methylation at some of the TE sequences. Unlike the CHH methylation, the CG and CHG methylation showed no significant differences between CS and CS-3C in all the bisulfite-sequenced TE fragments (Fig. 7; Supplementary Fig. S6). All primers used in the present study are listed in Supplementary Table S9. Fig. 7. View largeDownload slide Bisulfite sequencing analysis shows reduced CHH methylation in CS-3C. The x-axis indicates the individual cytosines in a sequential 5' to 3' order along the bisulfite-sequenced region, and the y-axis shows the percentage of methylated cytosines at each CG, CHH, or CHG position. The CG and CHG sites show no clear changes in methylation between CS and CS-3C. (a–c) The changes in methylation in region 2 of 3A176442068. (d–f) The changes in methylation in region 3 of 3A176442068. (g–i) The changes in methylation in region 1 of 7D195776517. (j–l) The changes in methylation in region 2 of 7D237973401. (m–o) The changes in methylation in region 1 of 3B119165020. Fig. 7. View largeDownload slide Bisulfite sequencing analysis shows reduced CHH methylation in CS-3C. The x-axis indicates the individual cytosines in a sequential 5' to 3' order along the bisulfite-sequenced region, and the y-axis shows the percentage of methylated cytosines at each CG, CHH, or CHG position. The CG and CHG sites show no clear changes in methylation between CS and CS-3C. (a–c) The changes in methylation in region 2 of 3A176442068. (d–f) The changes in methylation in region 3 of 3A176442068. (g–i) The changes in methylation in region 1 of 7D195776517. (j–l) The changes in methylation in region 2 of 7D237973401. (m–o) The changes in methylation in region 1 of 3B119165020. Discussion In the present study, we investigated the potential role of sRNAs in the function of the Gc chromosome, which can cause chromosomal mutations and integration of beneficial alien chromosome segments that are useful for wheat breeding. The sequencing analysis of sRNA populations from the anthers uncovered miRNAs and 24 nt repeat-associated siRNAs that are differentially accumulated in the CS and CS-3C lines. These changes in sRNA accumulation corresponded to differential miRNA target gene expression or differential RdDM, which potentially affects fertility and genome stability—the features of Gc action. The sRNA analysis in Gc wheat lines suggests the involvement of miRNA and 24 nt siRNA in the function of the Gc chromosome. Some of the 135 differentially expressed miRNAs (such as miR159 and miR167) have been suggested to play crucial roles in plant fertility by regulating the expression patterns of their targets (Wu et al., 2006; Tang et al., 2012; Wang et al., 2012). Among the predicted targets of the differentially accumulated miRNAs, there were at least seven types of transcription factors (AP2, GRF, MYB, ARF, NAC, SBP, and MADS-box). Previous studies have indicated that some of these transcription factors participate in the development of anther or male fertility (ARF, MYB, and SBP), some regulate cell proliferation or participate in the transcriptional control of cyclins (GRF, AP2, and MYB), and others participate in cell death (MYB and NAC) (Riechmann and Ratcliffe, 2000; Wu et al., 2006; Li et al., 2007; Raffaele et al., 2008; Kaneda et al., 2009; Rodriguez et al., 2010; Xing et al., 2010; Feller et al., 2011; Wang et al., 2012). All these processes might be related to Gc action. In addition to these transcription factors, the differentially accumulated miRNAs also targeted some key binding proteins, including RNA-binding protein, DNA-binding protein, telomere-binding protein, and CDK activator-binding protein, which can potentially participate in chromosomal organization and cell division (Aravind and Koonin, 2000; den Boer and Murray, 2000; McKnight et al., 2002; Kuchar and Fajkus, 2004). For instance, one such target gene, TC422650, belonging to the DNA-binding protein family, was verified to have an inverse expression pattern with the corresponding miRNA, the newly identified miR9657b-3p. Furthermore, a putative DNA-binding motif, SAP, could play a role in chromosomal organization (Aravind and Koonin, 2000). Moreover, the predicted miRNA targets consisted of genes encoding a variety of enzymes, including oxidoreductase, transferase, hydrolase, isomerase, and ligase. These enzymes have important catalytic activity in a number of biological processes, such as DNA repair and replication, microsporogenesis, cell proliferation control and cell death, and endoreduplication (Nishihama et al., 2001; Sugimoto-Shirasu et al., 2002; Gallego and Virshup, 2005; Meloche and Pouysségur, 2007; Wang and Li, 2009; Waterworth et al., 2009). In addition to these three main categories, several other potentially important miRNA targets were also identified in the present study. One target gene of miR9657b-3p, TC419713, encodes a nucleosome/chromatin assembly factor C, and its down-regulation can potentially cause abnormal chromatin assembly. As a Gc chromosome affects plant fertility by killing the gametes lacking it by causing chromosomal aberrations, the predicted targets seemed to be related to Gc action. Furthermore, the miRNAs might participate in Gc action by regulating their expression patterns. To explore the link between the miRNAs and fertility, and to investigate further the role of miRNAs in Gc action, we generated transgenic rice overexpressing tae-miR9657b-3p, an up-regulated miRNA in CS-3C. The results revealed that the rice target gene TC492017 (an ortholog of the wheat target gene, TC419713), encoding the nucleosome/chromatin assembly factor C, was also significantly down-regulated in transgenic lines, potentially causing abnormal chromatin assembly. The fertility of transgenic rice was reduced, which was reminiscent of low fertility in CS-3C (Tsujimoto and Tsunewaki, 1985). As the seed-setting and pollen germination rates of transgenic plants were reduced, the present study provides preliminary evidence to confirm the link between the miRNA and fertility and further demonstrates that the miRNA may be associated with Gc action. In our follow-up study, a pollen-specific promoter that will effectively overexpress key miRNA in wheat will be evaluated to verify comprehensively whether the differentially accumulated miRNA functions in Gc action. The potential function of these putative miRNA targets in male sterility, DNA damage response, chromosomal organization, cell division, and cell death indicates that these differentially accumulated miRNAs may have important regulatory functions in Gc action. It has been reported that the sRNA can move and/or communicate between the pollen grain cytoplasm and the sperm cells (Slotkin et al., 2009). The different miRNAs may participate in different aspects of the dual ‘breaking’ and ‘protecting’ functions of the Gc chromosome. For instance, the miRNAs that are up-regulated in CS-3C, such as miR159a and miR9657b-3p (whose overexpression can lead to reduced fertility), may be expressed in or transported to the gametes without chromosome 3C, thus causing gamete abortion. These miRNAs may participate in the ‘breaking’ function. Similarly, the miRNAs that are down-regulated in CS-3C, such as miR167d-5p and miR167e-5p (whose target is important for DNA repair, somatic recombination, and meiosis), may potentially participate in the ‘protecting’ function of the Gc chromosome in order to protect the gametes. An important finding of the sRNA analysis was the down-regulation of 24 nt repeat-associated siRNAs. This class of siRNAs is the inducer of RdDM and plays a key role in silencing TEs and other repetitive DNA elements in the genome (Zaratiegui et al., 2007; Ha et al., 2009; Kenan-Eichler et al., 2011; Li et al., 2014). In the present study, consistent with the down-regulation of 24 nt siRNAs and their function in RdDM, the bisulfite sequencing analysis of four LINE/L1 retrotransposons showed reduced CHH methylation (hypo-mCHH) in CS-3C plants, whereas no significant changes in CG and CHG methylation were observed. As reported in wheat, the down-regulation of siRNAs may lead to the activation of TEs in the allopolyploids, and the down-regulation of TE-siRNA in the allopolyploids may contribute to genome destabilization at the initial stages of speciation, and a similar phenomenon has also been reported in Arabidopsis allotetraploids (Ha et al., 2009; Kenan-Eichler et al., 2011). It has been proposed that the global demethylation and activation of TEs caused by the significant decrease of siRNAs corresponding to TEs in the polyploids may occur through disruption of developmentally regulated processes in the germline during meiosis, or gametogenesis, or even during the development of an embryo derived from the ‘down-regulated’ gametes, which are reminiscent of the processes and the stage of Gc action. However, in the subsequent generation, the methylation level increased, and the genome was stabilized (Kraitshtein et al., 2010; Kenan-Eichler et al., 2011). Shen et al. (2012) suggested that the increase in genome-wide DNA methylation in F1 hybrids, which is possibly due to RdDM, may lead to hybrid vigor by preserving genomic integrity via altering the expression of genes that normally limit plant growth. Martínez et al. (2012) reported that the hypomethylation of LINE-1 (retrotransposon) may cause pericentromeric instability, which in turn may induce a chromosomal instability phenotype in HNSCC of humans (Martínez et al., 2012). Therefore, it seems that the degree of methylation correlates positively with genomic stability in both plants and animals. Thus, the decrease of repeat-associated 24 nt siRNAs and the associated reduction in CHH methylation in the CS-3C line may cause genome instability, and hence contribute to Gc action. This is similar to the processes involved in the polyploidization of wheat (Kenan-Eichler et al., 2011). Consistently, treatment with the demethylation reagent 5-azacytidine has been reported to enhance chromosome aberration in common wheat carrying the Gc chromosome (de Las Heras et al., 2001; Su et al., 2013). The present study uncovers the possible role of miRNAs and 24 nt siRNAs in Gc action in wheat. The miRNAs may play a dual role by participating in both ‘breaking’ and ‘protecting’ functions of Gc action, whereas the siRNAs may participate in the ‘protecting’ function, loss of which causes reduction in DNA methylation and a decrease in genome stability. It is possible that CG and CHG methylation (more probably hyper-mCG, hyper-mCHG, or both) or some other epigenetic alterations may contribute to the ‘protecting’ function. However, the limited methylation analysis of the present study could not confirm this. Future studies should investigate the genome-wide methylation status of the CS and CS-3C lines to verify whether CG and CHG methylation can take part in the ‘protecting’ function of Gc action. Supplementary data Supplementary data are available at JXB online. Fig. S1. The distribution of small RNA (sRNA) reads in CS and CS-3C wheat lines. Fig. S2. qRT-PCR validation of the expression levels of miRNAs in CS-3C and CS leaf samples. Fig. S3. Classification of all TEs present in CS and CS-3C. Fig. S4. Transgenic overexpression of tae-miR9657b-3p in rice. Fig. S5. Examples of sequencing trace files of bisulfite PCR products. Fig. S6. Bisulfite-sequenced TE regions showing no clear difference in methylation between CS and CS-3C. Table S1. Summary of small RNA sequencing data sets from CS and CS-3C wheat lines. Table S2. The conserved miRNAs identified from the CS and CS-3C wheat lines. Table S3. The novel miRNAs identified from the CS and CS-3C wheat lines. Table S4. The miRNAs selected for TaqMan qPCR validation. Table S5. Total 24 nt siRNA reads mapped to the A, B, and D genome in CS and CS-3C. Table S6. Total number of sliding windows of the differentially expressed siRNAs. Table S7. Classification of 24 nt siRNA-associated sequences. Table S8. Putative target genes for differentially expressed miRNAs. Table S9. Sequence of primers used in this study. Acknowledgments This work was supported by the National Basic Research Program of China (#2011CB111500), the International S&T Cooperation Program of China (#2009DFA32470), and the Graduate Innovation Fund of Harbin Normal University (HSDBSCX2014-03). The authors declare no conflict of interests. References Aravind L , Koonin EV . 2000 . SAP—a putative DNA-binding motif involved in chromosomal organization . Trends in Biochemical Sciences 25 , 112 – 114 . Google Scholar CrossRef Search ADS PubMed Baumberger N , Baulcombe DC . 2005 . Arabidopsis ARGONAUTE1 is an RNA Slicer that selectively recruits microRNAs and short interfering RNAs . Proceedings of the National Academy of Sciences, USA 102 , 11928 – 11933 . Google Scholar CrossRef Search ADS Brennecke J , Malone CD , Aravin AA , Sachidanandam R , Stark A , Hannon GJ . 2008 . An epigenetic role for maternally inherited piRNAs in transposon silencing . Science 322 , 1387 – 1392 . Google Scholar CrossRef Search ADS PubMed Brodersen P , Sakvarelidze-Achard L , Bruun-Rasmussen M , Dunoyer P , Yamamoto YY , Sieburth L , Voinnet O . 2008 . Widespread translational inhibition by plant miRNAs and siRNAs . Science 320 , 1185 – 1190 . Google Scholar CrossRef Search ADS PubMed Carthew RW , Sontheimer EJ . 2009 . Origins and mechanisms of miRNAs and siRNAs . Cell 136 , 642 – 655 . Google Scholar CrossRef Search ADS PubMed Chen C , Ridzon DA , Broomer AJ et al. 2005 . Real-time quantification of microRNAs by stem–loop RT-PCR . Nucleic Acids Research 33 , e179 . Google Scholar CrossRef Search ADS PubMed Crosby ME , Kulshreshtha R , Ivan M , Glazer PM . 2009 . MicroRNA regulation of DNA repair gene expression in hypoxic stress . Cancer Research 69 , 1221 – 1229 . Google Scholar CrossRef Search ADS PubMed de las Heras JI , King IP , Parker JS . 2001 . 5-Azacytidine induces chromosomal breakage in the root tips of wheat carrying the cuckoo chromosome 4S(L) from Aegilops sharonensis . Heredity 87 , 474 – 479 . Google Scholar CrossRef Search ADS PubMed den Boer BG , Murray JA . 2000 . Triggering the cell cycle in plants . Trends in Cell Biology 10 , 245 – 250 . Google Scholar CrossRef Search ADS PubMed Denman RB . 1993 . Using RNAFOLD to predict the activity of small catalytic RNAs . Biotechniques 15 , 1090 – 1095 . Google Scholar PubMed Eamens A , Wang MB , Smith NA , Waterhouse PM . 2008 . RNA silencing in plants: yesterday, today, and tomorrow . Plant Physiology 147 , 456 – 468 . Google Scholar CrossRef Search ADS PubMed Endo TR . 1990 . Gametocidal chromosomes and their induction of chromosome mutations in wheat . Japanese Journal of Genetics 65 , 135 – 152 . Google Scholar CrossRef Search ADS Endo TR , Gill BS . 1996 . The deletion stocks of common wheat . Journal of Heredity 87 , 295 – 307 . Google Scholar CrossRef Search ADS Endo TR , Tsunewaki K . 1975 . Sterility of common wheat with Aegilops triuncialis cytoplasm . Journal of Heredity 66 , 13 – 18 . Google Scholar CrossRef Search ADS Endo TR , Yamamoto M , Mukai Y . 1994 . Structural changes of rye chromosome 1R induced by a gametocidal chromosome . Japanese Journal of Genetics 69 , 13 – 19 . Google Scholar CrossRef Search ADS Feller A , Machemer K , Braun EL , Grotewold E . 2011 . Evolutionary and comparative analysis of MYB and bHLH plant transcription factors . The Plant Journal 66 , 94 – 116 . Google Scholar CrossRef Search ADS PubMed Finch RA , Miller TE , Bennett MD . 1984 . ‘Cuckoo’ Aegilops addition chromosome in wheat ensures its transmission by causing chromosome breaks in meiospores lacking it . Chromosoma 90 , 84 – 88 . Google Scholar CrossRef Search ADS Gallego M , Virshup DM . 2005 . Protein serine/threonine phosphatases: life, death, and sleeping . Current Opinion in Cell Biology 17 , 197 – 202 . Google Scholar CrossRef Search ADS PubMed Gent JI , Ellis NA , Guo L , Harkess AE , Yao Y , Zhang X , Dawe RK . 2013 . CHH islands: de novo DNA methylation in near-gene chromatin regulation in maize . Genome Research 23 , 628 – 637 . Google Scholar CrossRef Search ADS PubMed Ghani MA , Li J , Rao L , Raza MA , Cao L , Yu N , Zou X , Chen L . 2014 . The role of small RNAs in wide hybridisation and allopolyploidisation between Brassica rapa and Brassica nigra . BMC Plant Biology 14 , 272 . Google Scholar CrossRef Search ADS PubMed Ha M , Lu J , Tian L , Ramachandran V , Kasschau KD , Chapman EJ , Carrington JC , Chen X , Wang XJ , Chen ZJ . 2009 . Small RNAs serve as a genetic buffer against genomic shock in Arabidopsis interspecific hybrids and allopolyploids . Proceedings of the National Academy of Sciences, USA 106 , 17835 – 17840 . Google Scholar CrossRef Search ADS Haag JR , Pikaard CS . 2011 . Multisubunit RNA polymerases IV and V: purveyors of non-coding RNA for plant gene silencing . Nature Reviews. Molecular Cell Biology 12 , 483 – 492 . Google Scholar CrossRef Search ADS PubMed Hsieh LC , Lin SI , Shih AC , Chen JW , Lin WY , Tseng CY , Li WH , Chiou TJ . 2009 . Uncovering small RNA-mediated responses to phosphate deficiency in Arabidopsis by deep sequencing . Plant Physiology 151 , 2120 – 2132 . Google Scholar CrossRef Search ADS PubMed Hu H , Gatti RA . 2011 . MicroRNAs: new players in the DNA damage response . Journal of Molecular Cell Biology 3 , 151 – 158 . Google Scholar CrossRef Search ADS PubMed Huang H , Jiao R . 2012 . Roles of chromatin assembly factor 1 in the epigenetic control of chromatin plasticity . Science China Life Sciences 55 , 15 – 19 . Google Scholar CrossRef Search ADS PubMed Kaneda T , Taga Y , Takai R , Iwano M , Matsui H , Takayama S , Isogai A , Che FS . 