Eur J Plant Pathol (2018) 151:629–647 https://doi.org/10.1007/s10658-017-1399-y Identification of Magnaporthe oryzae-elicited rice novel miRNAs and their targets by miRNA and degradome sequencing Shuangyu Dong & Jingxin Zhang & Dayuan Sun & Hao Liu & Qiyun Yang & Hui Wang & Zhiqiang Chen & Jiafeng Wang Accepted: 7 December 2017 /Published online: 6 February 2018 The Author(s) 2018. This article is an open access publication Abstract MicroRNAs (miRNAs) playanimportant identified from four small RNA libraries. More- role in plant growth, development and responses to over, 176 putative targets of 23 novel miRNAs, stresses. Rice blast is one of the most devastating which are involved in the various functions, were diseases in rice production. However, little is validated by two degradome analysis. Six novel known regarding the effects of miRNAs response miRNAs were selected for further validation with toriceblast.Herein,bydeepsequencingsmall qRT-PCR analysis and the results showed that their RNA from the susceptible line ZhongerRuanzhan expression levels were associated with blast re- and its space-induced blast resistant mutant line sponse. The knowledge obtained in this study will H4 under normal conditions and upon help us understanding the functions of miRNAs Magnaporthe oryzae (M. orzyzae) infection, sever- and their targets in regulating blast resistance. al known miRNAs were detected and their expres- sion profiles were found to be negatively correlat- ed with their targets. And, a total of 50 novel Keywords Oryza sativa Magnaporthe oryzae-elicited miRNAs induced by M. oryzae infection were also microRNA Target identification Shuangyu Dong, Jingxin Zhang are co-first author Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10658-017-1399-y) contains supplementary material, which is available to authorized users. : : : : : : S. Dong H. Liu H. Wang Z. Chen (*) J. Wang (*) J. Zhang D. Sun Q. Yang National Engineering Research Center of Plant Space Breeding, Key Laboratory of New Technique for Plant Protection in South China Agricultural University, Guangzhou 510642, Guangdong, Plant Protection Research Institute, Guangdong People’sRepublic of China Academy of Agricultural Science, Guangzhou, Guangdong e-mail: firstname.lastname@example.org: email@example.com 510640, People’s Republic of China S. Dong e-mail: firstname.lastname@example.org J. Zhang e-mail: email@example.com H. Liu e-mail: firstname.lastname@example.org D. Sun e-mail: email@example.com H. Wang Q. Yang e-mail: firstname.lastname@example.org e-mail: email@example.com 630 Eur J Plant Pathol (2018) 151:629–647 Introduction To date, the next-generation sequencing technologies have provided high-throughput quantitative expression Rice blast is one of the most serious and devastating profiles with high accuracy that can greatly improve the diseases that cause huge economic losses in rice pro- identification of novel miRNAs. These technologies duction. The development of resistant lines is closely have been widely used in the identification of rice related to maintenance and improvement of rice grain miRNAs (Addo-Quaye et al. 2008;Germanetal. yield and quality. To date, multiple regulatory factors 2008;Li et al. 2011;Liet al. 2016; Sunkar et al. 2008; that mediate rice resistance to diseases have been iden- Zhang et al. 2010). Moreover, degradome sequencing tified by a combination of genetic, biochemical and and/or parallel analysis of RNA ends (PARE) provide high-throughput sequencing approaches. However, the the efficient tools to identify sRNA targets by directly relevant regulatory mechanisms and the complex regu- and globally identifying the residues of sRNA-directed latory networks still need to be further elucidated. target cleavage (Deng et al. 2006; Morin et al. 2008;Zhu MicroRNAs (miRNAs) are one of the most abundant et al. 2008). Herein, in order to identify novel miRNAs small RNAs (sRNAs) in plants and their typical lengths and miRNA-targets modulated by biotic stress, we se- are 18-25 nucleotides (Li et al. 2014). They are a group quenced four small RNAs and two degradome sequenc- of endogenous, non-coding sRNAs involved in regulat- ing libraries. As the result, a total of 50 novel miRNAs ing gene expression mainly via repressing the transla- and 176 miRNA-targets were identified in rice during tion or mediating the cleavage of target mRNA at the M. oryzae infection. Among them, the expression pro- post-transcriptional level (Campo et al. 2013). In addi- files of six selected novel miRNAs were further validat- tion to their role in regulating gene expression associat- ed by qRT-PCR and the target genes of four miRNAs ed with various plant developmental processes, such as were detected through degradome sequencing. There- fore, our data has provided the valuable information for organ morphogenesis, signal transduction pathway, re- sponses to environmental stresses (Lu et al. 2007), investigating the miRNAs induced by rice blast and the miRNAs have also emerged as a potentially important interactions with their targets. Furthermore, further re- means to be applied in the studies on the complex search on rice-blast fungus plant-pathogen system will regulatory networks being operated during plant- enable us to reveal the molecular mechanisms and to fungus interactions (Jones-Rhoades and Bartel 2004; gain new insights into the related regulatory factors, and Nunes et al. 2011;Zhaoet al. 2012a, b). to apply new findings in rice resistance breeding. The discovery and functional analysis of miRNAs related to rice blast have been performed to a certain extent. Host miRNAs are involved in the interactions Materials and Methods between rice and rice blast. It has been demonstrated that the expression profiles of miRNAs are modulated Plant material by blast stress and M. oryzae-derived elicitors could induce the expression of a number of miRNAs in rice One susceptible line ZhongerRuanzhan (ZE) and its (Campo et al. 2013;Wuet al. 2009; Zhu et al. 2008). space-induced blast resistant mutant line H4 were used Accumulating evidence has demonstrated that a number in this study. H4 contains the resistance gene of Pik-H4, of miRNAs are involved in rice immunity against the an allele at the Pik locus in the ZE background (Xiao blast fungus. For example, osa-miR7695 was reported et al. 2011). The M. oryzae race GD0193, one of the to mediate the negative regulation of natural resistance- associated macrophage protein 6 (OsNramp6) to rice Table 1 Build of four sRNA libraries blast(Campoetal. 2013). And, overexpression of Material Treated miR160a and miR398b enhanced plant resistance by restraining the fungal growth (Li et al. 2014). Moreover, H O (mock) Blast (GO0193) it has been verified recently that osa-miR169 negatively wild/susceptible line AC regulates rice immunity by differentially repressing its Zhonger Ruanzhan target genes, the NF-YA family members (Li et al. mutation/resistant line BD 2017). These studies have indicated that miRNAs play H4 an important role in rice immunity against M. oryzae. Eur J Plant Pathol (2018) 151:629–647 631 primary M. oryzae race in Guangdong Province, in Clara, CA). The plant materials were sampled at 0 h incompatible with H4 but compatible with ZE and 24 h after inoculation. The expression profiles of (Additional files S1). Fourth-leaf-stage rice seedlings corresponding target genes were clustered by Cluster grown under natural light in a greenhouse at 28 °C were 3.0 to explore the functions of novel miRNAs. The used for inoculation of the rice blast fungus. For fungal cluster analysis was performed by adapting the hierar- inoculation, freshly prepared M. oryzae spores (1 × 10 chical, Median Center (gene), and average linkage conidia/ ml, containing 0.02% v/v gelatin). program. Leaves of H4 and ZE were collected at 0 h, 24 h after inoculation (HAI), respectively. Four small RNA librar- Analysis of gene expression profiles with qRT-PCR ies (Table 1) were constructed with the small RNA extracted from the collected leaves and subjected to high Total RNA was extracted from 100 mg of rice seedlings throughput sequencing. For two degradome library con- with Trizol (Invitrogen, Carlsbad, CA, USA), and puri- struction, equal amounts of RNA samples (water-treated fied RNA was reversely transcribed into cDNA using and pathogen-treated) of ZE isolated at 0 h and 24 h after PrimeScript™RT Reagent Kit (Takara, Dalian, China). inoculation were mixed to generate one degradome The qRT-PCR assay was performed using the Applied library S, and equal amounts of RNA samples (water- Biosystems TaqMan® microRNA Assay (Foster City, treated and pathogen-treated) of H4 isolated at 0 h and CA, USA). A two-step assay was performed on a 24 h after inoculation were mixed to generate another RoterGene-6000 (Corbett Research, Australia) accord- degradome library R. All the samples were immediately ing to commercial protocols. After being