2009 . The transcription factor OsNAC4 is a key positive regulator of plant hypersensitive cell death . EMBO Journal 28 , 926 – 936 . Google Scholar CrossRef Search ADS PubMed Kasschau KD , Fahlgren N , Chapman EJ , Sullivan CM , Cumbie JS , Givan SA , Carrington JC . 2007 . Genome-wide profiling and analysis of Arabidopsis siRNAs . PLoS Biology 5 , e57 . Google Scholar CrossRef Search ADS PubMed Kenan-Eichler M , Leshkowitz D , Tal L , Noor E , Melamed-Bessudo C , Feldman M , Levy AA . 2011 . Wheat hybridization and polyploidization results in deregulation of small RNAs . Genetics 188 , 263 – 272 . Google Scholar CrossRef Search ADS PubMed Kim VN . 2005 . MicroRNA biogenesis: coordinated cropping and dicing . Nature Reviews. Molecular Cell Biology 6 , 376 – 385 . Google Scholar CrossRef Search ADS PubMed King IP , Laurie DA . 1993 . Chromosome damage in early embryo and endosperm development in crosses involving the preferentially transmitted 4S1 chromosome of Aegilops sharonensis . Heredity 70 , 52 – 59 . Google Scholar CrossRef Search ADS Kofman AV , Kim J , Park SY et al. 2013 . microRNA-34a promotes DNA damage and mitotic catastrophe . Cell Cycle 12 , 3500 – 3511 . Google Scholar CrossRef Search ADS PubMed Kozomara A , Griffiths-Jones S . 2014 . miRBase: annotating high confidence microRNAs using deep sequencing data . Nucleic Acids Research 42 , D68 – D73 . Google Scholar CrossRef Search ADS PubMed Kraitshtein Z , Yaakov B , Khasdan V , Kashkush K . 2010 . Genetic and epigenetic dynamics of a retrotransposon after allopolyploidization of wheat . Genetics 186 , 801 – 812 . Google Scholar CrossRef Search ADS PubMed Kuchar M , Fajkus J . 2004 . Interactions of putative telomere-binding proteins in Arabidopsis thaliana: identification of functional TRF2 homolog in plants . FEBS Letters 578 , 311 – 315 . Google Scholar CrossRef Search ADS PubMed Kurihara Y , Takashi Y , Watanabe Y . 2006 . The interaction between DCL1 and HYL1 is important for efficient and precise processing of pri-miRNA in plant microRNA biogenesis . RNA 12 , 206 – 212 . Google Scholar CrossRef Search ADS PubMed Lal A , Pan Y , Navarro F , Dykxhoorn DM , Moreau L , Meire E , Bentwich Z , Lieberman J , Chowdhury D . 2009 . miR-24-mediated downregulation of H2AX suppresses DNA repair in terminally differentiated blood cells . Nature Structural and Molecular Biology 16 , 492 – 498 . Google Scholar CrossRef Search ADS PubMed Le TN , Schumann U , Smith NA et al. 2014 . DNA demethylases target promoter transposable elements to positively regulate stress responsive genes in Arabidopsis . Genome Biology 15 , 458 . Google Scholar CrossRef Search ADS PubMed Lee Y , Kim M , Han J , Yeom KH , Lee S , Baek SH , Kim VN . 2004 . MicroRNA genes are transcribed by RNA polymerase II . EMBO Journal 23 , 4051 – 4060 . Google Scholar CrossRef Search ADS PubMed Li A , Liu D , Wu J et al. 2014 . mRNA and small RNA transcriptomes reveal insights into dynamic homoeolog regulation of allopolyploid heterosis in nascent hexaploid wheat . The Plant Cell 26 , 1878 – 1900 . Google Scholar CrossRef Search ADS PubMed Li SF , Iacuone S , Parish RW . 2007 . Suppression and restoration of male fertility using a transcription factor . Plant Biotechnology Journal 5 , 297 – 312 . Google Scholar CrossRef Search ADS PubMed Liu HH , Tian X , Li YJ , Wu CA , Zheng CC . 2008 . Microarray-based analysis of stress-regulated microRNAs in Arabidopsis thaliana . RNA 14 , 836 – 843 . Google Scholar CrossRef Search ADS PubMed Liu YX , Wang M , Wang XJ . 2014 . Endogenous small RNA clusters in plants . Genomics, Proteomics and Bioinformatics 12 , 64 – 71 . Google Scholar CrossRef Search ADS Llave C , Xie Z , Kasschau KD , Carrington JC . 2002 . Cleavage of Scarecrow-like mRNA targets directed by a class of Arabidopsis miRNA . Science 297 , 2053 – 2056 . Google Scholar CrossRef Search ADS PubMed Maan SS . 1975 . Exclusive preferential transmission of an alien chromosome in common wheat . Crop Science 15 , 287 – 292 . Google Scholar CrossRef Search ADS Martínez JG , Pérez-Escuredo J , Castro-Santos P , Marcos CA , Pendás JL , Fraga MF , Hermsen MA . 2012 . Hypomethylation of LINE-1, and not centromeric SAT-α, is associated with centromeric instability in head and neck squamous cell carcinoma . Cellular Oncology 35 , 259 – 267 . Google Scholar CrossRef Search ADS McKnight TD , Riha K , Shippen DE . 2002 . Telomeres, telomerase, and stability of the plant genome . Plant Molecular Biology 48 , 331 – 337 . Google Scholar CrossRef Search ADS PubMed Meloche S , Pouysségur J . 2007 . The ERK1/2 mitogen-activated protein kinase pathway as a master regulator of the G1- to S-phase transition . Oncogene 26 , 3227 – 3239 . Google Scholar CrossRef Search ADS PubMed Moxon S , Jing R , Szittya G , Schwach F , Rusholme Pilcher RL , Moulton V , Dalmay T . 2008 . Deep sequencing of tomato short RNAs identifies microRNAs targeting genes involved in fruit ripening . Genome Research 18 , 1602 – 1609 . Google Scholar CrossRef Search ADS PubMed Nasuda S , Friebe B , Gill BS . 1998 . Gametocidal genes induce chromosome breakage in the interphase prior to the first mitotic cell division of the male gametophyte in wheat . Genetics 149 , 1115 – 1124 . Google Scholar PubMed Ng DW , Lu J , Chen ZJ . 2012 . Big roles for small RNAs in polyploidy, hybrid vigor, and hybrid incompatibility . Current Opinion in Plant Biology 15 , 154 – 161 . Google Scholar CrossRef Search ADS PubMed Nishihama R , Ishikawa M , Araki S , Soyano T , Asada T , Machida Y . 2001 . The NPK1 mitogen-activated protein kinase kinase kinase is a regulator of cell-plate formation in plant cytokinesis . Genes and Development 15 , 352 – 363 . Google Scholar CrossRef Search ADS PubMed Ogihara Y , Hasegawa K , Tsujimoto H . 1994 . High-resolution cytological mapping of the long arm of chromosome 5A in common wheat using a series of deletion lines induced by gametocidal (Gc) genes of Aegilops speltoides . Molecular and General Genetics 244 , 253 – 259 . Google Scholar CrossRef Search ADS PubMed Pothof J , Verkaik NS , Hoeijmakers JH , van Gent DC . 2009a. MicroRNA responses and stress granule formation modulate the DNA damage response . Cell Cycle 8 , 3462 – 3468 . Google Scholar CrossRef Search ADS PubMed Pothof J , Verkaik NS , van IJcken W , Wiemer EA , Ta VT , van der Horst GT , Jaspers NG , van Gent DC , Hoeijmakers JH , Persengiev SP . 2009b. MicroRNA-mediated gene silencing modulates the UV-induced DNA-damage response . EMBO Journal 28 , 2090 – 2099 . Google Scholar CrossRef Search ADS PubMed Raffaele S , Vailleau F , Léger A , Joubès J , Miersch O , Huard C , Blée E , Mongrand S , Domergue F , Roby D . 2008 . A MYB transcription factor regulates very-long-chain fatty acid biosynthesis for activation of the hypersensitive cell death response in Arabidopsis . The Plant Cell 20 , 752 – 767 . Google Scholar CrossRef Search ADS PubMed Rhoades MW , Reinhart BJ , Lim LP , Burge CB , Bartel B , Bartel DP . 2002 . Prediction of plant microRNA targets . Cell 110 , 513 – 520 . Google Scholar CrossRef Search ADS PubMed Riechmann JL , Ratcliffe OJ . 2000 . A genomic perspective on plant transcription factors . Current Opinion in Plant Biology 3 , 423 – 434 . Google Scholar CrossRef Search ADS PubMed Rodriguez RE , Mecchia MA , Debernardi JM , Schommer C , Weigel D , Palatnik JF . 2010 . Control of cell proliferation in Arabidopsis thaliana by microRNA miR396 . Development 137 , 103 – 112 . Google Scholar CrossRef Search ADS PubMed Rosa M , Von Harder M , Cigliano RA , Schlögelhofer P , Mittelsten Scheid O . 2013 . The Arabidopsis SWR1 chromatin-remodeling complex is important for DNA repair, somatic recombination, and meiosis . The Plant Cell 25 , 1990 – 2001 . Google Scholar CrossRef Search ADS PubMed Shen Y , Zhang Z , Lin H , Liu H , Chen J , Peng H , Cao M , Rong T , Pan G . 2011 . Cytoplasmic male sterility-regulated novel microRNAs from maize . Functional and Integrative Genomics 11 , 179 – 191 . Google Scholar CrossRef Search ADS PubMed Shi F , Endo TR . 1997 . Production of wheat–barley disomic addition lines possessing an Aegilops cylindrica gametocidal chromosome . Genes and Genetic Systems 72 , 243 – 248 . Google Scholar CrossRef Search ADS Slotkin RK , Vaughn M , Borges F , Tanurdzić M , Becker JD , Feijó JA , Martienssen RA . 2009 . Epigenetic reprogramming and small RNA silencing of transposable elements in pollen . Cell 136 , 461 – 472 . Google Scholar CrossRef Search ADS PubMed Su WY , Cong WW , Shu YJ , Wang D , Xu GH , Guo CH . 2013 . Gametocidal chromosomes enhancing chromosome aberration in common wheat induced by 5-azacytidine . Genetics and Molecular Research 12 , 2227 – 2233 . Google Scholar CrossRef Search ADS PubMed Sugimoto-Shirasu K , Stacey NJ , Corsar J , Roberts K , McCann MC . 2002 . DNA topoisomerase VI is essential for endoreduplication in Arabidopsis . Current Biology 12 , 1782 – 1786 . Google Scholar CrossRef Search ADS PubMed Tang Z , Zhang L , Xu C , Yuan S , Zhang F , Zheng Y , Zhao C . 2012 . Uncovering small RNA-mediated responses to cold stress in a wheat thermosensitive genic male-sterile line by deep sequencing . Plant Physiology 159 , 721 – 738 . Google Scholar CrossRef Search ADS PubMed Tsujimoto H . 2005 . Gametocidal genes in wheat as the inducer of chromosome breakage . Frontiers of Wheat Bioscience 100 , 33 – 48 . Tsujimoto H , Tsunewaki K . 1985 . Gametocidal genes in wheat and its relatives. II. Suppressor of the chromosome 3C gametocidal gene of Aegilops triuncialis . Canadian Journal of Genetics and Cytology 27 , 178 – 185 . Google Scholar CrossRef Search ADS Voinnet O . 2009 . Origin, biogenesis, and activity of plant microRNAs . Cell 136 , 669 – 687 . Google Scholar CrossRef Search ADS PubMed Wan G , Mathur R , Hu X , Zhang X , Lu X . 2011 . miRNA response to DNA damage . Trends in Biochemical Sciences 36 , 478 – 484 . Google Scholar CrossRef Search ADS PubMed Wang XL , Li XB . 2009 . The GhACS1 gene encodes an acyl-CoA synthetase which is essential for normal microsporogenesis in early anther development of cotton . The Plant Journal 57 , 473 – 486 . Google Scholar CrossRef Search ADS PubMed Wang Y , Huang JW , Li M , Cavenee WK , Mitchell PS , Zhou X , Tewari M , Furnari FB , Taniguchi T . 2011 . MicroRNA-138 modulates DNA damage response by repressing histone H2AX expression . Molecular Cancer Research 9 , 1100 – 1111 . Google Scholar CrossRef Search ADS PubMed Wang Y , Sun F , Cao H , Peng H , Ni Z , Sun Q , Yao Y . 2012 . TamiR159 directed wheat TaGAMYB cleavage and its involvement in anther development and heat response . PLoS One 7 , e48445 . Google Scholar CrossRef Search ADS PubMed Wang Y , Taniguchi T . 2013 . MicroRNAs and DNA damage response: implications for cancer therapy . Cell Cycle 12 , 32 – 42 . Google Scholar CrossRef Search ADS PubMed Waterworth WM , Kozak J , Provost CM , Bray CM , Angelis KJ , West CE . 2009 . DNA ligase 1 deficient plants display severe growth defects and delayed repair of both DNA single and double strand breaks . BMC Plant Biology 9 , 79 . Google Scholar CrossRef Search ADS PubMed Wei M , Wei H , Wu M , Song M , Zhang J , Yu J , Fan S , Yu S . 2013 . Comparative expression profiling of miRNA during anther development in genetic male sterile and wild type cotton . BMC Plant Biology 13 , 66 . Google Scholar CrossRef Search ADS PubMed Wilting SM , Snijders PJ , Verlaat W et al. 2013 . Altered microRNA expression associated with chromosomal changes contributes to cervical carcinogenesis . Oncogene 32 , 106 – 116 . Google Scholar CrossRef Search ADS PubMed Wu L , Zhou H , Zhang Q , Zhang J , Ni F , Liu C , Qi Y . 2010 . DNA methylation mediated by a microRNA pathway . Molecular Cell 38 , 465 – 475 . Google Scholar CrossRef Search ADS PubMed Wu MF , Tian Q , Reed JW . 2006 . Arabidopsis microRNA167 controls patterns of ARF6 and ARF8 expression, and regulates both female and male reproduction . Development 133 , 4211 – 4218 . Google Scholar CrossRef Search ADS PubMed Xing S , Salinas M , Höhmann S , Berndtgen R , Huijser P . 2010 . miR156-targeted and nontargeted SBP-box transcription factors act in concert to secure male fertility in Arabidopsis . The Plant Cell 22 , 3935 – 3950 . Google Scholar CrossRef Search ADS PubMed Yan J , Zhang H , Zheng Y , Ding Y . 2015 . Comparative expression profiling of miRNAs between the cytoplasmic male sterile line MeixiangA and its maintainer line MeixiangB during rice anther development . Planta 241 , 109 – 123 . Google Scholar CrossRef Search ADS PubMed Yang X , Li L . 2011 . miRDeep-P: a computational tool for analyzing the microRNA transcriptome in plants . Bioinformatics 27 , 2614 – 2615 . Google Scholar CrossRef Search ADS PubMed Zaratiegui M , Irvine DV , Martienssen RA . 2007 . Noncoding RNAs and gene silencing . Cell 128 , 763 – 776 . Google Scholar CrossRef Search ADS PubMed Zhai J , Zhang H , Arikit S , Huang K , Nan GL , Walbot V , Meyers BC . 2015 . Spatiotemporally dynamic, cell-type-dependent premeiotic and meiotic phasiRNAs in maize anthers . Proceedings of the National Academy of Sciences, USA 112 , 3146 – 3151 . Google Scholar CrossRef Search ADS Zhang B , Wang Q . 2015 . MicroRNA-based biotechnology for plant improvement . Journal of Cellular Physiology 230 , 1 – 15 . Google Scholar CrossRef Search ADS PubMed Zhang H , Zhu JK . 2011 . RNA-directed DNA methylation . Current Opinion in Plant Biology 14 , 142 – 147 . Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Experimental Botany Oxford University Press

Uncovering key small RNAs associated with gametocidal action in wheat

Loading next page...
 
/lp/ou_press/uncovering-key-small-rnas-associated-with-gametocidal-action-in-wheat-wJ8rZ5Hf1b
Publisher
Oxford University Press
Copyright
© The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com
ISSN
0022-0957
eISSN
1460-2431
D.O.I.
10.1093/jxb/ery175
Publisher site
See Article on Publisher Site

Abstract

Abstract Gametocidal (Gc) chromosomes can kill gametes that lack them by causing chromosomal breakage to ensure their preferential transmission, and they have been exploited in genetic breeding. The present study investigated the possible roles of small RNAs (sRNAs) in Gc action. By sequencing two small RNA libraries from the anthers of Triticum aestivum cv. Chinese Spring (CS) and the Chinese Spring-Gc 3C chromosome monosomic addition line (CS-3C), we identified 239 conserved and 72 putative novel miRNAs, including 135 differentially expressed miRNAs. These miRNAs were predicted to target multiple genes with various molecular functions relevant to the features of Gc action, including sterility and genome instability. The transgenic overexpression of miRNA, which was up-regulated in CS-3C, reduced rice fertility. The CS-3C line exhibited a genome-wide reduction in 24 nt siRNAs compared with that of the CS line, particularly in transposable element (TE) and repetitive DNA sequences. Corresponding to this reduction, the bisulfite sequencing analysis of four retro-TE sequences showed a decrease in CHH methylation, typical of RNA-directed DNA methylation (RdDM). These results demonstrate that both miRNA-directed regulation of gene expression and siRNA-directed DNA methylation of target TE loci could play a role in Gc action. Chromosomal breakage, DNA methylation, fertility, gametocidal action, microRNA, siRNA, Triticum aestivum Introduction Gametocidal (Gc) genes, which are introduced into common wheat from related Aegilops species, are strong segregation distorters that affect plant fertility (Endo and Tsunewaki, 1975; Maan, 1975; Endo, 1990). Wheat lines that are heterozygous or hemizygous for a Gc chromosome can produce two types of gametes, one with and the other without the Gc chromosome, and only those with the Gc chromosome are functional, ensuring their preferential transmission (Endo and Tsunewaki, 1975; Maan, 1975; Endo, 1990). The gametes without the Gc chromosome are killed due to chromosomal aberrations induced by Gc chromosomes in the G1 phase prior to DNA synthesis of the first post-meiotic mitosis, including chromatid fragments, dicentric chromosomes, and chromatin bridges (Finch et al., 1984; King and Laurie, 1993; Nasuda et al., 1998). Consequently, the fertility of wheat lines with heterozygous or hemizygous Gc chromosomes declines. This genetic phenomenon is of great interest to breeders, who use the Gc chromosome as an effective breeding tool. Breeders have used the Gc chromosome to produce chromosome deletion stocks in common wheat and to induce rearrangement of alien chromosomes added to common wheat, which are highly significant for mapping genes and molecular markers on wheat chromosomes and for breeding high-quality wheat using the genetic resources of wheat and its related species (Endo et al., 1994; Ogihara et al., 1994; Endo and Gill, 1996; Shi and Endo, 1997). However, currently, very little is known about the molecular mechanisms underlying the action of Gc chromosomes. Several hypotheses have been proposed for the mode of action of the Gc locus, including the dual-function model, in which the Gc genes have both ‘breaking’ and ‘protecting’ functions (Endo, 1990; Tsujimoto, 2005). Tsujimoto (2005) proposed that the Gc genes produce both restriction enzymes (REs) and modification enzymes (MEs), such as methylase. The REs cleave specific restriction sites unless the sites are modified by the MEs (Endo, 1990; Tsujimoto, 2005). This model explains the dual function of Gc chromosomes. However, the study did not provide compelling experimental evidence to support it. Furthermore, studies have reported that DNA methylation can repress chromosome breakages induced by the Gc factors (de Las Heras et al., 2001; Su et al., 2013). Recently, small non-coding RNAs have emerged as important regulators of gene expression and genome stability in eukaryotes that can direct both transcriptional and post-transcriptional gene silencing (Carthew and Sontheimer, 2009). Plant endogenous small RNAs (sRNAs) can be categorized into several classes, with the most prominent being the miRNAs and siRNAs (Eamens et al., 2008). Most plant miRNAs are derived from primary miRNA transcripts (pri-miRNA) transcribed by RNA polymerase II, which form short stem–loop structures that are processed by Dicer-like 1 to generate mature 20–24 nt miRNA (Lee et al., 2004; Kim, 2005; Kurihara et al., 2006; Eamens et al., 2008; Voinnet, 2009). The mature miRNAs are loaded into the protein factor Argonaute to form RNA-induced silencing complexes in order to cleave target mRNA molecules or repress their translation (Baumberger and Baulcombe, 2005; Brodersen et al., 2008; Tang et al., 2012). The miRNAs participate in various aspects of plant development and in a series of biological processes, including RNA-directed DNA methylation (RdDM) (Rhoades et al., 2002; Liu et al., 2008; Wu et al., 2010; Zhang and Wang, 2015). Increasing evidence suggests that miRNAs may also participate in the development of the anther to regulate male sterility, as reported in wheat, rice, cotton, and maize (Shen et al., 2011; Tang et al., 2012; Wei et al., 2013; Yan et al., 2015; Zhai et al., 2015). In animals, the miRNAs have been implicated in regulating the DNA damage response (Pothof et al., 2009a; Hu and Gatti, 2010; Wan et al., 2011; Wang and Taniguchi, 2013). For example, miR-138 and miR-24 in human tumor cells modulate the DNA damage response by suppressing the expression of histone H2AX (Lal et al., 2009; Wang et al., 2011). The miRNA can also modulate the UV-induced DNA damage response and DNA repair gene expression in HeLa cells under hypoxic stress (Crosby et al., 2009; Pothof et al., 2009b). Wilting et al. (2013) reported that altered expression of miRNAs in a human cervical carcinoma cell line is associated with frequent chromosomal alterations (Wilting et al., 2013). Moreover, miR-34a can increase the frequency of chromosome breaks and disturb mitotic progression in irradiated cells (Kofman et al., 2013). However, the role of miRNAs in DNA damage repair in plants remains unclear. Among the several classes of siRNAs, the 24 nt size class is unique as it directs RdDM, a plant-specific transcriptional gene silencing mechanism (Haag and Pikaard, 2011; Zhang and Zhu, 2011; Ng et al., 2012). Therefore, the 24 nt siRNAs play an important role in genome reconstruction and stabilization by maintaining transcriptional silencing of transposable elements (TEs) and repetitive sequences in the genome (Ha et al., 2009; Ghani et al., 2014). Furthermore, the majority of endogenous plant siRNAs are 24 nt long, and are primarily produced from the TEs and DNA repeats (Kasschau et al., 2007; Liu et al., 2014). In the newly synthesized hexaploid wheat, the number of 24 nt siRNAs corresponding to the TEs was significantly reduced compared with its parental tetraploid and diploid wheat, and this was accompanied by a decrease in CpG methylation, which may have contributed to genomic instability in the allopolyploids during the early stage of their formation (Kenan-Eichler et al., 2011). Similar to that of the 24 nt siRNAs in plants, piRNAs in Drosophila may participate in hybrid dysgenesis (Brennecke et al., 2008). The function of sRNAs in genome stability and gene expression made us assume that they might also participate in Gc action. To investigate this hypothesis, two sRNA libraries were constructed from Triticum aestivum cv. Chinese Spring (CS) and the Chinese Spring-Gc 3C chromosome monosomic addition line (CS-3C), and sequenced by the Illumina high-throughput sequencing technology. The bioinformatic analysis of the sequencing data, followed by quantitative real-time PCR (qRT-PCR) analysis of miRNA expression and bisulfite sequencing analysis of DNA methylation uncovered both miRNA-directed regulation of target gene expression and siRNA-directed DNA methylation of target TE loci that could play a role in Gc action. The present study has opened up a new avenue for uncovering the underlying molecular mechanism of Gc action during the development of the wheat anther. Materials and methods Plant materials and growth conditions Triticum aestivum cv. Chinese Spring (CS, AABBDD, 2n=42) and the Chinese Spring-gametocidal 3C chromosome monosomic addition line (CS-3C, AABBDD+3C I, 2n=43) that carries a gametocidal chromosome 3C derived from Aegilops triuncialis were used. Seed of the Chinese Spring-gametocidal 3C chromosome disomic addition line (CS-3C3C, AABBDD+3C II, 2n=44) that carries two gametocidal chromosome 3Cs was kindly provided by Endo (National BioResource Project-Wheat, Japan), and seeds of CS-3C were obtained from crosses between CS (female parent) and CS-3C3C (male parent). Seeds of CS and CS-3C were planted in a 1:1:1 (w/w/w) mixture of peat, sand, and soil, and were kept in a greenhouse at 18–20 °C with a 16 h/8 h light/dark photoperiod. Cytological observations and sample collection Anthers whose pollen was in the interphase prior to the first mitotic cell division were selected after staining with 1% acetocarmine. Slides were prepared using the acetocarmine squash method and they were viewed with a Leica DM 750 microscope. For RNA and DNA analysis, anthers were immediately frozen in liquid nitrogen after collection, and stored at –80 °C. RNA isolation and Illumina sequencing Both samples were harvested from two independent biological replicates. Total RNA was isolated from pooled anthers using TRIZOL reagent (Invitrogen, CA, USA) according to the manufacturer’s instructions, and the quantity and quality of total RNA were measured using an Agilent 2100 Bioanalyzer. RNA samples were sent to BGI (BGI-Shenzhen, Shenzhen, China) for sRNA sequencing using an Illumina Genome Analyzer. Preliminary processing of sequencing reads Raw sequence reads were processed into clean reads after filtering out low-quality reads, trimming the adaptor sequences, and removing other noise reads. To identify miRNAs, the reads that were mapped to the known non-coding RNAs (rRNA, tRNA, snRNA, etc.) in the Rfam database were discarded. The remaining reads were mapped to the A, B, and D genome using the BOWTIE2 software with the Triticum_aestivum.IWGSC2.26 (http://ensemblgenomes.org/) as the reference genome. Mapped reads were further analyzed to identify conserved and novel miRNAs. For siRNAs, all the 24 nt sRNA reads were extracted for further analysis. Identification of conserved and novel miRNAs To identify conserved miRNAs, mapping reads were retrieved and aligned using BLASTN against known plant mature miRNA sequences in miRBase V21 (Kozomara and Griffiths-Jones, 2014), and sRNA reads showing identical sequences to the known miRNAs were selected as the conserved miRNAs. For identification of novel miRNAs, the remaining reads were aligned to the wheat reference genome. Flanking sequences of perfectly matched reads plus the sRNA sequence were extracted using BOWTIE2 and MIREAP, and pre-miRNA-like secondary structures were predicted using RNAFOLD (Denman, 1993). If the sequences were predicted to form a stem–loop structure, the sRNAs were considered as putative novel miRNAs. miRNA abundance was evaluated and normalized using the tags per million reads (TPM) method, based on BLASTN mapping results. The miRNA expression fold changes between CS-3C and CS (CS-3C/CS) were computed and the miRNAs with fold change (TPM ratios) ≥1.5 or ≤0.67 were screened as differentially expressed miRNAs. Identification and differential expression analysis of siRNA After removing the 24 nt miRNAs from the 24 nt reads, the remaining 24 nt sRNA reads were mapped to the A, B, and D genomes using BOWTIE2 with the Triticum_aestivum.IWGSC2.26 as the reference genome. We then used the mapped siRNA reads within a 1 kb sliding genome window to examine the distribution and the densities of siRNA. Differential siRNA sliding windows (clustered siRNA locus) were then identified that contained a minimum of 20 siRNA reads in a 1 kb region in either library with fold change (FC) ≥1.5 or ≤0.67 between CS-3C and CS. Annotation of genomic loci containing differential siRNAs was carried out by aligning the siRNA-associated sequences with the Triticum_aestivum.IWGSC2.26.chromosome.gff3 database. The distribution patterns of siRNAs in the 3 kb up- and downstream regions as well as the transcribed regions of genes were studied. Gene Ontology (GO) analysis was then conducted on genes with differentially expressed siRNAs in the transcribed and 1 kb upstream regions (with a minimum of 10 siRNA reads in either library and FC ≥1.5 or ≤0.67), using the web-based tool and database AgriGO (http://bioinfo.cau.edu.cn/agriGO/). GO terms with corrected false discovery rate (FDR) <0.05 were considered significantly enriched. TaqMan assay for miRNA validation We used the Applied Biosystems TaqMan miRNA assays to detect and quantify the miRNAs according to the manufacturer’s protocol, with three technical replicates and two independent biological replicates for each plant line (Chen et al., 2005). The small nuclear RNA U6 was used as the internal reference gene for normalization. Briefly, 5.0 μl of total RNA was incubated with 7.0 μl of RT-MIX including 1.5 μl of 10× Reverse Transcription buffer, 0.15 μl of deoxyribonucleotide triphosphates (100 mM, with dTTP), 0.19 μl of RNase inhibitor (20 U μl−1), 1 μl of MultiScribe™ reverse transcriptase, and 4.16 μl of nuclease-free water, then 3 μl of 5× stem–loop reverse transcription primer (Applied Biosystems) was added to the mix to set up a 15 μl reaction mixture. Real-time PCR for each assay contained 1 μl of cDNA, 10 μl of TaqMan® 2× Universal PCR master mix II (No AmpErase® UNG), 1 μl of 20× TaqMan MicroRNA assay mix including miRNA-specific primers and the TaqMan probe, and 8 μl of nuclease-free water. The reaction was performed in an Applied Biosystems (USA) 7500Fast Real-Time PCR System on 96-well plates at 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 60 s. Target mRNA prediction and GO function analysis of targets The web-based psRNATarget program (http://plantgrn.noble.org/psRNATarget/) was used to identify putative targets for differentially expressed miRNAs with default parameters. The analysis was performed using two different transcript libraries for target search: Triticum aestivum unigenes from DFCI Gene Index (TAGI) version 12 and T. aestivum cDNA from EbsemblPlants. GO analysis of the putative targets predicted from the wheat cDNA from EbsemblPlants was conducted using AgriGO, with a corrected FDR <0.05 as an indication of significant enrichment. Validation of predicted target genes by real-time reverse transcription–PCR (RT–PCR) Briefly, first-strand cDNA synthesis was performed by using a ReverTra Ace qPCR RT kit (Toyobo Life Science, Shanghai, China) according to the manufacturer’s instructions. Real-time PCR was carried out using SYBR Green and monitored on an ABI 7300 Real-Time PCR System (Applied Biosystems). TaGAPDH was used as a reference gene for each sample, and the real-time PCR mixture was performed in technical triplicate. For each qRT-PCR, 1.4 μl of cDNA was used in a 20.0 μl PCR system, containing 0.6 μl of each primer (10 μM), 7.0 μl of dH2O, 10.0 μl of SYBR® qPCR Mix, and 0.4 μl of 50× ROX reference dye (Toyobo Life Science), and a dissociation stage was generated by the software to analyze the melting curve. Validation of miRNA-guided cleavage of target mRNA by 5' RACE Target mRNA was validated by a modified version of 5' RACE (Llave et al., 2002) using the 5'-Full RACE Kit (Takara, Dalian, China). By using the 5' RACE primer provided in the kit, and gene-specific primers (Supplementary Table S9 at JXB online) designed by primer 5.0, the cleavage products were detected by nested PCR. The cleavage products were cloned into the pMD-18T vector (Takara) and sequenced. Preparation of the Ubi:pre-miR9657b-3p construct and rice transformation A 207 bp genomic sequence containing the pre-miR9657b-3p fold-back region was amplified from wheat genomic DNA by PCR. A modified binary vector, pCAMBIA-1305.1 (pOx) (provided by Yaoguang Liu), which carries a maize ubiquitin (Ubi) promoter, was used to generate a construct for overexpression of pre-miR9657b-3p. Subsequently, the recombinant expression plasmids were introduced into Agrobacterium tumefaciens strain EHA105 and used for rice (Oryza sativa spp. japonica var. Nipponbare) transformation. Putative transgenic lines were selected for further confirmation by PCR. A highly sensitive real-time stem–loop RT–PCR procedure was used to investigate the expression of the mature miR9657b-3p. qRT-PCR was also performed to measure the expression abundance of the target gene (TC492017) of miR9657b-3p in rice. RNA isolation and quantitative real-time RT–PCR were conducted in the same way as described above. In order to study the effect of tae-miR9657b-3p overexpression on plant fertility, we analyzed morphological traits of transgenic rice plants. The number of spikelets per panicle, seed setting percentage, and filled grain number were measured. The pollen germination rate was determined by visualization using a Leica DM 750 microscope (Leica). The collected fresh mature pollen was incubated at 28–30 °C. A liquid germination medium [20% sucrose, 10% PEG4000, 3.0 mmol l−1 Ca (NO3)2, 3.0 mg l−1 vitamin B1, and 40 mg l−1 H3BO3] was used for rice pollen germination using an optimized procedure with 30–40 min culture time. Bisulfite sequencing analysis Wheat DNA was extracted using the DNAsecure Plant Kit (TIANGEN, Beijing, China). DNA bisulfite modification was performed by using the EZ DNA Methylation-Gold™ kit (Zymo Research, CA, USA) according to the manufacturer’s instructions. PCR and nested PCR primers (degenerate primers) (Supplementarty Table S9) were designed by Primer 5.0, and the online software methprimer (http://www.urogene.org/cgi-bin/methprimer/methprimer.cgi) was used to predict CpG islands. PCR products were purified and directly sequenced as described in Le et al. (2014). Trace file data of the sequenced PCR products were opened using the BioEdit software (http://www.mbio.ncsu.edu/bioedit/bioedit.html) and exported to Microsoft Excel by using the ‘Export trace values (tab-delimited text)’ feature. The relative peak heights of cytosines and thymines were calculated to indicate the relative degree of methylation at each cytosine location, and the quality of bisulfite conversion was indicated by the PCR product of the wheat chloroplast psaA gene. Accession number All the sequencing data were uploaded to the Sequence Read Archive (SRA) database with accession no. SRP102555. Results Sequencing of sRNA libraries and data analysis Two sRNA libraries were constructed using the total RNA from CS and CS-3C wheat lines and were sequenced. After removing the adaptor sequences, poor-quality reads and those with inserts smaller than 18 nucleotides, 13481233 and 12699141 high-quality clean reads were obtained from CS and CS-3C, respectively (Supplementary Table S1). These included 70.11% reads that were common to both the samples, and 16.27% and 13.62% that were specific to CS and CS-3C, respectively (Supplementary Fig. S1a). Consistent with the findings of previous studies (Moxon et al., 2008; Hsieh et al., 2009), 24 nt (62.2%) and 21 nt (18.3%) sRNAs were the two most abundant sRNA size classes in the total clean sRNA reads (Supplementary Fig. S1b). Furthermore, the 24 nt class (76.4%) was the dominant species among the unique sRNA reads (SupplementaryFig. S1c). Collectively, the two libraries contained 9170210 unique reads (Supplementary Table S1). For 24 nt siRNAs, all reads were extracted, resulting in 8605226 and 7673544 high-quality clean 24 nt reads of the CS and CS-3C lines, respectively (Supplementary Table S1; Supplementary Fig. S1b). To identify the miRNAs, we first removed the sRNAs matching known non-coding RNAs (rRNA, tRNA, snRNA, snoRNA, and other ncRNAs); 366833 and 563185 total sRNAs of CS and CS-3C libraries were removed, respectively (Supplementary Table S1). The remaining sRNAs were mapped against the wheat reference genomic sequences (Triticum_aestivum.IWGSC2.26) using BOWTIE2, and the matching sRNA reads were analyzed to identify conserved and novel miRNAs. Identification of miRNAs To identify the miRNAs in the sequencing data, we downloaded miRNA sequences from the miRBase (version 21) (http://www.mirbase.org/) and conducted a local BLASTN search of known plant miRNAs with the sRNA reads matching the wheat reference genomic sequences. We identified 239 miRNAs, belonging to 115 miRNA families as described previously in other plant species (Supplementary Table S2). The read number for the majority of these miRNAs was relatively small in the sequencing data, possibly due to the low expression levels, tissue-specific expression, or relatively low coverage of the sequencing data. However, the expression levels of several miRNA families, such as miR167, miR156, and miR9863, were significantly high (Supplementary Table S2). The relative abundance of different members of miRNA families, such as the miR167 and miR156 families, also varied significantly (Supplementary Table S2). To identify novel miRNAs (nov-miRNAs), we removed the known miRNA reads from the wheat reference genome matching sRNAs, aligned the remaining unannotated reads to the wheat reference genomic sequences, and extracted the flanking genomic sequences of the perfectly matched reads using miRDEEP-P (Yang and Li, 2011). The sRNA sequences plus the extracted surrounding sequences were then used to predict the miRNA precursor-like stem–loop structures and putative miRNAs using the Mireap program with default plant parameters. Finally, 72 putative nov-miRNAs were predicted from the two libraries (Supplementary Table S3). Differential expression of conserved and novel miRNAs between CS-3C and CS Among the 311 miRNAs (including 239 conserved miRNAs and 72 nov-miRNAs), 135 miRNAs (43.4%) (including 31 nov-miRNAs) exhibited differential accumulation between CS-3C and CS, with a CS-3C to CS FC of ≥1.5 (up-regulation, 74 miRNAs) or ≤0.67 (down-regulation, 61 miRNAs; Table 1; Fig. 1a). The remaining 176 miRNAs (56.6%) were equally expressed between the two samples (Fig. 1a). Table 1. The differentially expressed miRNAs in gametocidal action miRNA family Name Sequences (5'–3') TPMa Log2 FCb Typical homology in miRBase CS CS-3C miR1120 miR1120a ACAUUCUUAUAUUAUGAGACGGAG 0.67 0.16 –2.08 tae-miR1120a miR1133 miR1133 CAUAUACUCCCUCCGUCCGAAA 0.82 1.50 0.87 tae-miR1133 miR1139 miR1139 GAGUAACAUACACUAGUAACA 0.89 1.57 0.82 bdi-miR1139 miR1508 miR1508c UAGAAAGGGAAAUAGCAGUUG 1.19 2.05 0.79 gma-miR1508c miR156 miR156 UUUGACAGAAGAUAGAGAGCAC 0.96 1.89 0.97 bcy-miR156 miR156s UGACAGAAGAGAGUGAGCACU 0.45 1.26 1.50 gma-miR156s miR156j UGACAGAAGAGGGUGAGCAC 0.37 1.26 1.76 mtr-miR156j miR159 miR159b-3p UUUGGAUUGAAGGGAGCUCUU 2.15 4.88 1.18 aly-miR159b-3p miR159a-3p CUUGGAUUGAAGGGAGCUCU 1.56 3.70 1.25 bdi-miR159a-3p miR159a UUUGGAUUGAAGGGAGCUCUG 147.24 283.80 0.95 tae-miR159a miR160 miR160c-3p GCGUGCAAGGAGCCAAGCAUG 0.89 2.13 1.26 ata-miR160c-3p miR160 UGCCUGGCUCCCUGUAUGCCA 3.04 0.63 –2.27 tae-miR160 miR160a-3p GCGUGCGAGGAGCCAAGCAUG 0.22 1.18 2.41 bdi-miR160a-3p miR164 miR164a-5p UGGAGAAGCAGGGCACGUGCU 3.86 1.97 –0.97 ata-miR164a-5p miR166 miR166d-5p GGAAUGUUGUCUGGCUCGGGG 5.19 10.71 1.04 ata-miR166d-5p miR166e-5p GGAAUGUUGUCUGGUUGGAGA 0.37 2.28 2.62 ata-miR166e-5p miR166e-3p CUCGGACCAGGCUUCAUUCCC 0.74 0.31 –1.24 bdi-miR166e-3p miR166i UUGGACCAGGCUUCAUUCCCC 0.96 1.50 0.63 mes-miR166i miR166e-3p UCGAACCAGGCUUCAUUCCCC 0.37 0.87 1.22 osa-miR166e-3p miR167 miR167b-3p AGGUCAUGCUGGAGUUUCAUC 0.52 2.36 2.19 ata-miR167b-3p miR167d-5p UGAAGCUGCCAGCAUGAUCUGA 7317.95 4706.54 –0.64 ata-miR167d-5p miR167e-5p UGAAGCUGCCAGCAUGAUCUA 2585.59 1681.22 –0.62 ata-miR167e-5p miR167f-3p CAGAUCAUGCUGCAGCUUCAU 3.49 1.65 –1.08 ata-miR167f-3p miR167a UGAAGCUGCCAGCAUGAUCUAA 6.38 3.78 –0.76 tae-miR167a miR167c UGAAGCUGCCAGCAUGAUCUU 38.94 23.70 –0.72 cpa-miR167c miR167j UGAAGCUGCCAGCAUGAUCUUA 2.15 0.94 –1.19 mdm-miR167j miR167c UGAAGCUGCCAGCAUGAUCUGG 2.15 0.71 –1.60 nta-miR167c miR167b-5p UAAAGCUGCCAGCAUGAUCUGG 1.11 0.71 –0.65 sly-miR167b-5p miR167c UGAAGCUGCCAGCAUGAUCUC 2.74 1.34 –1.04 vvi-miR167c miR168 miR168-3p CCCGCCUUGCACCAAGUGAAU 1.26 5.83 2.21 ata-miR168-3p miR169 miR169d-5p CAGCCAAGGAUGACUUGCCGG 3.34 1.50 –1.16 ata-miR169d-5p miR169g-3p GGCGAGUUGUUCUUGGCUACA 0.89 0.47 –0.91 ata-miR169g-3p miR171 miR171c-5p CGGUAUUGGUGCGGUUCAAUC 0.89 4.09 2.20 ata-miR171c-5p miR171d-5p UGUUGGCUCGACUCACUCAGA 0.74 2.52 1.76 ata-miR171d-5p miR171a UUGAGCCGUGCCAAUAUCACU 0.67 1.18 0.82 smo-miR171a miR172 miR172b-3p AGAAUCUUGAUGAUGCUGCAU 32.12 19.69 –0.71 ata-miR172b-3p miR172b-5p GCAGCACCACCAAGAUUCACA 0.01 0.94 6.56 ata-miR172b-5p miR172b GGGAAUCUUGAUGAUGCUGCA 0.15 0.63 2.09 cpa-miR172b miR1878 miR1878-3p AUUUGUAGUGUUCAGAUUGAGUUU 12.02 7.95 –0.60 bdi-miR1878-3p miR2118 miR2118b-3p GGGAAUGGGAACAUGGAGGAA 0.30 0.55 0.89 ata-miR2118b-3p miR2118b-5p UUCCCGAUGCCUCCCAUUCCUA 1.34 5.43 2.02 ata-miR2118b-5p miR2118d-3p UUCCUGAUGCCUCCCAUGCCUA 0.37 0.63 0.76 ata-miR2118d-3p miR2118b UUCCUGAUGCCUCCCAUUCCUA 0.59 2.83 2.26 bdi-miR2118b miR2118q UUCCCGAUGCCUCCUAUUCCUA 0.52 2.52 2.28 osa-miR2118q miR2118e UUCCUGAUGUCUCCCAUUCCUA 0.82 1.57 0.95 zma-miR2118e miR2120 miR2120 AAAGAUCUUUAGUCCCGGUUGUUC 2.30 0.87 –1.41 osa-miR2120 miR2275 miR2275b UUCAGUUUCUUCUAAUAUCUCA 4.23 8.66 1.03 bdi-miR2275b miR2275-3p UUUGGUUUCCUCCAAUAUCUCG 1.93 0.79 –1.29 tae-miR2275-3p miR319 miR319-3p ACUGGAUGACGCGGGAGCUAA 125.58 209.15 0.74 ata-miR319-3p miR319b-3p UUGGACUGAAGGGUGCUCCCU 3.34 0.55 –2.60 bdi-miR319b-3p miR3446 miR3446-5p CUCGGAAGCUAGACGUGUGGCAGG 13.35 27.80 1.06 aly-miR3446-5p miR3711 miR3711 UGGCGCUAGAAGGAGGGCCU 1.11 2.76 1.31 pab-miR3711 miR393 miR393-5p UUCCAAAGGGAUCGCAUUGAU 164.82 95.36 –0.79 ata-miR393-5p miR393b-3p UCAGUGCAAUCCCUUUGGAAU 5.79 3.23 –0.84 bdi-miR393b-3p miR3947 miR3947-5p UUAUUUCAGUAGACGACGUCACA 0.52 0.24 –1.14 csi-miR3947-5p miR395 miR395b UGAAGUGUUUGGGGGAACUC 0.67 0.08 –3.08 tae-miR395b miR396 miR396b-5p UCCACAGGCUUUCUUGAACUG 126.18 71.66 –0.82 ata-miR396b-5p miR396c-5p UUCCACAGCUUUCUUGAACUU 23.29 14.25 –0.71 ata-miR396c-5p miR396d-3p GUUCAAGAAAGCCCAUGGAAA 0.07 0.79 3.41 ata-miR396d-3p miR396e-3p GUUCAAUAAAGCUGUGGGAAA 1.11 2.60 1.22 ata-miR396e-3p miR396-5p AACUGUGAACUCGCGGGGAUG 5.49 2.83 –0.95 tae-miR396-5p miR397 miR397b-5p AUUGAGUGCAGCGUUGAUGAA 7.42 4.02 –0.89 bdi-miR397b-5p miR397-5p AUUGAGUGCAGCGUUGAUGAC 0.59 0.31 –0.91 stu-miR397-5p miR4384 miR4384 AAUCAGACACUGCAUUCAAAGACG 2.74 1.50 –0.88 gma-miR4384 miR444 miR444b UGCAGUUGCUGCCUCAAGCUU 6.45 2.05 –1.66 bdi-miR444b miR444b UGCAGUUGCUGUCUCAAGCUU 2.89 0.55 –2.39 hvu-miR444b miR5049 miR5049-3p AAUAUGGAUCGGAGGGAGUAC 3.78 7.40 0.97 tae-miR5049-3p miR5062 miR5062b-3p UGAACCUUAGGGAAAAGCCGCAU 1107.39 674.22 –0.72 ata-miR5062b-3p miR5062b-5p GCGGAUUUUUCACCAAGAUUCAAG 0.74 3.62 2.29 ata-miR5062b-5p miR5062-5p UGAACCUUAGGGAACAGCCGCAU 709.13 384.51 –0.88 tae-miR5062-5p miR5071 miR5071 UCAAGCAUCAUAUCGUGGACA 221.20 135.13 –0.71 osa-miR5071 miR5084 miR5084-3p ACCAUACGGUACUGCAGAGGAUC 35.90 22.52 –0.67 ata-miR5084-3p miR5084-5p AUCCUCUACAGUACUGUACGGUGC 12.68 8.19 –0.63 ata-miR5084-5p miR5169 miR5169b UUUGACCAAGUUUGUAGAACA 7.05 4.65 –0.60 bdi-miR5169b miR5175 miR5175-5p UUCCAAAUUACUCGUCGUGGU 25.15 12.13 –1.05 tae-miR5175-5p miR528 miR528-5p UGGAAGGGGCAUGCAGAGGAG 17.51 9.84 –0.83 ata-miR528-5p miR5387 miR5387b CGUGGCUCUGACCGGUGCUAAAGG 0.30 0.47 0.67 sbi-miR5387b miR6179 miR6179 AACCAGUCGAGGCCAGGGGGUU 0.52 0.94 0.86 hvu-miR6179 miR7714 miR7714-3p CUAAUAUGUAUCGAAGGGAGUAGC 1.34 2.05 0.62 bdi-miR7714-3p miR7757 miR7757-5p.2 CUUCCAUAUCAAAUCAUCUCU 0.22 0.55 1.31 bdi-miR7757-5p.2 miR8028 miR8028-5p UCCUUAUGCUACAAUUGUGAACAA 0.52 0.24 –1.14 stu-miR8028-5p miR8175 miR8175 GAUCCCCGGCAACGGCGCCA 10.53 28.11 1.42 ath-miR8175 miR827 miR827 UUAGAUGACCAUCAGCAAACA 12.76 19.69 0.63 ssp-miR827 miR837 miR837-3p AAACGAACAAAAAACUGAUGG 0.59 0.16 –1.91 ath-miR837-3p miR845 miR845 UGCUCUGAUACCAAUUGUUGG 0.52 0.79 0.60 bdi-miR845 miR894 miR894 CGUUUCACGUCGGGUUCACC 1.41 2.99 1.09 ppt-miR894 miR9653 miR9653b UGGCCAAGGUCUCUUGAGGCU 0.59 0.31 –0.91 tae-miR9653b miR9654 miR9654a-3p UUCUGAAAGGCUUGAAGCGAAU 1.48 0.79 –0.91 tae-miR9654a-3p miR9654b-3p UUCCGAAAGGCUUGAAGCGAAU 3.12 1.57 –0.98 tae-miR9654b-3p miR9655 miR9655-3p CAAGGGAAGGAAGUAGCCAAC 0.30 0.63 1.09 tae-miR9655-3p miR9656 miR9656-3p CUUCGAGACUCUGAACAGCGG 0.89 1.65 0.89 tae-miR9656-3p miR9657 miR9657b-3p CGUGCUUCCUCGUCGAACGGU 0.01 5.83 9.19 tae-miR9657b-3p miR9661 miR9661-5p UGAAGUAGAGCAGGGACCUCA 61.94 37.88 –0.71 tae-miR9661-5p miR9664 miR9664-3p UUGCAGUCCUCGAUGUCGUAG 9.35 5.51 –0.76 tae-miR9664-3p miR9669 miR9669-5p UACUGUGGGCACUUAUUUGAC 2.08 5.43 1.39 tae-miR9669-5p miR9670 miR9670-3p AGGUGGAAUACUUGAAGAAGA 19.06 96.07 2.33 tae-miR9670-3p miR9672 miR9672-5p CUUAAUGACAGUCGUGGUGUC 6.53 1.42 –2.20 ata-miR9672-5p miR9673 miR9673-5p UAAGAAGCAAAUAGCACAUG 14.17 5.83 –1.28 tae-miR9673-5p miR9676 miR9676-5p UGGAUGUCAUCGUGGCCGUACA 11.42 7.09 –0.69 tae-miR9676-5p miR9677 miR9677-5p UUCCACUCUACCAACAGCCACG 1.34 0.63 –1.08 ata-miR9677-5p miR9774 miR9774 CAAGAUAUUGGGUAUUUCUGUC 3.26 1.65 –0.98 tae-miR9774 miR9775 miR9775 UGUGCGCAAUAAGAUUUUGCUA 3.86 1.65 –1.22 tae-miR9775 miR9776 miR9776-5p AGCUUGGACGAGGAUGUGCAA 18.25 34.25 0.91 ata-miR9776-5p miR9863 miR9863b-5p UGUUAUGAUCUGCUUCUCAUC 1.85 0.63 –1.56 ata-miR9863b-5p gc-m0082-3p gc-m0082-3p UGUGAAAACCAGAUCGGACGG 0.89 0.55 –0.69 gc-m0088-3p gc-m0088-3p CGAGAGACUAGUGUAUAAGAA 1.85 2.91 0.65 gc-m0094-3p gc-m0094-3p GUCGAAUGAUGAUCCCAAGUGCA 1.93 0.87 –1.15 gc-m0098-3p gc-m0098-3p ACGUCGAUGAUAGAGAGAGGG 0.67 1.18 0.82 gc-m0187-5p gc-m0187-5p ACAAGGAGGAGGGAGCUUGUG 0.07 0.79 3.41 gc-m0192-5p gc-m0192-5p ACAGAUCAUGAUGUUUGGUAG 2.67 1.50 –0.84 gc-m0298-3p gc-m0298-3p CCAGAUUCAUUGGUACCUCGG 0.59 0.39 –0.59 gc-m0924-5p gc-m0924-5p GCAUCUUGGCUGCGUCGGUGG 0.67 1.42 1.09 gc-m1117-5p gc-m1117-5p CGAGUUGUUGAAAUCCGGCAG 3.04 5.20 0.77 gc-m1136-3p gc-m1136-3p UGGAUAGUUUGAGGUUUUAUUU 5.56 8.90 0.68 gc-m1443-3p gc-m1443-3p AUACCAUCGAAUAGAGCCUAG 0.67 1.34 1.00 gc-m1684-5p gc-m1684-5p UAUUGCAACGGCAGAGAGGAG 6.45 9.92 0.62 gc-m1730-3p gc-m1730-3p GAUUGACGGAUAACAUGUGGC 2.00 5.20 1.38 gc-m1737-5p gc-m1737-5p UGAGUUGAGGAUGUUGCCGCU 0.89 0.55 –0.69 gc-m2007-5p gc-m2007-5p UUAGGAACGGGGCAGCAGACU 1.63 5.43 1.74 gc-m2240-5p gc-m2240-5p AAGGAGGAGGAAGGAGGAAGA 3.12 5.35 0.78 gc-m2445-5p gc-m2445-5p CUGAAAGAGGGACGCCAUUAG 0.74 1.42 0.93 gc-m2668-5p gc-m2668-5p CGGGAGUGGCACGAAUAAGUC 12.91 19.53 0.60 gc-m2726-3p gc-m2726-3p UCUAGUUGGCCUAUGCAGUGA 2.82 6.30 1.16 gc-m3001-3p gc-m3001-3p CUGCCAAUCGAGGAGUUUGG 0.59 0.31 –0.91 gc-m3192-3p gc-m3192-3p UGGUUGGCGUGAGAAGCAGUG 0.67 0.31 –1.08 gc-m3263-5p gc-m3263-5p CCGCGGUGAGGAAUCAGAGGA 2.37 4.02 0.76 gc-m3551-5p gc-m3551-5p CUUGAGCAGACGUAGUAGCAA 20.70 36.70 0.83 gc-m3739-5p gc-m3739-5p AAGAACUCAUCACAUCUAGGC 2.00 3.94 0.98 gc-m3828-3p gc-m3828-3p UCUUCAGUGGUAGGCAGGCGGC 0.30 0.63 1.09 gc-m3845-5p gc-m3845-5p UUGGAAUUUGGAACUGGCUAG 1.93 4.88 1.34 gc-m4070-3p gc-m4070-3p CCACAAUGGUCAGACUCUUAG 2.97 5.12 0.79 gc-m4082-3p gc-m4082-3p UGCAUCCAGUAGUUUUGACCA 0.96 1.65 0.78 gc-m4289-5p gc-m4289-5p AUCGAUACAGAUGAGUUUGAA 0.74 1.18 0.67 gc-m4300-5p gc-m4300-5p UUUCUUUGAAUUUUGGAAGGC 0.52 0.87 0.74 gc-m4309-5p gc-m4309-5p CGGUGGAAGUUGCUCGGACUGA 3.34 5.35 0.68 miRNA family Name Sequences (5'–3') TPMa Log2 FCb Typical homology in miRBase CS CS-3C miR1120 miR1120a ACAUUCUUAUAUUAUGAGACGGAG 0.67 0.16 –2.08 tae-miR1120a miR1133 miR1133 CAUAUACUCCCUCCGUCCGAAA 0.82 1.50 0.87 tae-miR1133 miR1139 miR1139 GAGUAACAUACACUAGUAACA 0.89 1.57 0.82 bdi-miR1139 miR1508 miR1508c UAGAAAGGGAAAUAGCAGUUG 1.19 2.05 0.79 gma-miR1508c miR156 miR156 UUUGACAGAAGAUAGAGAGCAC 0.96 1.89 0.97 bcy-miR156 miR156s UGACAGAAGAGAGUGAGCACU 0.45 1.26 1.50 gma-miR156s miR156j UGACAGAAGAGGGUGAGCAC 0.37 1.26 1.76 mtr-miR156j miR159 miR159b-3p UUUGGAUUGAAGGGAGCUCUU 2.15 4.88 1.18 aly-miR159b-3p miR159a-3p CUUGGAUUGAAGGGAGCUCU 1.56 3.70 1.25 bdi-miR159a-3p miR159a UUUGGAUUGAAGGGAGCUCUG 147.24 283.80 0.95 tae-miR159a miR160 miR160c-3p GCGUGCAAGGAGCCAAGCAUG 0.89 2.13 1.26 ata-miR160c-3p miR160 UGCCUGGCUCCCUGUAUGCCA 3.04 0.63 –2.27 tae-miR160 miR160a-3p GCGUGCGAGGAGCCAAGCAUG 0.22 1.18 2.41 bdi-miR160a-3p miR164 miR164a-5p UGGAGAAGCAGGGCACGUGCU 3.86 1.97 –0.97 ata-miR164a-5p miR166 miR166d-5p GGAAUGUUGUCUGGCUCGGGG 5.19 10.71 1.04 ata-miR166d-5p miR166e-5p GGAAUGUUGUCUGGUUGGAGA 0.37 2.28 2.62 ata-miR166e-5p miR166e-3p CUCGGACCAGGCUUCAUUCCC 0.74 0.31 –1.24 bdi-miR166e-3p miR166i UUGGACCAGGCUUCAUUCCCC 0.96 1.50 0.63 mes-miR166i miR166e-3p UCGAACCAGGCUUCAUUCCCC 0.37 0.87 1.22 osa-miR166e-3p miR167 miR167b-3p AGGUCAUGCUGGAGUUUCAUC 0.52 2.36 2.19 ata-miR167b-3p miR167d-5p UGAAGCUGCCAGCAUGAUCUGA 7317.95 4706.54 –0.64 ata-miR167d-5p miR167e-5p UGAAGCUGCCAGCAUGAUCUA 2585.59 1681.22 –0.62 ata-miR167e-5p miR167f-3p CAGAUCAUGCUGCAGCUUCAU 3.49 1.65 –1.08 ata-miR167f-3p miR167a UGAAGCUGCCAGCAUGAUCUAA 6.38 3.78 –0.76 tae-miR167a miR167c UGAAGCUGCCAGCAUGAUCUU 38.94 23.70 –0.72 cpa-miR167c miR167j UGAAGCUGCCAGCAUGAUCUUA 2.15 0.94 –1.19 mdm-miR167j miR167c UGAAGCUGCCAGCAUGAUCUGG 2.15 0.71 –1.60 nta-miR167c miR167b-5p UAAAGCUGCCAGCAUGAUCUGG 1.11 0.71 –0.65 sly-miR167b-5p miR167c UGAAGCUGCCAGCAUGAUCUC 2.74 1.34 –1.04 vvi-miR167c miR168 miR168-3p CCCGCCUUGCACCAAGUGAAU 1.26 5.83 2.21 ata-miR168-3p miR169 miR169d-5p CAGCCAAGGAUGACUUGCCGG 3.34 1.50 –1.16 ata-miR169d-5p miR169g-3p GGCGAGUUGUUCUUGGCUACA 0.89 0.47 –0.91 ata-miR169g-3p miR171 miR171c-5p CGGUAUUGGUGCGGUUCAAUC 0.89 4.09 2.20 ata-miR171c-5p miR171d-5p UGUUGGCUCGACUCACUCAGA 0.74 2.52 1.76 ata-miR171d-5p miR171a UUGAGCCGUGCCAAUAUCACU 0.67 1.18 0.82 smo-miR171a miR172 miR172b-3p AGAAUCUUGAUGAUGCUGCAU 32.12 19.69 –0.71 ata-miR172b-3p miR172b-5p GCAGCACCACCAAGAUUCACA 0.01 0.94 6.56 ata-miR172b-5p miR172b GGGAAUCUUGAUGAUGCUGCA 0.15 0.63 2.09 cpa-miR172b miR1878 miR1878-3p AUUUGUAGUGUUCAGAUUGAGUUU 12.02 7.95 –0.60 bdi-miR1878-3p miR2118 miR2118b-3p GGGAAUGGGAACAUGGAGGAA 0.30 0.55 0.89 ata-miR2118b-3p miR2118b-5p UUCCCGAUGCCUCCCAUUCCUA 1.34 5.43 2.02 ata-miR2118b-5p miR2118d-3p UUCCUGAUGCCUCCCAUGCCUA 0.37 0.63 0.76 ata-miR2118d-3p miR2118b UUCCUGAUGCCUCCCAUUCCUA 0.59 2.83 2.26 bdi-miR2118b miR2118q UUCCCGAUGCCUCCUAUUCCUA 0.52 2.52 2.28 osa-miR2118q miR2118e UUCCUGAUGUCUCCCAUUCCUA 0.82 1.57 0.95 zma-miR2118e miR2120 miR2120 AAAGAUCUUUAGUCCCGGUUGUUC 2.30 0.87 –1.41 osa-miR2120 miR2275 miR2275b UUCAGUUUCUUCUAAUAUCUCA 4.23 8.66 1.03 bdi-miR2275b miR2275-3p UUUGGUUUCCUCCAAUAUCUCG 1.93 0.79 –1.29 tae-miR2275-3p miR319 miR319-3p ACUGGAUGACGCGGGAGCUAA 125.58 209.15 0.74 ata-miR319-3p miR319b-3p UUGGACUGAAGGGUGCUCCCU 3.34 0.55 –2.60 bdi-miR319b-3p miR3446 miR3446-5p CUCGGAAGCUAGACGUGUGGCAGG 13.35 27.80 1.06 aly-miR3446-5p miR3711 miR3711 UGGCGCUAGAAGGAGGGCCU 1.11 2.76 1.31 pab-miR3711 miR393 miR393-5p UUCCAAAGGGAUCGCAUUGAU 164.82 95.36 –0.79 ata-miR393-5p miR393b-3p UCAGUGCAAUCCCUUUGGAAU 5.79 3.23 –0.84 bdi-miR393b-3p miR3947 miR3947-5p UUAUUUCAGUAGACGACGUCACA 0.52 0.24 –1.14 csi-miR3947-5p miR395 miR395b UGAAGUGUUUGGGGGAACUC 0.67 0.08 –3.08 tae-miR395b miR396 miR396b-5p UCCACAGGCUUUCUUGAACUG 126.18 71.66 –0.82 ata-miR396b-5p miR396c-5p UUCCACAGCUUUCUUGAACUU 23.29 14.25 –0.71 ata-miR396c-5p miR396d-3p GUUCAAGAAAGCCCAUGGAAA 0.07 0.79 3.41 ata-miR396d-3p miR396e-3p GUUCAAUAAAGCUGUGGGAAA 1.11 2.60 1.22 ata-miR396e-3p miR396-5p AACUGUGAACUCGCGGGGAUG 5.49 2.83 –0.95 tae-miR396-5p miR397 miR397b-5p AUUGAGUGCAGCGUUGAUGAA 7.42 4.02 –0.89 bdi-miR397b-5p miR397-5p AUUGAGUGCAGCGUUGAUGAC 0.59 0.31 –0.91 stu-miR397-5p miR4384 miR4384 AAUCAGACACUGCAUUCAAAGACG 2.74 1.50 –0.88 gma-miR4384 miR444 miR444b UGCAGUUGCUGCCUCAAGCUU 6.45 2.05 –1.66 bdi-miR444b miR444b UGCAGUUGCUGUCUCAAGCUU 2.89 0.55 –2.39 hvu-miR444b miR5049 miR5049-3p AAUAUGGAUCGGAGGGAGUAC 3.78 7.40 0.97 tae-miR5049-3p miR5062 miR5062b-3p UGAACCUUAGGGAAAAGCCGCAU 1107.39 674.22 –0.72 ata-miR5062b-3p miR5062b-5p GCGGAUUUUUCACCAAGAUUCAAG 0.74 3.62 2.29 ata-miR5062b-5p miR5062-5p UGAACCUUAGGGAACAGCCGCAU 709.13 384.51 –0.88 tae-miR5062-5p miR5071 miR5071 UCAAGCAUCAUAUCGUGGACA 221.20 135.13 –0.71 osa-miR5071 miR5084 miR5084-3p ACCAUACGGUACUGCAGAGGAUC 35.90 22.52 –0.67 ata-miR5084-3p miR5084-5p AUCCUCUACAGUACUGUACGGUGC 12.68 8.19 –0.63 ata-miR5084-5p miR5169 miR5169b UUUGACCAAGUUUGUAGAACA 7.05 4.65 –0.60 bdi-miR5169b miR5175 miR5175-5p UUCCAAAUUACUCGUCGUGGU 25.15 12.13 –1.05 tae-miR5175-5p miR528 miR528-5p UGGAAGGGGCAUGCAGAGGAG 17.51 9.84 –0.83 ata-miR528-5p miR5387 miR5387b CGUGGCUCUGACCGGUGCUAAAGG 0.30 0.47 0.67 sbi-miR5387b miR6179 miR6179 AACCAGUCGAGGCCAGGGGGUU 0.52 0.94 0.86 hvu-miR6179 miR7714 miR7714-3p CUAAUAUGUAUCGAAGGGAGUAGC 1.34 2.05 0.62 bdi-miR7714-3p miR7757 miR7757-5p.2 CUUCCAUAUCAAAUCAUCUCU 0.22 0.55 1.31 bdi-miR7757-5p.2 miR8028 miR8028-5p UCCUUAUGCUACAAUUGUGAACAA 0.52 0.24 –1.14 stu-miR8028-5p miR8175 miR8175 GAUCCCCGGCAACGGCGCCA 10.53 28.11 1.42 ath-miR8175 miR827 miR827 UUAGAUGACCAUCAGCAAACA 12.76 19.69 0.63 ssp-miR827 miR837 miR837-3p AAACGAACAAAAAACUGAUGG 0.59 0.16 –1.91 ath-miR837-3p miR845 miR845 UGCUCUGAUACCAAUUGUUGG 0.52 0.79 0.60 bdi-miR845 miR894 miR894 CGUUUCACGUCGGGUUCACC 1.41 2.99 1.09 ppt-miR894 miR9653 miR9653b UGGCCAAGGUCUCUUGAGGCU 0.59 0.31 –0.91 tae-miR9653b miR9654 miR9654a-3p UUCUGAAAGGCUUGAAGCGAAU 1.48 0.79 –0.91 tae-miR9654a-3p miR9654b-3p UUCCGAAAGGCUUGAAGCGAAU 3.12 1.57 –0.98 tae-miR9654b-3p miR9655 miR9655-3p CAAGGGAAGGAAGUAGCCAAC 0.30 0.63 1.09 tae-miR9655-3p miR9656 miR9656-3p CUUCGAGACUCUGAACAGCGG 0.89 1.65 0.89 tae-miR9656-3p miR9657 miR9657b-3p CGUGCUUCCUCGUCGAACGGU 0.01 5.83 9.19 tae-miR9657b-3p miR9661 miR9661-5p UGAAGUAGAGCAGGGACCUCA 61.94 37.88 –0.71 tae-miR9661-5p miR9664 miR9664-3p UUGCAGUCCUCGAUGUCGUAG 9.35 5.51 –0.76 tae-miR9664-3p miR9669 miR9669-5p UACUGUGGGCACUUAUUUGAC 2.08 5.43 1.39 tae-miR9669-5p miR9670 miR9670-3p AGGUGGAAUACUUGAAGAAGA 19.06 96.07 2.33 tae-miR9670-3p miR9672 miR9672-5p CUUAAUGACAGUCGUGGUGUC 6.53 1.42 –2.20 ata-miR9672-5p miR9673 miR9673-5p UAAGAAGCAAAUAGCACAUG 14.17 5.83 –1.28 tae-miR9673-5p miR9676 miR9676-5p UGGAUGUCAUCGUGGCCGUACA 11.42 7.09 –0.69 tae-miR9676-5p miR9677 miR9677-5p UUCCACUCUACCAACAGCCACG 1.34 0.63 –1.08 ata-miR9677-5p miR9774 miR9774 CAAGAUAUUGGGUAUUUCUGUC 3.26 1.65 –0.98 tae-miR9774 miR9775 miR9775 UGUGCGCAAUAAGAUUUUGCUA 3.86 1.65 –1.22 tae-miR9775 miR9776 miR9776-5p AGCUUGGACGAGGAUGUGCAA 18.25 34.25 0.91 ata-miR9776-5p miR9863 miR9863b-5p UGUUAUGAUCUGCUUCUCAUC 1.85 0.63 –1.56 ata-miR9863b-5p gc-m0082-3p gc-m0082-3p UGUGAAAACCAGAUCGGACGG 0.89 0.55 –0.69 gc-m0088-3p gc-m0088-3p CGAGAGACUAGUGUAUAAGAA 1.85 2.91 0.65 gc-m0094-3p gc-m0094-3p GUCGAAUGAUGAUCCCAAGUGCA 1.93 0.87 –1.15 gc-m0098-3p gc-m0098-3p ACGUCGAUGAUAGAGAGAGGG 0.67 1.18 0.82 gc-m0187-5p gc-m0187-5p ACAAGGAGGAGGGAGCUUGUG 0.07 0.79 3.41 gc-m0192-5p gc-m0192-5p ACAGAUCAUGAUGUUUGGUAG 2.67 1.50 –0.84 gc-m0298-3p gc-m0298-3p CCAGAUUCAUUGGUACCUCGG 0.59 0.39 –0.59 gc-m0924-5p gc-m0924-5p GCAUCUUGGCUGCGUCGGUGG 0.67 1.42 1.09 gc-m1117-5p gc-m1117-5p CGAGUUGUUGAAAUCCGGCAG 3.04 5.20 0.77 gc-m1136-3p gc-m1136-3p UGGAUAGUUUGAGGUUUUAUUU 5.56 8.90 0.68 gc-m1443-3p gc-m1443-3p AUACCAUCGAAUAGAGCCUAG 0.67 1.34 1.00 gc-m1684-5p gc-m1684-5p UAUUGCAACGGCAGAGAGGAG 6.45 9.92 0.62 gc-m1730-3p gc-m1730-3p GAUUGACGGAUAACAUGUGGC 2.00 5.20 1.38 gc-m1737-5p gc-m1737-5p UGAGUUGAGGAUGUUGCCGCU 0.89 0.55 –0.69 gc-m2007-5p gc-m2007-5p UUAGGAACGGGGCAGCAGACU 1.63 5.43 1.74 gc-m2240-5p gc-m2240-5p AAGGAGGAGGAAGGAGGAAGA 3.12 5.35 0.78 gc-m2445-5p gc-m2445-5p CUGAAAGAGGGACGCCAUUAG 0.74 1.42 0.93 gc-m2668-5p gc-m2668-5p CGGGAGUGGCACGAAUAAGUC 12.91 19.53 0.60 gc-m2726-3p gc-m2726-3p UCUAGUUGGCCUAUGCAGUGA 2.82 6.30 1.16 gc-m3001-3p gc-m3001-3p CUGCCAAUCGAGGAGUUUGG 0.59 0.31 –0.91 gc-m3192-3p gc-m3192-3p UGGUUGGCGUGAGAAGCAGUG 0.67 0.31 –1.08 gc-m3263-5p gc-m3263-5p CCGCGGUGAGGAAUCAGAGGA 2.37 4.02 0.76 gc-m3551-5p gc-m3551-5p CUUGAGCAGACGUAGUAGCAA 20.70 36.70 0.83 gc-m3739-5p gc-m3739-5p AAGAACUCAUCACAUCUAGGC 2.00 3.94 0.98 gc-m3828-3p gc-m3828-3p UCUUCAGUGGUAGGCAGGCGGC 0.30 0.63 1.09 gc-m3845-5p gc-m3845-5p UUGGAAUUUGGAACUGGCUAG 1.93 4.88 1.34 gc-m4070-3p gc-m4070-3p CCACAAUGGUCAGACUCUUAG 2.97 5.12 0.79 gc-m4082-3p gc-m4082-3p UGCAUCCAGUAGUUUUGACCA 0.96 1.65 0.78 gc-m4289-5p gc-m4289-5p AUCGAUACAGAUGAGUUUGAA 0.74 1.18 0.67 gc-m4300-5p gc-m4300-5p UUUCUUUGAAUUUUGGAAGGC 0.52 0.87 0.74 gc-m4309-5p gc-m4309-5p CGGUGGAAGUUGCUCGGACUGA 3.34 5.35 0.68 a Figures in columns headed ‘CS’ and ‘CS-3C’ are normalized read counts. Normalized expression (TPM)=count of miRNA/total count of clean sRNAs×106. The signals were considered as differentially expressed with 1.5-fold change values. b FC, fold change (CS-3C/CS) View Large Table 1. The differentially expressed miRNAs in gametocidal action miRNA family Name Sequences (5'–3') TPMa Log2 FCb Typical homology in miRBase CS CS-3C miR1120 miR1120a ACAUUCUUAUAUUAUGAGACGGAG 0.67 0.16 –2.08 tae-miR1120a miR1133 miR1133 CAUAUACUCCCUCCGUCCGAAA 0.82 1.50 0.87 tae-miR1133 miR1139 miR1139 GAGUAACAUACACUAGUAACA 0.89 1.57 0.82 bdi-miR1139 miR1508 miR1508c UAGAAAGGGAAAUAGCAGUUG 1.19 2.05 0.79 gma-miR1508c miR156 miR156 UUUGACAGAAGAUAGAGAGCAC 0.96 1.89 0.97 bcy-miR156 miR156s UGACAGAAGAGAGUGAGCACU 0.45 1.26 1.50 gma-miR156s miR156j UGACAGAAGAGGGUGAGCAC 0.37 1.26 1.76 mtr-miR156j miR159 miR159b-3p UUUGGAUUGAAGGGAGCUCUU 2.15 4.88 1.18 aly-miR159b-3p miR159a-3p CUUGGAUUGAAGGGAGCUCU 1.56 3.70 1.25 bdi-miR159a-3p miR159a UUUGGAUUGAAGGGAGCUCUG 147.24 283.80 0.95 tae-miR159a miR160 miR160c-3p GCGUGCAAGGAGCCAAGCAUG 0.89 2.13 1.26 ata-miR160c-3p miR160 UGCCUGGCUCCCUGUAUGCCA 3.04 0.63 –2.27 tae-miR160 miR160a-3p GCGUGCGAGGAGCCAAGCAUG 0.22 1.18 2.41 bdi-miR160a-3p miR164 miR164a-5p UGGAGAAGCAGGGCACGUGCU 3.86 1.97 –0.97 ata-miR164a-5p miR166 miR166d-5p GGAAUGUUGUCUGGCUCGGGG 5.19 10.71 1.04 ata-miR166d-5p miR166e-5p GGAAUGUUGUCUGGUUGGAGA 0.37 2.28 2.62 ata-miR166e-5p miR166e-3p CUCGGACCAGGCUUCAUUCCC 0.74 0.31 –1.24 bdi-miR166e-3p miR166i UUGGACCAGGCUUCAUUCCCC 0.96 1.50 0.63 mes-miR166i miR166e-3p UCGAACCAGGCUUCAUUCCCC 0.37 0.87 1.22 osa-miR166e-3p miR167 miR167b-3p AGGUCAUGCUGGAGUUUCAUC 0.52 2.36 2.19 ata-miR167b-3p miR167d-5p UGAAGCUGCCAGCAUGAUCUGA 7317.95 4706.54 –0.64 ata-miR167d-5p miR167e-5p UGAAGCUGCCAGCAUGAUCUA 2585.59 1681.22 –0.62 ata-miR167e-5p miR167f-3p CAGAUCAUGCUGCAGCUUCAU 3.49 1.65 –1.08 ata-miR167f-3p miR167a UGAAGCUGCCAGCAUGAUCUAA 6.38 3.78 –0.76 tae-miR167a miR167c UGAAGCUGCCAGCAUGAUCUU 38.94 23.70 –0.72 cpa-miR167c miR167j UGAAGCUGCCAGCAUGAUCUUA 2.15 0.94 –1.19 mdm-miR167j miR167c UGAAGCUGCCAGCAUGAUCUGG 2.15 0.71 –1.60 nta-miR167c miR167b-5p UAAAGCUGCCAGCAUGAUCUGG 1.11 0.71 –0.65 sly-miR167b-5p miR167c UGAAGCUGCCAGCAUGAUCUC 2.74 1.34 –1.04 vvi-miR167c miR168 miR168-3p CCCGCCUUGCACCAAGUGAAU 1.26 5.83 2.21 ata-miR168-3p miR169 miR169d-5p CAGCCAAGGAUGACUUGCCGG 3.34 1.50 –1.16 ata-miR169d-5p miR169g-3p GGCGAGUUGUUCUUGGCUACA 0.89 0.47 –0.91 ata-miR169g-3p miR171 miR171c-5p CGGUAUUGGUGCGGUUCAAUC 0.89 4.09 2.20 ata-miR171c-5p miR171d-5p UGUUGGCUCGACUCACUCAGA 0.74 2.52 1.76 ata-miR171d-5p miR171a UUGAGCCGUGCCAAUAUCACU 0.67 1.18 0.82 smo-miR171a miR172 miR172b-3p AGAAUCUUGAUGAUGCUGCAU 32.12 19.69 –0.71 ata-miR172b-3p miR172b-5p GCAGCACCACCAAGAUUCACA 0.01 0.94 6.56 ata-miR172b-5p miR172b GGGAAUCUUGAUGAUGCUGCA 0.15 0.63 2.09 cpa-miR172b miR1878 miR1878-3p AUUUGUAGUGUUCAGAUUGAGUUU 12.02 7.95 –0.60 bdi-miR1878-3p miR2118 miR2118b-3p GGGAAUGGGAACAUGGAGGAA 0.30 0.55 0.89 ata-miR2118b-3p miR2118b-5p UUCCCGAUGCCUCCCAUUCCUA 1.34 5.43 2.02 ata-miR2118b-5p miR2118d-3p UUCCUGAUGCCUCCCAUGCCUA 0.37 0.63 0.76 ata-miR2118d-3p miR2118b UUCCUGAUGCCUCCCAUUCCUA 0.59 2.83 2.26 bdi-miR2118b miR2118q UUCCCGAUGCCUCCUAUUCCUA 0.52 2.52 2.28 osa-miR2118q miR2118e UUCCUGAUGUCUCCCAUUCCUA 0.82 1.57 0.95 zma-miR2118e miR2120 miR2120 AAAGAUCUUUAGUCCCGGUUGUUC 2.30 0.87 –1.41 osa-miR2120 miR2275 miR2275b UUCAGUUUCUUCUAAUAUCUCA 4.23 8.66 1.03 bdi-miR2275b miR2275-3p UUUGGUUUCCUCCAAUAUCUCG 1.93 0.79 –1.29 tae-miR2275-3p miR319 miR319-3p ACUGGAUGACGCGGGAGCUAA 125.58 209.15 0.74 ata-miR319-3p miR319b-3p UUGGACUGAAGGGUGCUCCCU 3.34 0.55 –2.60 bdi-miR319b-3p miR3446 miR3446-5p CUCGGAAGCUAGACGUGUGGCAGG 13.35 27.80 1.06 aly-miR3446-5p miR3711 miR3711 UGGCGCUAGAAGGAGGGCCU 1.11 2.76 1.31 pab-miR3711 miR393 miR393-5p UUCCAAAGGGAUCGCAUUGAU 164.82 95.36 –0.79 ata-miR393-5p miR393b-3p UCAGUGCAAUCCCUUUGGAAU 5.79 3.23 –0.84 bdi-miR393b-3p miR3947 miR3947-5p UUAUUUCAGUAGACGACGUCACA 0.52 0.24 –1.14 csi-miR3947-5p miR395 miR395b UGAAGUGUUUGGGGGAACUC 0.67 0.08 –3.08 tae-miR395b miR396 miR396b-5p UCCACAGGCUUUCUUGAACUG 126.18 71.66 –0.82 ata-miR396b-5p miR396c-5p UUCCACAGCUUUCUUGAACUU 23.29 14.25 –0.71 ata-miR396c-5p miR396d-3p GUUCAAGAAAGCCCAUGGAAA 0.07 0.79 3.41 ata-miR396d-3p miR396e-3p GUUCAAUAAAGCUGUGGGAAA 1.11 2.60 1.22 ata-miR396e-3p miR396-5p AACUGUGAACUCGCGGGGAUG 5.49 2.83 –0.95 tae-miR396-5p miR397 miR397b-5p AUUGAGUGCAGCGUUGAUGAA 7.42 4.02 –0.89 bdi-miR397b-5p miR397-5p AUUGAGUGCAGCGUUGAUGAC 0.59 0.31 –0.91 stu-miR397-5p miR4384 miR4384 AAUCAGACACUGCAUUCAAAGACG 2.74 1.50 –0.88 gma-miR4384 miR444 miR444b UGCAGUUGCUGCCUCAAGCUU 6.45 2.05 –1.66 bdi-miR444b miR444b UGCAGUUGCUGUCUCAAGCUU 2.89 0.55 –2.39 hvu-miR444b miR5049 miR5049-3p AAUAUGGAUCGGAGGGAGUAC 3.78 7.40 0.97 tae-miR5049-3p miR5062 miR5062b-3p UGAACCUUAGGGAAAAGCCGCAU 1107.39 674.22 –0.72 ata-miR5062b-3p miR5062b-5p GCGGAUUUUUCACCAAGAUUCAAG 0.74 3.62 2.29 ata-miR5062b-5p miR5062-5p UGAACCUUAGGGAACAGCCGCAU 709.13 384.51 –0.88 tae-miR5062-5p miR5071 miR5071 UCAAGCAUCAUAUCGUGGACA 221.20 135.13 –0.71 osa-miR5071 miR5084 miR5084-3p ACCAUACGGUACUGCAGAGGAUC 35.90 22.52 –0.67 ata-miR5084-3p miR5084-5p AUCCUCUACAGUACUGUACGGUGC 12.68 8.19 –0.63 ata-miR5084-5p miR5169 miR5169b UUUGACCAAGUUUGUAGAACA 7.05 4.65 –0.60 bdi-miR5169b miR5175 miR5175-5p UUCCAAAUUACUCGUCGUGGU 25.15 12.13 –1.05 tae-miR5175-5p miR528 miR528-5p UGGAAGGGGCAUGCAGAGGAG 17.51 9.84 –0.83 ata-miR528-5p miR5387 miR5387b CGUGGCUCUGACCGGUGCUAAAGG 0.30 0.47 0.67 sbi-miR5387b miR6179 miR6179 AACCAGUCGAGGCCAGGGGGUU 0.52 0.94 0.86 hvu-miR6179 miR7714 miR7714-3p CUAAUAUGUAUCGAAGGGAGUAGC 1.34 2.05 0.62 bdi-miR7714-3p miR7757 miR7757-5p.2 CUUCCAUAUCAAAUCAUCUCU 0.22 0.55 1.31 bdi-miR7757-5p.2 miR8028 miR8028-5p UCCUUAUGCUACAAUUGUGAACAA 0.52 0.24 –1.14 stu-miR8028-5p miR8175 miR8175 GAUCCCCGGCAACGGCGCCA 10.53 28.11 1.42 ath-miR8175 miR827 miR827 UUAGAUGACCAUCAGCAAACA 12.76 19.69 0.63 ssp-miR827 miR837 miR837-3p AAACGAACAAAAAACUGAUGG 0.59 0.16 –1.91 ath-miR837-3p miR845 miR845 UGCUCUGAUACCAAUUGUUGG 0.52 0.79 0.60 bdi-miR845 miR894 miR894 CGUUUCACGUCGGGUUCACC 1.41 2.99 1.09 ppt-miR894 miR9653 miR9653b UGGCCAAGGUCUCUUGAGGCU 0.59 0.31 –0.91 tae-miR9653b miR9654 miR9654a-3p UUCUGAAAGGCUUGAAGCGAAU 1.48 0.79 –0.91 tae-miR9654a-3p miR9654b-3p UUCCGAAAGGCUUGAAGCGAAU 3.12 1.57 –0.98 tae-miR9654b-3p miR9655 miR9655-3p CAAGGGAAGGAAGUAGCCAAC 0.30 0.63 1.09 tae-miR9655-3p miR9656 miR9656-3p CUUCGAGACUCUGAACAGCGG 0.89 1.65 0.89 tae-miR9656-3p miR9657 miR9657b-3p CGUGCUUCCUCGUCGAACGGU 0.01 5.83 9.19 tae-miR9657b-3p miR9661 miR9661-5p UGAAGUAGAGCAGGGACCUCA 61.94 37.88 –0.71 tae-miR9661-5p miR9664 miR9664-3p UUGCAGUCCUCGAUGUCGUAG 9.35 5.51 –0.76 tae-miR9664-3p miR9669 miR9669-5p UACUGUGGGCACUUAUUUGAC 2.08 5.43 1.39 tae-miR9669-5p miR9670 miR9670-3p AGGUGGAAUACUUGAAGAAGA 19.06 96.07 2.33 tae-miR9670-3p miR9672 miR9672-5p CUUAAUGACAGUCGUGGUGUC 6.53 1.42 –2.20 ata-miR9672-5p miR9673 miR9673-5p UAAGAAGCAAAUAGCACAUG 14.17 5.83 –1.28 tae-miR9673-5p miR9676 miR9676-5p UGGAUGUCAUCGUGGCCGUACA 11.42 7.09 –0.69 tae-miR9676-5p miR9677 miR9677-5p UUCCACUCUACCAACAGCCACG 1.34 0.63 –1.08 ata-miR9677-5p miR9774 miR9774 CAAGAUAUUGGGUAUUUCUGUC 3.26 1.65 –0.98 tae-miR9774 miR9775 miR9775 UGUGCGCAAUAAGAUUUUGCUA 3.86 1.65 –1.22 tae-miR9775 miR9776 miR9776-5p AGCUUGGACGAGGAUGUGCAA 18.25 34.25 0.91 ata-miR9776-5p miR9863 miR9863b-5p UGUUAUGAUCUGCUUCUCAUC 1.85 0.63 –1.56 ata-miR9863b-5p gc-m0082-3p gc-m0082-3p UGUGAAAACCAGAUCGGACGG 0.89 0.55 –0.69 gc-m0088-3p gc-m0088-3p CGAGAGACUAGUGUAUAAGAA 1.85 2.91 0.65 gc-m0094-3p gc-m0094-3p GUCGAAUGAUGAUCCCAAGUGCA 1.93 0.87 –1.15 gc-m0098-3p gc-m0098-3p ACGUCGAUGAUAGAGAGAGGG 0.67 1.18 0.82 gc-m0187-5p gc-m0187-5p ACAAGGAGGAGGGAGCUUGUG 0.07 0.79 3.41 gc-m0192-5p gc-m0192-5p ACAGAUCAUGAUGUUUGGUAG 2.67 1.50 –0.84 gc-m0298-3p gc-m0298-3p CCAGAUUCAUUGGUACCUCGG 0.59 0.39 –0.59 gc-m0924-5p gc-m0924-5p GCAUCUUGGCUGCGUCGGUGG 0.67 1.42 1.09 gc-m1117-5p gc-m1117-5p CGAGUUGUUGAAAUCCGGCAG 3.04 5.20 0.77 gc-m1136-3p gc-m1136-3p UGGAUAGUUUGAGGUUUUAUUU 5.56 8.90 0.68 gc-m1443-3p gc-m1443-3p AUACCAUCGAAUAGAGCCUAG 0.67 1.34 1.00 gc-m1684-5p gc-m1684-5p UAUUGCAACGGCAGAGAGGAG 6.45 9.92 0.62 gc-m1730-3p gc-m1730-3p GAUUGACGGAUAACAUGUGGC 2.00 5.20 1.38 gc-m1737-5p gc-m1737-5p UGAGUUGAGGAUGUUGCCGCU 0.89 0.55 –0.69 gc-m2007-5p gc-m2007-5p UUAGGAACGGGGCAGCAGACU 1.63 5.43 1.74 gc-m2240-5p gc-m2240-5p AAGGAGGAGGAAGGAGGAAGA 3.12 5.35 0.78 gc-m2445-5p gc-m2445-5p CUGAAAGAGGGACGCCAUUAG 0.74 1.42 0.93 gc-m2668-5p gc-m2668-5p CGGGAGUGGCACGAAUAAGUC 12.91 19.53 0.60 gc-m2726-3p gc-m2726-3p UCUAGUUGGCCUAUGCAGUGA 2.82 6.30 1.16 gc-m3001-3p gc-m3001-3p CUGCCAAUCGAGGAGUUUGG 0.59 0.31 –0.91 gc-m3192-3p gc-m3192-3p UGGUUGGCGUGAGAAGCAGUG 0.67 0.31 –1.08 gc-m3263-5p gc-m3263-5p CCGCGGUGAGGAAUCAGAGGA 2.37 4.02 0.76 gc-m3551-5p gc-m3551-5p CUUGAGCAGACGUAGUAGCAA 20.70 36.70 0.83 gc-m3739-5p gc-m3739-5p AAGAACUCAUCACAUCUAGGC 2.00 3.94 0.98 gc-m3828-3p gc-m3828-3p UCUUCAGUGGUAGGCAGGCGGC 0.30 0.63 1.09 gc-m3845-5p gc-m3845-5p UUGGAAUUUGGAACUGGCUAG 1.93 4.88 1.34 gc-m4070-3p gc-m4070-3p CCACAAUGGUCAGACUCUUAG 2.97 5.12 0.79 gc-m4082-3p gc-m4082-3p UGCAUCCAGUAGUUUUGACCA 0.96 1.65 0.78 gc-m4289-5p gc-m4289-5p AUCGAUACAGAUGAGUUUGAA 0.74 1.18 0.67 gc-m4300-5p gc-m4300-5p UUUCUUUGAAUUUUGGAAGGC 0.52 0.87 0.74 gc-m4309-5p gc-m4309-5p CGGUGGAAGUUGCUCGGACUGA 3.34 5.35 0.68 miRNA family Name Sequences (5'–3') TPMa Log2 FCb Typical homology in miRBase CS CS-3C miR1120 miR1120a ACAUUCUUAUAUUAUGAGACGGAG 0.67 0.16 –2.08 tae-miR1120a miR1133 miR1133 CAUAUACUCCCUCCGUCCGAAA 0.82 1.50 0.87 tae-miR1133 miR1139 miR1139 GAGUAACAUACACUAGUAACA 0.89 1.57 0.82 bdi-miR1139 miR1508 miR1508c UAGAAAGGGAAAUAGCAGUUG 1.19 2.05 0.79 gma-miR1508c miR156 miR156 UUUGACAGAAGAUAGAGAGCAC 0.96 1.89 0.97 bcy-miR156 miR156s UGACAGAAGAGAGUGAGCACU 0.45 1.26 1.50 gma-miR156s miR156j UGACAGAAGAGGGUGAGCAC 0.37 1.26 1.76 mtr-miR156j miR159 miR159b-3p UUUGGAUUGAAGGGAGCUCUU 2.15 4.88 1.18 aly-miR159b-3p miR159a-3p CUUGGAUUGAAGGGAGCUCU 1.56 3.70 1.25 bdi-miR159a-3p miR159a UUUGGAUUGAAGGGAGCUCUG 147.24 283.80 0.95 tae-miR159a miR160 miR160c-3p GCGUGCAAGGAGCCAAGCAUG 0.89 2.13 1.26 ata-miR160c-3p miR160 UGCCUGGCUCCCUGUAUGCCA 3.04 0.63 –2.27 tae-miR160 miR160a-3p GCGUGCGAGGAGCCAAGCAUG 0.22 1.18 2.41 bdi-miR160a-3p miR164 miR164a-5p UGGAGAAGCAGGGCACGUGCU 3.86 1.97 –0.97 ata-miR164a-5p miR166 miR166d-5p GGAAUGUUGUCUGGCUCGGGG 5.19 10.71 1.04 ata-miR166d-5p miR166e-5p GGAAUGUUGUCUGGUUGGAGA 0.37 2.28 2.62 ata-miR166e-5p miR166e-3p CUCGGACCAGGCUUCAUUCCC 0.74 0.31 –1.24 bdi-miR166e-3p miR166i UUGGACCAGGCUUCAUUCCCC 0.96 1.50 0.63 mes-miR166i miR166e-3p UCGAACCAGGCUUCAUUCCCC 0.37 0.87 1.22 osa-miR166e-3p miR167 miR167b-3p AGGUCAUGCUGGAGUUUCAUC 0.52 2.36 2.19 ata-miR167b-3p miR167d-5p UGAAGCUGCCAGCAUGAUCUGA 7317.95 4706.54 –0.64 ata-miR167d-5p miR167e-5p UGAAGCUGCCAGCAUGAUCUA 2585.59 1681.22 –0.62 ata-miR167e-5p miR167f-3p CAGAUCAUGCUGCAGCUUCAU 3.49 1.65 –1.08 ata-miR167f-3p miR167a UGAAGCUGCCAGCAUGAUCUAA 6.38 3.78 –0.76 tae-miR167a miR167c UGAAGCUGCCAGCAUGAUCUU 38.94 23.70 –0.72 cpa-miR167c miR167j UGAAGCUGCCAGCAUGAUCUUA 2.15 0.94 –1.19 mdm-miR167j miR167c UGAAGCUGCCAGCAUGAUCUGG 2.15 0.71 –1.60 nta-miR167c miR167b-5p UAAAGCUGCCAGCAUGAUCUGG 1.11 0.71 –0.65 sly-miR167b-5p miR167c UGAAGCUGCCAGCAUGAUCUC 2.74 1.34 –1.04 vvi-miR167c miR168 miR168-3p CCCGCCUUGCACCAAGUGAAU 1.26 5.83 2.21 ata-miR168-3p miR169 miR169d-5p CAGCCAAGGAUGACUUGCCGG 3.34 1.50 –1.16 ata-miR169d-5p miR169g-3p GGCGAGUUGUUCUUGGCUACA 0.89 0.47 –0.91 ata-miR169g-3p miR171 miR171c-5p CGGUAUUGGUGCGGUUCAAUC 0.89 4.09 2.20 ata-miR171c-5p miR171d-5p UGUUGGCUCGACUCACUCAGA 0.74 2.52 1.76 ata-miR171d-5p miR171a UUGAGCCGUGCCAAUAUCACU 0.67 1.18 0.82 smo-miR171a miR172 miR172b-3p AGAAUCUUGAUGAUGCUGCAU 32.12 19.69 –0.71 ata-miR172b-3p miR172b-5p GCAGCACCACCAAGAUUCACA 0.01 0.94 6.56 ata-miR172b-5p miR172b GGGAAUCUUGAUGAUGCUGCA 0.15 0.63 2.09 cpa-miR172b miR1878 miR1878-3p AUUUGUAGUGUUCAGAUUGAGUUU 12.02 7.95 –0.60 bdi-miR1878-3p miR2118 miR2118b-3p GGGAAUGGGAACAUGGAGGAA 0.30 0.55 0.89 ata-miR2118b-3p miR2118b-5p UUCCCGAUGCCUCCCAUUCCUA 1.34 5.43 2.02 ata-miR2118b-5p miR2118d-3p UUCCUGAUGCCUCCCAUGCCUA 0.37 0.63 0.76 ata-miR2118d-3p miR2118b UUCCUGAUGCCUCCCAUUCCUA 0.59 2.83 2.26 bdi-miR2118b miR2118q UUCCCGAUGCCUCCUAUUCCUA 0.52 2.52 2.28 osa-miR2118q miR2118e UUCCUGAUGUCUCCCAUUCCUA 0.82 1.57 0.95 zma-miR2118e miR2120 miR2120 AAAGAUCUUUAGUCCCGGUUGUUC 2.30 0.87 –1.41 osa-miR2120 miR2275 miR2275b UUCAGUUUCUUCUAAUAUCUCA 4.23 8.66 1.03 bdi-miR2275b miR2275-3p UUUGGUUUCCUCCAAUAUCUCG 1.93 0.79 –1.29 tae-miR2275-3p miR319 miR319-3p ACUGGAUGACGCGGGAGCUAA 125.58 209.15 0.74 ata-miR319-3p miR319b-3p UUGGACUGAAGGGUGCUCCCU 3.34 0.55 –2.60 bdi-miR319b-3p miR3446 miR3446-5p CUCGGAAGCUAGACGUGUGGCAGG 13.35 27.80 1.06 aly-miR3446-5p miR3711 miR3711 UGGCGCUAGAAGGAGGGCCU 1.11 2.76 1.31 pab-miR3711 miR393 miR393-5p UUCCAAAGGGAUCGCAUUGAU 164.82 95.36 –0.79 ata-miR393-5p miR393b-3p UCAGUGCAAUCCCUUUGGAAU 5.79 3.23 –0.84 bdi-miR393b-3p miR3947 miR3947-5p UUAUUUCAGUAGACGACGUCACA 0.52 0.24 –1.14 csi-miR3947-5p miR395 miR395b UGAAGUGUUUGGGGGAACUC 0.67 0.08 –3.08 tae-miR395b miR396 miR396b-5p UCCACAGGCUUUCUUGAACUG 126.18 71.66 –0.82 ata-miR396b-5p miR396c-5p UUCCACAGCUUUCUUGAACUU 23.29 14.25 –0.71 ata-miR396c-5p miR396d-3p GUUCAAGAAAGCCCAUGGAAA 0.07 0.79 3.41 ata-miR396d-3p miR396e-3p GUUCAAUAAAGCUGUGGGAAA 1.11 2.60 1.22 ata-miR396e-3p miR396-5p AACUGUGAACUCGCGGGGAUG 5.49 2.83 –0.95 tae-miR396-5p miR397 miR397b-5p AUUGAGUGCAGCGUUGAUGAA 7.42 4.02 –0.89 bdi-miR397b-5p miR397-5p AUUGAGUGCAGCGUUGAUGAC 0.59 0.31 –0.91 stu-miR397-5p miR4384 miR4384 AAUCAGACACUGCAUUCAAAGACG 2.74 1.50 –0.88 gma-miR4384 miR444 miR444b UGCAGUUGCUGCCUCAAGCUU 6.45 2.05 –1.66 bdi-miR444b miR444b UGCAGUUGCUGUCUCAAGCUU 2.89 0.55 –2.39 hvu-miR444b miR5049 miR5049-3p AAUAUGGAUCGGAGGGAGUAC 3.78 7.40 0.97 tae-miR5049-3p miR5062 miR5062b-3p UGAACCUUAGGGAAAAGCCGCAU 1107.39 674.22 –0.72 ata-miR5062b-3p miR5062b-5p GCGGAUUUUUCACCAAGAUUCAAG 0.74 3.62 2.29 ata-miR5062b-5p miR5062-5p UGAACCUUAGGGAACAGCCGCAU 709.13 384.51 –0.88 tae-miR5062-5p miR5071 miR5071 UCAAGCAUCAUAUCGUGGACA 221.20 135.13 –0.71 osa-miR5071 miR5084 miR5084-3p ACCAUACGGUACUGCAGAGGAUC 35.90 22.52 –0.67 ata-miR5084-3p miR5084-5p AUCCUCUACAGUACUGUACGGUGC 12.68 8.19 –0.63 ata-miR5084-5p miR5169 miR5169b UUUGACCAAGUUUGUAGAACA 7.05 4.65 –0.60 bdi-miR5169b miR5175 miR5175-5p UUCCAAAUUACUCGUCGUGGU 25.15 12.13 –1.05 tae-miR5175-5p miR528 miR528-5p UGGAAGGGGCAUGCAGAGGAG 17.51 9.84 –0.83 ata-miR528-5p miR5387 miR5387b CGUGGCUCUGACCGGUGCUAAAGG 0.30 0.47 0.67 sbi-miR5387b miR6179 miR6179 AACCAGUCGAGGCCAGGGGGUU 0.52 0.94 0.86 hvu-miR6179 miR7714 miR7714-3p CUAAUAUGUAUCGAAGGGAGUAGC 1.34 2.05 0.62 bdi-miR7714-3p miR7757 miR7757-5p.2 CUUCCAUAUCAAAUCAUCUCU 0.22 0.55 1.31 bdi-miR7757-5p.2 miR8028 miR8028-5p UCCUUAUGCUACAAUUGUGAACAA 0.52 0.24 –1.14 stu-miR8028-5p miR8175 miR8175 GAUCCCCGGCAACGGCGCCA 10.53 28.11 1.42 ath-miR8175 miR827 miR827 UUAGAUGACCAUCAGCAAACA 12.76 19.69 0.63 ssp-miR827 miR837 miR837-3p AAACGAACAAAAAACUGAUGG 0.59 0.16 –1.91 ath-miR837-3p miR845 miR845 UGCUCUGAUACCAAUUGUUGG 0.52 0.79 0.60 bdi-miR845 miR894 miR894 CGUUUCACGUCGGGUUCACC 1.41 2.99 1.09 ppt-miR894 miR9653 miR9653b UGGCCAAGGUCUCUUGAGGCU 0.59 0.31 –0.91 tae-miR9653b miR9654 miR9654a-3p UUCUGAAAGGCUUGAAGCGAAU 1.48 0.79 –0.91 tae-miR9654a-3p miR9654b-3p UUCCGAAAGGCUUGAAGCGAAU 3.12 1.57 –0.98 tae-miR9654b-3p miR9655 miR9655-3p CAAGGGAAGGAAGUAGCCAAC 0.30 0.63 1.09 tae-miR9655-3p miR9656 miR9656-3p CUUCGAGACUCUGAACAGCGG 0.89 1.65 0.89 tae-miR9656-3p miR9657 miR9657b-3p CGUGCUUCCUCGUCGAACGGU 0.01 5.83 9.19 tae-miR9657b-3p miR9661 miR9661-5p UGAAGUAGAGCAGGGACCUCA 61.94 37.88 –0.71 tae-miR9661-5p miR9664 miR9664-3p UUGCAGUCCUCGAUGUCGUAG 9.35 5.51 –0.76 tae-miR9664-3p miR9669 miR9669-5p UACUGUGGGCACUUAUUUGAC 2.08 5.43 1.39 tae-miR9669-5p miR9670 miR9670-3p AGGUGGAAUACUUGAAGAAGA 19.06 96.07 2.33 tae-miR9670-3p miR9672 miR9672-5p CUUAAUGACAGUCGUGGUGUC 6.53 1.42 –2.20 ata-miR9672-5p miR9673 miR9673-5p UAAGAAGCAAAUAGCACAUG 14.17 5.83 –1.28 tae-miR9673-5p miR9676 miR9676-5p UGGAUGUCAUCGUGGCCGUACA 11.42 7.09 –0.69 tae-miR9676-5p miR9677 miR9677-5p UUCCACUCUACCAACAGCCACG 1.34 0.63 –1.08 ata-miR9677-5p miR9774 miR9774 CAAGAUAUUGGGUAUUUCUGUC 3.26 1.65 –0.98 tae-miR9774 miR9775 miR9775 UGUGCGCAAUAAGAUUUUGCUA 3.86 1.65 –1.22 tae-miR9775 miR9776 miR9776-5p AGCUUGGACGAGGAUGUGCAA 18.25 34.25 0.91 ata-miR9776-5p miR9863 miR9863b-5p UGUUAUGAUCUGCUUCUCAUC 1.85 0.63 –1.56 ata-miR9863b-5p gc-m0082-3p gc-m0082-3p UGUGAAAACCAGAUCGGACGG 0.89 0.55 –0.69 gc-m0088-3p gc-m0088-3p CGAGAGACUAGUGUAUAAGAA 1.85 2.91 0.65 gc-m0094-3p gc-m0094-3p GUCGAAUGAUGAUCCCAAGUGCA 1.93 0.87 –1.15 gc-m0098-3p gc-m0098-3p ACGUCGAUGAUAGAGAGAGGG 0.67 1.18 0.82 gc-m0187-5p gc-m0187-5p ACAAGGAGGAGGGAGCUUGUG 0.07 0.79 3.41 gc-m0192-5p gc-m0192-5p ACAGAUCAUGAUGUUUGGUAG 2.67 1.50 –0.84 gc-m0298-3p gc-m0298-3p CCAGAUUCAUUGGUACCUCGG 0.59 0.39 –0.59 gc-m0924-5p gc-m0924-5p GCAUCUUGGCUGCGUCGGUGG 0.67 1.42 1.09 gc-m1117-5p gc-m1117-5p CGAGUUGUUGAAAUCCGGCAG 3.04 5.20 0.77 gc-m1136-3p gc-m1136-3p UGGAUAGUUUGAGGUUUUAUUU 5.56 8.90 0.68 gc-m1443-3p gc-m1443-3p AUACCAUCGAAUAGAGCCUAG 0.67 1.34 1.00 gc-m1684-5p gc-m1684-5p UAUUGCAACGGCAGAGAGGAG 6.45 9.92 0.62 gc-m1730-3p gc-m1730-3p GAUUGACGGAUAACAUGUGGC 2.00 5.20 1.38 gc-m1737-5p gc-m1737-5p UGAGUUGAGGAUGUUGCCGCU 0.89 0.55 –0.69 gc-m2007-5p gc-m2007-5p UUAGGAACGGGGCAGCAGACU 1.63 5.43 1.74 gc-m2240-5p gc-m2240-5p AAGGAGGAGGAAGGAGGAAGA 3.12 5.35 0.78 gc-m2445-5p gc-m2445-5p CUGAAAGAGGGACGCCAUUAG 0.74 1.42 0.93 gc-m2668-5p gc-m2668-5p CGGGAGUGGCACGAAUAAGUC 12.91 19.53 0.60 gc-m2726-3p gc-m2726-3p UCUAGUUGGCCUAUGCAGUGA 2.82 6.30 1.16 gc-m3001-3p gc-m3001-3p CUGCCAAUCGAGGAGUUUGG 0.59 0.31 –0.91 gc-m3192-3p gc-m3192-3p UGGUUGGCGUGAGAAGCAGUG 0.67 0.31 –1.08 gc-m3263-5p gc-m3263-5p CCGCGGUGAGGAAUCAGAGGA 2.37 4.02 0.76 gc-m3551-5p gc-m3551-5p CUUGAGCAGACGUAGUAGCAA 20.70 36.70 0.83 gc-m3739-5p gc-m3739-5p AAGAACUCAUCACAUCUAGGC 2.00 3.94 0.98 gc-m3828-3p gc-m3828-3p UCUUCAGUGGUAGGCAGGCGGC 0.30 0.63 1.09 gc-m3845-5p gc-m3845-5p UUGGAAUUUGGAACUGGCUAG 1.93 4.88 1.34 gc-m4070-3p gc-m4070-3p CCACAAUGGUCAGACUCUUAG 2.97 5.12 0.79 gc-m4082-3p gc-m4082-3p UGCAUCCAGUAGUUUUGACCA 0.96 1.65 0.78 gc-m4289-5p gc-m4289-5p AUCGAUACAGAUGAGUUUGAA 0.74 1.18 0.67 gc-m4300-5p gc-m4300-5p UUUCUUUGAAUUUUGGAAGGC 0.52 0.87 0.74 gc-m4309-5p gc-m4309-5p CGGUGGAAGUUGCUCGGACUGA 3.34 5.35 0.68 a Figures in columns headed ‘CS’ and ‘CS-3C’ are normalized read counts. Normalized expression (TPM)=count of miRNA/total count of clean sRNAs×106. The signals were considered as differentially expressed with 1.5-fold change values. b FC, fold change (CS-3C/CS) View Large Fig. 1. View largeDownload slide Comparison of miRNA expression between CS and CS-3C. (a) Scatter plot of miRNAs from CS and CS-3C (normalized expression). (b–e) qRT-PCR validation of miRNA expression in CS and CS-3C. The error bars represent the SDs. Fig. 1. View largeDownload slide Comparison of miRNA expression between CS and CS-3C. (a) Scatter plot of miRNAs from CS and CS-3C (normalized expression). (b–e) qRT-PCR validation of miRNA expression in CS and CS-3C. The error bars represent the SDs. To verify the differential miRNA expression, we randomly selected four differentially expressed miRNAs: down-regulated miR167d-5p and miR167e-5p, and up-regulated tae-miR159a and tae-miR9657b-3p. We performed TaqMan qRT-PCR using the RNA samples from the anthers of CS-3C and CS (Supplementary Table S4). The expression profiles derived were consistent with those based on the number of sRNA reads (Fig. 1b–e). The differences in expression detected by qRT-PCR were significant for the four miRNAs (whose FCs are highlighted in red, Fig. 1b–e). To examine if the differential miRNA expression patterns were specific to the anther tissues, we also performed qRT-PCR analysis of the four miRNAs in the leaf tissues. The results (Supplementary Fig. S2) showed no significant differential expression between CS and CS-3C, suggesting that the differential miRNA expression occurs specifically during anther development. Dynamic changes of 24 nt siRNAs By removing the 24 nt miRNA reads from the total high-quality clean 24 nt sRNA reads, we obtained 7395431 and 6550830 in total of 24 nt siRNA reads for CS and CS-3C wheat lines, respectively. These reads were mapped to the A, B, and D genomes (Supplementary Table S5). It is noteworthy that some of these siRNAs were mapped to two or all three of these genomes. Therefore the numbers of the A, B, and D genome-matching siRNAs were collectively higher than the total sRNA number (Supplementary Table S5). Approximately 40–44% of the A, B, and D genome-matching 24 nt sRNAs were unique to CS or CS-3C, whereas the remaining sRNAs were common to both the wheat lines (Supplementary Table S5). This suggests that numerous 24 nt siRNAs are generated from the genomic sequences that are different between CS and CS-3C. To compare further the 24 nt siRNA profiles of CS-3C and CS, we surveyed the siRNA density along the 1A–7D chromosomes of the two wheat genotypes. We calculated the density of 24 nt siRNAs using the total siRNA reads (already normalized for the slightly different total sRNA read number between the two libraries) within 1 kb sliding windows. We used the value to compare the difference between CS-3C and CS in the distribution of 24 nt siRNAs on the corresponding chromosomes. Supplementary Table S6 lists the number of differential siRNA sliding windows (clustered siRNA locus) for the wheat chromosomes, which were defined to have a minimum of 20 siRNA reads within a 1 kb sliding window in at least one line with FC (CS-3C:CS) ≥1.5 or ≤0.67. Consistent with the 24 nt siRNAs targeting mainly TEs and repetitive sequences, most of the siRNAs in the differentially expressed siRNA sliding windows (~90%) corresponded to the repeat sequences of the chromosomes, with a few corresponding to protein-coding genes and non-coding RNA genes (Supplementary Table S7). These repeats generally had a lower level of total 24 nt siRNA reads in CS-3C than in CS, with 75.4% and 10.5% of them showing down-regulation (FC ≤0.67) and up-regulation (FC ≥1.5) of the 24 nt siRNAs, respectively, and 14.1% exhibiting equal amounts between CS-3C and CS (with a minimum of 10 siRNA reads in at least one line). Of the 64964 repeats in the two wheat lines, 11558 corresponded to the known types of TEs (Supplementary Fig. S3). Among the 24154 repeats with differential 24 nt siRNAs (with a minimum of 10 siRNA reads in at least one line), 5136 corresponded to the known types of TEs (Supplementary Fig. S3). Similar to that of the total repeat population, 75.2% of these sequences of known TEs showed down-regulation of 24 nt siRNAs, with only 11.7% showing siRNA up-regulation in CS-3C compared with that in CS. These results indicate that repeat-associated 24 nt siRNAs are generally down-regulated in the CS-3C line. We also examined the distribution of siRNAs within the differential siRNA sliding windows in protein-coding genes, including both transcribed and 3 kb upstream and downstream flanking regions. As expected from previous studies (Gent et al., 2013), the highest density of 24 nt siRNAs occurred within 1 kb upstream and downstream of the transcribed regions in both the lines (Fig. 2). The distribution of siRNA around genes largely overlapped between CS and CS-3C, with some subtle variations. The siRNA levels in the transcribed regions were slightly higher in CS-3C than in CS for all three genomes. Furthermore, for genomes B and D, the siRNA levels at the siRNA peak at ~500 bp upstream of the transcription start site increased in CS-3C. However, for genome D, the siRNA levels immediately upstream of the transcription start site decreased in CS-3C. Fig. 2. View largeDownload slide Distribution of 24 nt siRNAs around protein-coding genes in CS and CS-3C. (a–d) siRNA densities (1 kb sliding window) in upstream (3 kb), transcribed, and downstream (3 kb) regions of CS and CS-3C on the A genome, B genome, D genome, and the whole genome. Fig. 2. View largeDownload slide Distribution of 24 nt siRNAs around protein-coding genes in CS and CS-3C. (a–d) siRNA densities (1 kb sliding window) in upstream (3 kb), transcribed, and downstream (3 kb) regions of CS and CS-3C on the A genome, B genome, D genome, and the whole genome. As siRNAs at the gene-flanking regions can potentially regulate gene expression, a GO analysis was performed on genes associated with differentially expressed siRNAs within the transcribed and 1 kb upstream regions. The result showed that these genes were enriched for the molecular functions catalytic (GO:0003824) and binding (GO:0005488) activities (Fig. 3). Fig. 3. View largeDownload slide GO enrichment analysis of genes with differentially expressed siRNAs in the transcribed and 1 kb upstream regions between CS and CS-3C. GO terms with corrected FDR <0.05 were considered significantly enriched. (a) Graphical results of GO category of Molecular Function of genes with differentially expressed siRNAs in the transcribed and 1 kb upstream regions. (b) GO flash chart of GO categories of Biological Process, Cellular Component, and Molecular Function of genes with differentially expressed siRNAs in the transcribed and 1 kb upstream regions. Fig. 3. View largeDownload slide GO enrichment analysis of genes with differentially expressed siRNAs in the transcribed and 1 kb upstream regions between CS and CS-3C. GO terms with corrected FDR <0.05 were considered significantly enriched. (a) Graphical results of GO category of Molecular Function of genes with differentially expressed siRNAs in the transcribed and 1 kb upstream regions. (b) GO flash chart of GO categories of Biological Process, Cellular Component, and Molecular Function of genes with differentially expressed siRNAs in the transcribed and 1 kb upstream regions. Target prediction and function analysis of miRNAs To understand the potential biological functions of the 135 differentially expressed miRNAs, we predicted their putative targets using the web-based psRNATarget program with default parameters (http://plantgrn.noble.org/psRNATarget/). The analysis was performed using two different transcript libraries for target search: T. aestivum unigenes from DFCI Gene Index (TAGI) version 12 and T. aestivum cDNA from EbsemblPlants. The putative targets included the essential transcription factors (such as MYB, GRF, ARF, and NAC), DNA-binding protein family-like, DNA ligase, nucleosome/chromatin assembly factor C, and telomere-binding protein. These target genes are known to play crucial roles in the growth and developmental processes of plants (Supplementary Table S8). However, many of these putative target genes had no functional description, which might be attributed to insufficient wheat sequence data, but many of them had conserved functional domains, and might be associated with Gc action (Supplementary Table S8). The GO analysis of the putative target genes predicted using wheat cDNA from EbsemblPlants revealed that most of these genes were in the binding category (GO: 0005488; Fig. 4). Fig. 4. View largeDownload slide GO enrichment analysis for the targets of the differentially expressed miRNAs. (a) Graphical results of GO category of Biological Process for the targets of the differentially expressed miRNAs. (b) GO flash chart of GO categories of Biological Process, Cellular Component, and Molecular Function for the targets of the differentially expressed miRNAs. Fig. 4. View largeDownload slide GO enrichment analysis for the targets of the differentially expressed miRNAs. (a) Graphical results of GO category of Biological Process for the targets of the differentially expressed miRNAs. (b) GO flash chart of GO categories of Biological Process, Cellular Component, and Molecular Function for the targets of the differentially expressed miRNAs. Expression patterns of putative miRNA target genes To examine the functional relevance of the predicted target genes and the corresponding miRNAs, we analyzed the expression of six putative target genes for the four differentially expressed miRNAs, namely tae-miR159a, tae-miR9657b-3p, tae-miR167d-5p, and tae-miR167e-5p, by qRT-PCR. The target genes included TC421314 (MYB3) and TaGAMYB1 for miR159a; TC422650 (DNA-binding protein family), TC419713 (nucleosome/chromatin assembly factor C), and TC451300 for miR9657b-3p; and TC402576 (with the probable chromatin-remodeling complex ATPase chain functional conserved domains) for miR167d-5p and miR167e-5p. Previous studies have indicated that the Myb transcription factors could play a role in anther development or affect male fertility. For instance, TaGAMYB1 and TaGAMYB2 have been verified as targets of tae-miR159, and overexpressing tae-miR159 in rice can reduce fertility and delay flowering time (Li et al., 2007; Wang et al., 2012). MiR9657b-3p has been predicted to target the DNA-binding protein family (TC422650) and nucleosome/chromatin assembly factor C (TC419713) genes. A putative DNA-binding motif, SAP, has been reported to play a role in chromosomal organization (Aravind and Koonin, 2000). Further, CAF-1 has a role in maintaining genomic and epigenetic stability during cell division and development (Huang and Jiao, 2012). Thus, the down-regulation of these two targets could cause abnormal chromosomal organization and chromatin assembly. The target gene of tae-miR167d-5p and tae-miR167e-5p, TC402576, has the probable chromatin-remodeling complex ATPase chain functionally conserved domains. The Arabidopsis SWR1 chromatin-remodeling complex has been reported to be important for DNA repair, somatic recombination, and meiosis, and mutations in At-SWR1 can cause reduced DNA repair capacity, reduced fertility, and irregular gametogenesis (Rosa et al., 2013). The sRNA sequencing data and TaqMan qRT-PCR results indicated that miR159a and miR9657b-3p were up-regulated, whereas miR167d-5p and miR167e-5p were down-regulated in CS-3C when compared with CS (Fig. 1). Consistent with the miRNA expression pattern, the targets of miR159a (TC421314 and TaGAMYB1) and miR9657b-3p (TC422650 and TC419713) were down-regulated in CS-3C, whereas one target of miR167d-5p and miR167e-5p (TC402576) was up-regulated in CS-3C (Fig. 5). The inverse expression correlation between the miRNAs and their putative targets suggests that these genes are genuine targets of the miRNAs. However, the expression level of the target gene TC451300 did not exhibit an inverse correlation with the abundance of miR9657b-3p (Fig. 5). Fig. 5. View largeDownload slide Confirmation of predicted miRNA target genes. (a–f) Quantitative real-time PCR analysis of the relative expression of miRNA targets in the CS and CS-3C line. GAPDH was used as the endogenous control. The error bars represent the SDs. (g) 5' RACE confirmation of the predicted miRNA target site. The arrow indicates the cleavage site, and numbers in parenthesis indicate the proportion of clones analyzed that are mapped to the miR159a cleavage position. Fig. 5. View largeDownload slide Confirmation of predicted miRNA target genes. (a–f) Quantitative real-time PCR analysis of the relative expression of miRNA targets in the CS and CS-3C line. GAPDH was used as the endogenous control. The error bars represent the SDs. (g) 5' RACE confirmation of the predicted miRNA target site. The arrow indicates the cleavage site, and numbers in parenthesis indicate the proportion of clones analyzed that are mapped to the miR159a cleavage position. Validation of miRNA-guided cleavage of target mRNA using 5' RACE The 5' RACE was performed to detect the predicted cleavage product of miR159a for the target gene TC421314, encoding the MYB3 transcription factor, which was down-regulated in CS-3C compared with that of CS (Fig. 5b). Sequencing of seven RACE-PCR clones detected a single cleavage product with the predicted cleavage site corresponding to nucleotide 11 of miR159a (Fig. 5g). This result confirmed miR159a-mediated cleavage of the target gene TC421314 in wheat anther. Overexpression of miR9657b-3p in transgenic rice reduced fertility To test the potential role of the differentially expressed miRNAs in Gc action, we prepared a miR9657b-3p overexpression construct in which the miR9657b-3p precursor sequence (Supplementary Fig. S4a) was driven by the maize ubiquitin promoter (Fig. 6a). We transformed this construct into rice, a model cereal that is more amenable to genetic transformation than wheat. A number of transgenic lines were obtained (Supplementary Fig. S4b). Among them, three lines (Line4, Line8, and Line10) exhibited high levels of miR9657b-3p expression (Fig. 6b), which correlated with the down-regulation of the rice gene TC492017 (Fig. 6c), an ortholog of the predicted wheat target gene TC419713 encoding nucleosome/chromatin assembly factor C. Fig. 6. View largeDownload slide Overexpression of tae-miR9657b-3p in transgenic rice results in reduced fertility. (a) A DNA fragment, including the tae-miR9657b-3p precursor, was cloned into vector pOx to generate the overexpression construct. (b) Quantitative real-time PCR analysis of mature miR9657b-3p in transgenic plants. Data represent the mean of three biological replicates. (c) Quantitative real-time PCR analysis of the target gene of miR9657b-3p, TC492017, in transgenic and wild-type plants. Data represent the mean of three biological replicates. (d) Effects of overexpression of miR9657b-3p on rice fertility (with no seed setting or shriveled seeds). The white arrow and black arrows indicate fertile and sterile spikelets, respectively. (e) Transgenic miR9657b-3p overexpression plants show a reduced seed setting rate compared with wild-type plants. (f) The filled grain number of miR9657b-3p overexpression transgenic plants is reduced compared with wild-type plants. (g) The pollen germination rate of the miR9657b-3p overexpression transgenic plants is lower than that of the wild-type plants. WT, wild-type plants; Line4, 8, 10, transgenic plant lines 4, 8, and 10. Fig. 6. View largeDownload slide Overexpression of tae-miR9657b-3p in transgenic rice results in reduced fertility. (a) A DNA fragment, including the tae-miR9657b-3p precursor, was cloned into vector pOx to generate the overexpression construct. (b) Quantitative real-time PCR analysis of mature miR9657b-3p in transgenic plants. Data represent the mean of three biological replicates. (c) Quantitative real-time PCR analysis of the target gene of miR9657b-3p, TC492017, in transgenic and wild-type plants. Data represent the mean of three biological replicates. (d) Effects of overexpression of miR9657b-3p on rice fertility (with no seed setting or shriveled seeds). The white arrow and black arrows indicate fertile and sterile spikelets, respectively. (e) Transgenic miR9657b-3p overexpression plants show a reduced seed setting rate compared with wild-type plants. (f) The filled grain number of miR9657b-3p overexpression transgenic plants is reduced compared with wild-type plants. (g) The pollen germination rate of the miR9657b-3p overexpression transgenic plants is lower than that of the wild-type plants. WT, wild-type plants; Line4, 8, 10, transgenic plant lines 4, 8, and 10. The phenotypic analysis of the miR9657b-3p-overexpressing transgenic rice lines showed that the fertility was reduced (Fig. 6d, e). The numbers of spikelets per panicle and filled grains were also reduced (Fig. 6f). Additionally, relatively poor pollen germination was observed in the transgenic plants (Fig. 6g). These results indicate that the overaccumulation of miR9657b-3p can decrease plant fertility. Down-regulation of siRNAs corresponding to the LINE/L1 retrotransposon is associated with reduced CHH methylation The 24 nt siRNAs are inducers of RdDM, leading to de novo cytosine methylation at all cytosine contexts, including CG, CHG, and CHH (where H=A, C, or T). The CG and CHG methylation can be maintained during DNA replication by methyltransferase 1 (MET1) and chromomethylase 3 (CMT3), respectively. However, the maintenance of CHH methylation depends on continuous RdDM. The primary function of RdDM is to maintain genome stability by silencing TEs and repetitive DNA. The activation of LINE/L1 retrotransposons has been implicated in genome instability in the cells of head and neck squamous cell carcinoma (HNSCC) (Martínez et al., 2012). Therefore, we were interested to understand if the differential accumulation of 24 nt siRNAs from such TEs was associated with differential DNA methylation. The bisulfite sequencing-based methylation analysis was performed on 10 regions of four TEs (designated as 3A176442068, 3B119165020, 7D195776517, and 7D237973401). The results revealed reduced 24 nt siRNA accumulation in the anthers of CS-3C. All DNA samples were efficiently converted using the bisulfite conversion reagent, as indicated by a complete lack of cytosines in the bisulfite PCR product of the wheat chloroplast psaA gene (Supplementary Fig. S5a). The bisulfite sequencing result showed that CHH methylation, but not CG and CHG methylation, was down-regulated in five regions of all the four TEs in CS-3C compared with that in CS, with at least one region of each TE showing reduced CHH methylation (regions 2 and 3 of TE 3A176442068, region 1 of TE 3B119165020, region 1 of TE 7D195776517, and region 2 of TE 7D237973401). No significant difference in methylation was observed in the remaining five regions of TEs analyzed (Fig. 7; Supplementary Figs S5, S6). This reduced CHH methylation correlated with the down-regulation of 24 nt siRNAs in CS-3C, suggesting that the differential accumulation of 24 nt siRNAs is associated with differential CHH methylation at some of the TE sequences. Unlike the CHH methylation, the CG and CHG methylation showed no significant differences between CS and CS-3C in all the bisulfite-sequenced TE fragments (Fig. 7; Supplementary Fig. S6). All primers used in the present study are listed in Supplementary Table S9. Fig. 7. View largeDownload slide Bisulfite sequencing analysis shows reduced CHH methylation in CS-3C. The x-axis indicates the individual cytosines in a sequential 5' to 3' order along the bisulfite-sequenced region, and the y-axis shows the percentage of methylated cytosines at each CG, CHH, or CHG position. The CG and CHG sites show no clear changes in methylation between CS and CS-3C. (a–c) The changes in methylation in region 2 of 3A176442068. (d–f) The changes in methylation in region 3 of 3A176442068. (g–i) The changes in methylation in region 1 of 7D195776517. (j–l) The changes in methylation in region 2 of 7D237973401. (m–o) The changes in methylation in region 1 of 3B119165020. Fig. 7. View largeDownload slide Bisulfite sequencing analysis shows reduced CHH methylation in CS-3C. The x-axis indicates the individual cytosines in a sequential 5' to 3' order along the bisulfite-sequenced region, and the y-axis shows the percentage of methylated cytosines at each CG, CHH, or CHG position. The CG and CHG sites show no clear changes in methylation between CS and CS-3C. (a–c) The changes in methylation in region 2 of 3A176442068. (d–f) The changes in methylation in region 3 of 3A176442068. (g–i) The changes in methylation in region 1 of 7D195776517. (j–l) The changes in methylation in region 2 of 7D237973401. (m–o) The changes in methylation in region 1 of 3B119165020. Discussion In the present study, we investigated the potential role of sRNAs in the function of the Gc chromosome, which can cause chromosomal mutations and integration of beneficial alien chromosome segments that are useful for wheat breeding. The sequencing analysis of sRNA populations from the anthers uncovered miRNAs and 24 nt repeat-associated siRNAs that are differentially accumulated in the CS and CS-3C lines. These changes in sRNA accumulation corresponded to differential miRNA target gene expression or differential RdDM, which potentially affects fertility and genome stability—the features of Gc action. The sRNA analysis in Gc wheat lines suggests the involvement of miRNA and 24 nt siRNA in the function of the Gc chromosome. Some of the 135 differentially expressed miRNAs (such as miR159 and miR167) have been suggested to play crucial roles in plant fertility by regulating the expression patterns of their targets (Wu et al., 2006; Tang et al., 2012; Wang et al., 2012). Among the predicted targets of the differentially accumulated miRNAs, there were at least seven types of transcription factors (AP2, GRF, MYB, ARF, NAC, SBP, and MADS-box). Previous studies have indicated that some of these transcription factors participate in the development of anther or male fertility (ARF, MYB, and SBP), some regulate cell proliferation or participate in the transcriptional control of cyclins (GRF, AP2, and MYB), and others participate in cell death (MYB and NAC) (Riechmann and Ratcliffe, 2000; Wu et al., 2006; Li et al., 2007; Raffaele et al., 2008; Kaneda et al., 2009; Rodriguez et al., 2010; Xing et al., 2010; Feller et al., 2011; Wang et al., 2012). All these processes might be related to Gc action. In addition to these transcription factors, the differentially accumulated miRNAs also targeted some key binding proteins, including RNA-binding protein, DNA-binding protein, telomere-binding protein, and CDK activator-binding protein, which can potentially participate in chromosomal organization and cell division (Aravind and Koonin, 2000; den Boer and Murray, 2000; McKnight et al., 2002; Kuchar and Fajkus, 2004). For instance, one such target gene, TC422650, belonging to the DNA-binding protein family, was verified to have an inverse expression pattern with the corresponding miRNA, the newly identified miR9657b-3p. Furthermore, a putative DNA-binding motif, SAP, could play a role in chromosomal organization (Aravind and Koonin, 2000). Moreover, the predicted miRNA targets consisted of genes encoding a variety of enzymes, including oxidoreductase, transferase, hydrolase, isomerase, and ligase. These enzymes have important catalytic activity in a number of biological processes, such as DNA repair and replication, microsporogenesis, cell proliferation control and cell death, and endoreduplication (Nishihama et al., 2001; Sugimoto-Shirasu et al., 2002; Gallego and Virshup, 2005; Meloche and Pouysségur, 2007; Wang and Li, 2009; Waterworth et al., 2009). In addition to these three main categories, several other potentially important miRNA targets were also identified in the present study. One target gene of miR9657b-3p, TC419713, encodes a nucleosome/chromatin assembly factor C, and its down-regulation can potentially cause abnormal chromatin assembly. As a Gc chromosome affects plant fertility by killing the gametes lacking it by causing chromosomal aberrations, the predicted targets seemed to be related to Gc action. Furthermore, the miRNAs might participate in Gc action by regulating their expression patterns. To explore the link between the miRNAs and fertility, and to investigate further the role of miRNAs in Gc action, we generated transgenic rice overexpressing tae-miR9657b-3p, an up-regulated miRNA in CS-3C. The results revealed that the rice target gene TC492017 (an ortholog of the wheat target gene, TC419713), encoding the nucleosome/chromatin assembly factor C, was also significantly down-regulated in transgenic lines, potentially causing abnormal chromatin assembly. The fertility of transgenic rice was reduced, which was reminiscent of low fertility in CS-3C (Tsujimoto and Tsunewaki, 1985). As the seed-setting and pollen germination rates of transgenic plants were reduced, the present study provides preliminary evidence to confirm the link between the miRNA and fertility and further demonstrates that the miRNA may be associated with Gc action. In our follow-up study, a pollen-specific promoter that will effectively overexpress key miRNA in wheat will be evaluated to verify comprehensively whether the differentially accumulated miRNA functions in Gc action. The potential function of these putative miRNA targets in male sterility, DNA damage response, chromosomal organization, cell division, and cell death indicates that these differentially accumulated miRNAs may have important regulatory functions in Gc action. It has been reported that the sRNA can move and/or communicate between the pollen grain cytoplasm and the sperm cells (Slotkin et al., 2009). The different miRNAs may participate in different aspects of the dual ‘breaking’ and ‘protecting’ functions of the Gc chromosome. For instance, the miRNAs that are up-regulated in CS-3C, such as miR159a and miR9657b-3p (whose overexpression can lead to reduced fertility), may be expressed in or transported to the gametes without chromosome 3C, thus causing gamete abortion. These miRNAs may participate in the ‘breaking’ function. Similarly, the miRNAs that are down-regulated in CS-3C, such as miR167d-5p and miR167e-5p (whose target is important for DNA repair, somatic recombination, and meiosis), may potentially participate in the ‘protecting’ function of the Gc chromosome in order to protect the gametes. An important finding of the sRNA analysis was the down-regulation of 24 nt repeat-associated siRNAs. This class of siRNAs is the inducer of RdDM and plays a key role in silencing TEs and other repetitive DNA elements in the genome (Zaratiegui et al., 2007; Ha et al., 2009; Kenan-Eichler et al., 2011; Li et al., 2014). In the present study, consistent with the down-regulation of 24 nt siRNAs and their function in RdDM, the bisulfite sequencing analysis of four LINE/L1 retrotransposons showed reduced CHH methylation (hypo-mCHH) in CS-3C plants, whereas no significant changes in CG and CHG methylation were observed. As reported in wheat, the down-regulation of siRNAs may lead to the activation of TEs in the allopolyploids, and the down-regulation of TE-siRNA in the allopolyploids may contribute to genome destabilization at the initial stages of speciation, and a similar phenomenon has also been reported in Arabidopsis allotetraploids (Ha et al., 2009; Kenan-Eichler et al., 2011). It has been proposed that the global demethylation and activation of TEs caused by the significant decrease of siRNAs corresponding to TEs in the polyploids may occur through disruption of developmentally regulated processes in the germline during meiosis, or gametogenesis, or even during the development of an embryo derived from the ‘down-regulated’ gametes, which are reminiscent of the processes and the stage of Gc action. However, in the subsequent generation, the methylation level increased, and the genome was stabilized (Kraitshtein et al., 2010; Kenan-Eichler et al., 2011). Shen et al. (2012) suggested that the increase in genome-wide DNA methylation in F1 hybrids, which is possibly due to RdDM, may lead to hybrid vigor by preserving genomic integrity via altering the expression of genes that normally limit plant growth. Martínez et al. (2012) reported that the hypomethylation of LINE-1 (retrotransposon) may cause pericentromeric instability, which in turn may induce a chromosomal instability phenotype in HNSCC of humans (Martínez et al., 2012). Therefore, it seems that the degree of methylation correlates positively with genomic stability in both plants and animals. Thus, the decrease of repeat-associated 24 nt siRNAs and the associated reduction in CHH methylation in the CS-3C line may cause genome instability, and hence contribute to Gc action. This is similar to the processes involved in the polyploidization of wheat (Kenan-Eichler et al., 2011). Consistently, treatment with the demethylation reagent 5-azacytidine has been reported to enhance chromosome aberration in common wheat carrying the Gc chromosome (de Las Heras et al., 2001; Su et al., 2013). The present study uncovers the possible role of miRNAs and 24 nt siRNAs in Gc action in wheat. The miRNAs may play a dual role by participating in both ‘breaking’ and ‘protecting’ functions of Gc action, whereas the siRNAs may participate in the ‘protecting’ function, loss of which causes reduction in DNA methylation and a decrease in genome stability. It is possible that CG and CHG methylation (more probably hyper-mCG, hyper-mCHG, or both) or some other epigenetic alterations may contribute to the ‘protecting’ function. However, the limited methylation analysis of the present study could not confirm this. Future studies should investigate the genome-wide methylation status of the CS and CS-3C lines to verify whether CG and CHG methylation can take part in the ‘protecting’ function of Gc action. Supplementary data Supplementary data are available at JXB online. Fig. S1. The distribution of small RNA (sRNA) reads in CS and CS-3C wheat lines. Fig. S2. qRT-PCR validation of the expression levels of miRNAs in CS-3C and CS leaf samples. Fig. S3. Classification of all TEs present in CS and CS-3C. Fig. S4. Transgenic overexpression of tae-miR9657b-3p in rice. Fig. S5. Examples of sequencing trace files of bisulfite PCR products. Fig. S6. Bisulfite-sequenced TE regions showing no clear difference in methylation between CS and CS-3C. Table S1. Summary of small RNA sequencing data sets from CS and CS-3C wheat lines. Table S2. The conserved miRNAs identified from the CS and CS-3C wheat lines. Table S3. The novel miRNAs identified from the CS and CS-3C wheat lines. Table S4. The miRNAs selected for TaqMan qPCR validation. Table S5. Total 24 nt siRNA reads mapped to the A, B, and D genome in CS and CS-3C. Table S6. Total number of sliding windows of the differentially expressed siRNAs. Table S7. Classification of 24 nt siRNA-associated sequences. Table S8. Putative target genes for differentially expressed miRNAs. Table S9. Sequence of primers used in this study. Acknowledgments This work was supported by the National Basic Research Program of China (#2011CB111500), the International S&T Cooperation Program of China (#2009DFA32470), and the Graduate Innovation Fund of Harbin Normal University (HSDBSCX2014-03). The authors declare no conflict of interests. References Aravind L , Koonin EV . 2000 . SAP—a putative DNA-binding motif involved in chromosomal organization . Trends in Biochemical Sciences 25 , 112 – 114 . Google Scholar CrossRef Search ADS PubMed Baumberger N , Baulcombe DC . 2005 . Arabidopsis ARGONAUTE1 is an RNA Slicer that selectively recruits microRNAs and short interfering RNAs . Proceedings of the National Academy of Sciences, USA 102 , 11928 – 11933 . Google Scholar CrossRef Search ADS Brennecke J , Malone CD , Aravin AA , Sachidanandam R , Stark A , Hannon GJ . 2008 . An epigenetic role for maternally inherited piRNAs in transposon silencing . Science 322 , 1387 – 1392 . Google Scholar CrossRef Search ADS PubMed Brodersen P , Sakvarelidze-Achard L , Bruun-Rasmussen M , Dunoyer P , Yamamoto YY , Sieburth L , Voinnet O . 2008 . Widespread translational inhibition by plant miRNAs and siRNAs . Science 320 , 1185 – 1190 . Google Scholar CrossRef Search ADS PubMed Carthew RW , Sontheimer EJ . 2009 . Origins and mechanisms of miRNAs and siRNAs . Cell 136 , 642 – 655 . Google Scholar CrossRef Search ADS PubMed Chen C , Ridzon DA , Broomer AJ et al. 2005 . Real-time quantification of microRNAs by stem–loop RT-PCR . Nucleic Acids Research 33 , e179 . Google Scholar CrossRef Search ADS PubMed Crosby ME , Kulshreshtha R , Ivan M , Glazer PM . 2009 . MicroRNA regulation of DNA repair gene expression in hypoxic stress . Cancer Research 69 , 1221 – 1229 . Google Scholar CrossRef Search ADS PubMed de las Heras JI , King IP , Parker JS . 2001 . 5-Azacytidine induces chromosomal breakage in the root tips of wheat carrying the cuckoo chromosome 4S(L) from Aegilops sharonensis . Heredity 87 , 474 – 479 . Google Scholar CrossRef Search ADS PubMed den Boer BG , Murray JA . 2000 . Triggering the cell cycle in plants . Trends in Cell Biology 10 , 245 – 250 . Google Scholar CrossRef Search ADS PubMed Denman RB . 1993 . Using RNAFOLD to predict the activity of small catalytic RNAs . Biotechniques 15 , 1090 – 1095 . Google Scholar PubMed Eamens A , Wang MB , Smith NA , Waterhouse PM . 2008 . RNA silencing in plants: yesterday, today, and tomorrow . Plant Physiology 147 , 456 – 468 . Google Scholar CrossRef Search ADS PubMed Endo TR . 1990 . Gametocidal chromosomes and their induction of chromosome mutations in wheat . Japanese Journal of Genetics 65 , 135 – 152 . Google Scholar CrossRef Search ADS Endo TR , Gill BS . 1996 . The deletion stocks of common wheat . Journal of Heredity 87 , 295 – 307 . Google Scholar CrossRef Search ADS Endo TR , Tsunewaki K . 1975 . Sterility of common wheat with Aegilops triuncialis cytoplasm . Journal of Heredity 66 , 13 – 18 . Google Scholar CrossRef Search ADS Endo TR , Yamamoto M , Mukai Y . 1994 . Structural changes of rye chromosome 1R induced by a gametocidal chromosome . Japanese Journal of Genetics 69 , 13 – 19 . Google Scholar CrossRef Search ADS Feller A , Machemer K , Braun EL , Grotewold E . 2011 . Evolutionary and comparative analysis of MYB and bHLH plant transcription factors . The Plant Journal 66 , 94 – 116 . Google Scholar CrossRef Search ADS PubMed Finch RA , Miller TE , Bennett MD . 1984 . ‘Cuckoo’ Aegilops addition chromosome in wheat ensures its transmission by causing chromosome breaks in meiospores lacking it . Chromosoma 90 , 84 – 88 . Google Scholar CrossRef Search ADS Gallego M , Virshup DM . 2005 . Protein serine/threonine phosphatases: life, death, and sleeping . Current Opinion in Cell Biology 17 , 197 – 202 . Google Scholar CrossRef Search ADS PubMed Gent JI , Ellis NA , Guo L , Harkess AE , Yao Y , Zhang X , Dawe RK . 2013 . CHH islands: de novo DNA methylation in near-gene chromatin regulation in maize . Genome Research 23 , 628 – 637 . Google Scholar CrossRef Search ADS PubMed Ghani MA , Li J , Rao L , Raza MA , Cao L , Yu N , Zou X , Chen L . 2014 . The role of small RNAs in wide hybridisation and allopolyploidisation between Brassica rapa and Brassica nigra . BMC Plant Biology 14 , 272 . Google Scholar CrossRef Search ADS PubMed Ha M , Lu J , Tian L , Ramachandran V , Kasschau KD , Chapman EJ , Carrington JC , Chen X , Wang XJ , Chen ZJ . 2009 . Small RNAs serve as a genetic buffer against genomic shock in Arabidopsis interspecific hybrids and allopolyploids . Proceedings of the National Academy of Sciences, USA 106 , 17835 – 17840 . Google Scholar CrossRef Search ADS Haag JR , Pikaard CS . 2011 . Multisubunit RNA polymerases IV and V: purveyors of non-coding RNA for plant gene silencing . Nature Reviews. Molecular Cell Biology 12 , 483 – 492 . Google Scholar CrossRef Search ADS PubMed Hsieh LC , Lin SI , Shih AC , Chen JW , Lin WY , Tseng CY , Li WH , Chiou TJ . 2009 . Uncovering small RNA-mediated responses to phosphate deficiency in Arabidopsis by deep sequencing . Plant Physiology 151 , 2120 – 2132 . Google Scholar CrossRef Search ADS PubMed Hu H , Gatti RA . 2011 . MicroRNAs: new players in the DNA damage response . Journal of Molecular Cell Biology 3 , 151 – 158 . Google Scholar CrossRef Search ADS PubMed Huang H , Jiao R . 2012 . Roles of chromatin assembly factor 1 in the epigenetic control of chromatin plasticity . Science China Life Sciences 55 , 15 – 19 . Google Scholar CrossRef Search ADS PubMed Kaneda T , Taga Y , Takai R , Iwano M , Matsui H , Takayama S , Isogai A , Che FS . 2009 . The transcription factor OsNAC4 is a key positive regulator of plant hypersensitive cell death . EMBO Journal 28 , 926 – 936 . Google Scholar CrossRef Search ADS PubMed Kasschau KD , Fahlgren N , Chapman EJ , Sullivan CM , Cumbie JS , Givan SA , Carrington JC . 2007 . Genome-wide profiling and analysis of Arabidopsis siRNAs . PLoS Biology 5 , e57 . Google Scholar CrossRef Search ADS PubMed Kenan-Eichler M , Leshkowitz D , Tal L , Noor E , Melamed-Bessudo C , Feldman M , Levy AA . 2011 . Wheat hybridization and polyploidization results in deregulation of small RNAs . Genetics 188 , 263 – 272 . Google Scholar CrossRef Search ADS PubMed Kim VN . 2005 . MicroRNA biogenesis: coordinated cropping and dicing . Nature Reviews. Molecular Cell Biology 6 , 376 – 385 . Google Scholar CrossRef Search ADS PubMed King IP , Laurie DA . 1993 . Chromosome damage in early embryo and endosperm development in crosses involving the preferentially transmitted 4S1 chromosome of Aegilops sharonensis . Heredity 70 , 52 – 59 . Google Scholar CrossRef Search ADS Kofman AV , Kim J , Park SY et al. 2013 . microRNA-34a promotes DNA damage and mitotic catastrophe . Cell Cycle 12 , 3500 – 3511 . Google Scholar CrossRef Search ADS PubMed Kozomara A , Griffiths-Jones S . 2014 . miRBase: annotating high confidence microRNAs using deep sequencing data . Nucleic Acids Research 42 , D68 – D73 . Google Scholar CrossRef Search ADS PubMed Kraitshtein Z , Yaakov B , Khasdan V , Kashkush K . 2010 . Genetic and epigenetic dynamics of a retrotransposon after allopolyploidization of wheat . Genetics 186 , 801 – 812 . Google Scholar CrossRef Search ADS PubMed Kuchar M , Fajkus J . 2004 . Interactions of putative telomere-binding proteins in Arabidopsis thaliana: identification of functional TRF2 homolog in plants . FEBS Letters 578 , 311 – 315 . Google Scholar CrossRef Search ADS PubMed Kurihara Y , Takashi Y , Watanabe Y . 2006 . The interaction between DCL1 and HYL1 is important for efficient and precise processing of pri-miRNA in plant microRNA biogenesis . RNA 12 , 206 – 212 . Google Scholar CrossRef Search ADS PubMed Lal A , Pan Y , Navarro F , Dykxhoorn DM , Moreau L , Meire E , Bentwich Z , Lieberman J , Chowdhury D . 2009 . miR-24-mediated downregulation of H2AX suppresses DNA repair in terminally differentiated blood cells . Nature Structural and Molecular Biology 16 , 492 – 498 . Google Scholar CrossRef Search ADS PubMed Le TN , Schumann U , Smith NA et al. 2014 . DNA demethylases target promoter transposable elements to positively regulate stress responsive genes in Arabidopsis . Genome Biology 15 , 458 . Google Scholar CrossRef Search ADS PubMed Lee Y , Kim M , Han J , Yeom KH , Lee S , Baek SH , Kim VN . 2004 . MicroRNA genes are transcribed by RNA polymerase II . EMBO Journal 23 , 4051 – 4060 . Google Scholar CrossRef Search ADS PubMed Li A , Liu D , Wu J et al. 2014 . mRNA and small RNA transcriptomes reveal insights into dynamic homoeolog regulation of allopolyploid heterosis in nascent hexaploid wheat . The Plant Cell 26 , 1878 – 1900 . Google Scholar CrossRef Search ADS PubMed Li SF , Iacuone S , Parish RW . 2007 . Suppression and restoration of male fertility using a transcription factor . Plant Biotechnology Journal 5 , 297 – 312 . Google Scholar CrossRef Search ADS PubMed Liu HH , Tian X , Li YJ , Wu CA , Zheng CC . 2008 . Microarray-based analysis of stress-regulated microRNAs in Arabidopsis thaliana . RNA 14 , 836 – 843 . Google Scholar CrossRef Search ADS PubMed Liu YX , Wang M , Wang XJ . 2014 . Endogenous small RNA clusters in plants . Genomics, Proteomics and Bioinformatics 12 , 64 – 71 . Google Scholar CrossRef Search ADS Llave C , Xie Z , Kasschau KD , Carrington JC . 2002 . Cleavage of Scarecrow-like mRNA targets directed by a class of Arabidopsis miRNA . Science 297 , 2053 – 2056 . Google Scholar CrossRef Search ADS PubMed Maan SS . 1975 . Exclusive preferential transmission of an alien chromosome in common wheat . Crop Science 15 , 287 – 292 . Google Scholar CrossRef Search ADS Martínez JG , Pérez-Escuredo J , Castro-Santos P , Marcos CA , Pendás JL , Fraga MF , Hermsen MA . 2012 . Hypomethylation of LINE-1, and not centromeric SAT-α, is associated with centromeric instability in head and neck squamous cell carcinoma . Cellular Oncology 35 , 259 – 267 . Google Scholar CrossRef Search ADS McKnight TD , Riha K , Shippen DE . 2002 . Telomeres, telomerase, and stability of the plant genome . Plant Molecular Biology 48 , 331 – 337 . Google Scholar CrossRef Search ADS PubMed Meloche S , Pouysségur J . 2007 . The ERK1/2 mitogen-activated protein kinase pathway as a master regulator of the G1- to S-phase transition . Oncogene 26 , 3227 – 3239 . Google Scholar CrossRef Search ADS PubMed Moxon S , Jing R , Szittya G , Schwach F , Rusholme Pilcher RL , Moulton V , Dalmay T . 2008 . Deep sequencing of tomato short RNAs identifies microRNAs targeting genes involved in fruit ripening . Genome Research 18 , 1602 – 1609 . Google Scholar CrossRef Search ADS PubMed Nasuda S , Friebe B , Gill BS . 1998 . Gametocidal genes induce chromosome breakage in the interphase prior to the first mitotic cell division of the male gametophyte in wheat . Genetics 149 , 1115 – 1124 . Google Scholar PubMed Ng DW , Lu J , Chen ZJ . 2012 . Big roles for small RNAs in polyploidy, hybrid vigor, and hybrid incompatibility . Current Opinion in Plant Biology 15 , 154 – 161 . Google Scholar CrossRef Search ADS PubMed Nishihama R , Ishikawa M , Araki S , Soyano T , Asada T , Machida Y . 2001 . The NPK1 mitogen-activated protein kinase kinase kinase is a regulator of cell-plate formation in plant cytokinesis . Genes and Development 15 , 352 – 363 . Google Scholar CrossRef Search ADS PubMed Ogihara Y , Hasegawa K , Tsujimoto H . 1994 . High-resolution cytological mapping of the long arm of chromosome 5A in common wheat using a series of deletion lines induced by gametocidal (Gc) genes of Aegilops speltoides . Molecular and General Genetics 244 , 253 – 259 . Google Scholar CrossRef Search ADS PubMed Pothof J , Verkaik NS , Hoeijmakers JH , van Gent DC . 2009a. MicroRNA responses and stress granule formation modulate the DNA damage response . Cell Cycle 8 , 3462 – 3468 . Google Scholar CrossRef Search ADS PubMed Pothof J , Verkaik NS , van IJcken W , Wiemer EA , Ta VT , van der Horst GT , Jaspers NG , van Gent DC , Hoeijmakers JH , Persengiev SP . 2009b. MicroRNA-mediated gene silencing modulates the UV-induced DNA-damage response . EMBO Journal 28 , 2090 – 2099 . Google Scholar CrossRef Search ADS PubMed Raffaele S , Vailleau F , Léger A , Joubès J , Miersch O , Huard C , Blée E , Mongrand S , Domergue F , Roby D . 2008 . A MYB transcription factor regulates very-long-chain fatty acid biosynthesis for activation of the hypersensitive cell death response in Arabidopsis . The Plant Cell 20 , 752 – 767 . Google Scholar CrossRef Search ADS PubMed Rhoades MW , Reinhart BJ , Lim LP , Burge CB , Bartel B , Bartel DP . 2002 . Prediction of plant microRNA targets . Cell 110 , 513 – 520 . Google Scholar CrossRef Search ADS PubMed Riechmann JL , Ratcliffe OJ . 2000 . A genomic perspective on plant transcription factors . Current Opinion in Plant Biology 3 , 423 – 434 . Google Scholar CrossRef Search ADS PubMed Rodriguez RE , Mecchia MA , Debernardi JM , Schommer C , Weigel D , Palatnik JF . 2010 . Control of cell proliferation in Arabidopsis thaliana by microRNA miR396 . Development 137 , 103 – 112 . Google Scholar CrossRef Search ADS PubMed Rosa M , Von Harder M , Cigliano RA , Schlögelhofer P , Mittelsten Scheid O . 2013 . The Arabidopsis SWR1 chromatin-remodeling complex is important for DNA repair, somatic recombination, and meiosis . The Plant Cell 25 , 1990 – 2001 . Google Scholar CrossRef Search ADS PubMed Shen Y , Zhang Z , Lin H , Liu H , Chen J , Peng H , Cao M , Rong T , Pan G . 2011 . Cytoplasmic male sterility-regulated novel microRNAs from maize . Functional and Integrative Genomics 11 , 179 – 191 . Google Scholar CrossRef Search ADS PubMed Shi F , Endo TR . 1997 . Production of wheat–barley disomic addition lines possessing an Aegilops cylindrica gametocidal chromosome . Genes and Genetic Systems 72 , 243 – 248 . Google Scholar CrossRef Search ADS Slotkin RK , Vaughn M , Borges F , Tanurdzić M , Becker JD , Feijó JA , Martienssen RA . 2009 . Epigenetic reprogramming and small RNA silencing of transposable elements in pollen . Cell 136 , 461 – 472 . Google Scholar CrossRef Search ADS PubMed Su WY , Cong WW , Shu YJ , Wang D , Xu GH , Guo CH . 2013 . Gametocidal chromosomes enhancing chromosome aberration in common wheat induced by 5-azacytidine . Genetics and Molecular Research 12 , 2227 – 2233 . Google Scholar CrossRef Search ADS PubMed Sugimoto-Shirasu K , Stacey NJ , Corsar J , Roberts K , McCann MC . 2002 . DNA topoisomerase VI is essential for endoreduplication in Arabidopsis . Current Biology 12 , 1782 – 1786 . Google Scholar CrossRef Search ADS PubMed Tang Z , Zhang L , Xu C , Yuan S , Zhang F , Zheng Y , Zhao C . 2012 . Uncovering small RNA-mediated responses to cold stress in a wheat thermosensitive genic male-sterile line by deep sequencing . Plant Physiology 159 , 721 – 738 . Google Scholar CrossRef Search ADS PubMed Tsujimoto H . 2005 . Gametocidal genes in wheat as the inducer of chromosome breakage . Frontiers of Wheat Bioscience 100 , 33 – 48 . Tsujimoto H , Tsunewaki K . 1985 . Gametocidal genes in wheat and its relatives. II. Suppressor of the chromosome 3C gametocidal gene of Aegilops triuncialis . Canadian Journal of Genetics and Cytology 27 , 178 – 185 . Google Scholar CrossRef Search ADS Voinnet O . 2009 . Origin, biogenesis, and activity of plant microRNAs . Cell 136 , 669 – 687 . Google Scholar CrossRef Search ADS PubMed Wan G , Mathur R , Hu X , Zhang X , Lu X . 2011 . miRNA response to DNA damage . Trends in Biochemical Sciences 36 , 478 – 484 . Google Scholar CrossRef Search ADS PubMed Wang XL , Li XB . 2009 . The GhACS1 gene encodes an acyl-CoA synthetase which is essential for normal microsporogenesis in early anther development of cotton . The Plant Journal 57 , 473 – 486 . Google Scholar CrossRef Search ADS PubMed Wang Y , Huang JW , Li M , Cavenee WK , Mitchell PS , Zhou X , Tewari M , Furnari FB , Taniguchi T . 2011 . MicroRNA-138 modulates DNA damage response by repressing histone H2AX expression . Molecular Cancer Research 9 , 1100 – 1111 . Google Scholar CrossRef Search ADS PubMed Wang Y , Sun F , Cao H , Peng H , Ni Z , Sun Q , Yao Y . 2012 . TamiR159 directed wheat TaGAMYB cleavage and its involvement in anther development and heat response . PLoS One 7 , e48445 . Google Scholar CrossRef Search ADS PubMed Wang Y , Taniguchi T . 2013 . MicroRNAs and DNA damage response: implications for cancer therapy . Cell Cycle 12 , 32 – 42 . Google Scholar CrossRef Search ADS PubMed Waterworth WM , Kozak J , Provost CM , Bray CM , Angelis KJ , West CE . 2009 . DNA ligase 1 deficient plants display severe growth defects and delayed repair of both DNA single and double strand breaks . BMC Plant Biology 9 , 79 . Google Scholar CrossRef Search ADS PubMed Wei M , Wei H , Wu M , Song M , Zhang J , Yu J , Fan S , Yu S . 2013 . Comparative expression profiling of miRNA during anther development in genetic male sterile and wild type cotton . BMC Plant Biology 13 , 66 . Google Scholar CrossRef Search ADS PubMed Wilting SM , Snijders PJ , Verlaat W et al. 2013 . Altered microRNA expression associated with chromosomal changes contributes to cervical carcinogenesis . Oncogene 32 , 106 – 116 . Google Scholar CrossRef Search ADS PubMed Wu L , Zhou H , Zhang Q , Zhang J , Ni F , Liu C , Qi Y . 2010 . DNA methylation mediated by a microRNA pathway . Molecular Cell 38 , 465 – 475 . Google Scholar CrossRef Search ADS PubMed Wu MF , Tian Q , Reed JW . 2006 . Arabidopsis microRNA167 controls patterns of ARF6 and ARF8 expression, and regulates both female and male reproduction . Development 133 , 4211 – 4218 . Google Scholar CrossRef Search ADS PubMed Xing S , Salinas M , Höhmann S , Berndtgen R , Huijser P . 2010 . miR156-targeted and nontargeted SBP-box transcription factors act in concert to secure male fertility in Arabidopsis . The Plant Cell 22 , 3935 – 3950 . Google Scholar CrossRef Search ADS PubMed Yan J , Zhang H , Zheng Y , Ding Y . 2015 . Comparative expression profiling of miRNAs between the cytoplasmic male sterile line MeixiangA and its maintainer line MeixiangB during rice anther development . Planta 241 , 109 – 123 . Google Scholar CrossRef Search ADS PubMed Yang X , Li L . 2011 . miRDeep-P: a computational tool for analyzing the microRNA transcriptome in plants . Bioinformatics 27 , 2614 – 2615 . Google Scholar CrossRef Search ADS PubMed Zaratiegui M , Irvine DV , Martienssen RA . 2007 . Noncoding RNAs and gene silencing . Cell 128 , 763 – 776 . Google Scholar CrossRef Search ADS PubMed Zhai J , Zhang H , Arikit S , Huang K , Nan GL , Walbot V , Meyers BC . 2015 . Spatiotemporally dynamic, cell-type-dependent premeiotic and meiotic phasiRNAs in maize anthers . Proceedings of the National Academy of Sciences, USA 112 , 3146 – 3151 . Google Scholar CrossRef Search ADS Zhang B , Wang Q . 2015 . MicroRNA-based biotechnology for plant improvement . Journal of Cellular Physiology 230 , 1 – 15 . Google Scholar CrossRef Search ADS PubMed Zhang H , Zhu JK . 2011 . RNA-directed DNA methylation . Current Opinion in Plant Biology 14 , 142 – 147 . Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

Journal of Experimental BotanyOxford University Press

Published: May 11, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off