normalized by frozen in liquid nitrogen, and stored at −80 °C for further reference gene U6 RNA, relative quantification of ex- use. pression levels of novel miRNA was calculated with -ΔΔCT method (Livak and Schmittgen 2001) using Construction and sequencing of sRNA and degradome the manual threshold cycle setting. All samples and libraries standards were run in triplicate. Small RNA library construction and Illumina sequenc- ing were performed as described (Mi et al. 2008). Total Results RNA was isolated using Trizol (Invitrogen, Carlsbad, CA, USA), and sRNAs were separated and enriched by Deep sequencing of four sRNA libraries size fractionation with 15% denaturing PAGE. Purified 18-30 nt sRNAs were reversely transcribed after ligating To identify the miRNAs elicited by blast fungus, four 5’and 3’adaptors, and subsequently sequenced using a libraries were constructed (Table 1) and the results were Solexa high-throughput sequencer (Illumina, USA) at shown in Table 2, with the lengths ranging from 10 to BGI, Shenzhen, China. 30 nt. A majority of the redundant sRNAs were 21– The quantity and purity of the isolated total RNA 24 nt in length (Fig. 1a), consistent with the distribution were examined by using Bioanalyzer 2100 and RNA of the typical sizes by dicer-derived products (Eulgem 6000 Nano LabChip Kit (Agilent, CA, USA) with RIN and Somssich 2007). number > 7.0. Approximately 20 μg of total RNA were These sRNA libraries showed variations among dif- used to prepare degradome library as reported previous- ferent rice lines and treatments (Fig. 1b). Remarkable ly (Li et al. 2015;Addo-Quaye etal. 2008;Hafner etal. differences existed between two lines (ZE and H4) 2008). The single-end sequencing (36 bp) was per- under one kind of treatment (mock or blast infection), formed on an Illumina Hiseq2500 at the LC-BIO (Hang- as it demonstrated that the number of 79216 (3.15%) zhou, China) following the vendor’s recommended and 97743 (10.73%) unique sRNAs were found upon protocol. mock- and blast-treated libraries in different lines, re- spectively. However, the most significant changes were Analysis of target gene expression through microarray induced by the blast fungus in the ZE library, which shared only 54782 (2.72%) unique sRNAs with library Analysis of gene expression profiles was performed on A, though library A was the most abundant one; In the GeneChip rice genome array (Affymetrix, Santa contrast, the smallest difference was elicited by the blast 632 Eur J Plant Pathol (2018) 151:629–647 Table 2 Statistics of small RNA sequenced reads Type A B C D Number % Number % Number % Number % Total reads 11389356 14885325 12439284 13644512 High quality 10521573 100 13601697 100 11499840 100 12661721 100 Adaptor3’ null 2887 0.03 19351 0.14 27869 0.24 12134 0.10 Insert null 24634 0.23 7533 0.06 6637 0.06 3079 0.02 Adaptor5’ 295984 2.81 67206 0.49 58909 0.51 62863 0.50 contaminants Smaller than 18 nt 1448800 13.77 1349217 9.92 1468269 12.77 1444913 11.41 PolyA 232 0.00 595 0.00 796 0.01 545 0.00 Clean reads 8749036 83.15 12157795 89.38 9937360 86.41 11138187 87.97 Libraries: A, mock-treated wild-type; B, mock-treated space-induced mutant; C, Magnaporthe oryzae (blast)-treated wild-type; D, blast- treated space-induced mutant pathogen in the resistant mutant library B, which shared registered miRNAs were expressed specifically in li- 123609 (11.94%) unique sRNAs with library D. There- brary A. On the other hand, 186 registered miRNAs fore, these results suggested that diverse expression appeared synchronously in library B and D, as shown profiles of sRNAs might be related to blast resistance in Fig. 2b. of rice lines. The microarray analysis of rice genome expression Using the Short Oligonucleotide Analysis Package was used for auxiliary identification of miRNA targets, (SOAP)(Beijing Genomics Institute)(BGI) to match which enable us to gain a better knowledge about with the sRNA reads, the known rice sRNAs accounted miRNA functions (Sunkar et al. 2007). Therefore, we for 85.46% (A, 7476714), 79.35% (B, 9647324), linked up with the expression profiles of miRNAs and 77.70% (C, 7721715) and 76.10% (D, 8476386) of the their targets to further illustrate their relationships upon rice genome, respectively. After removing other RNA blast infection. As shown in Fig. 2c, the expression of categories matched to NCBI Genbank, Rfam database, miR164 was negatively correlated with its targets known rice miRNA precursor, repeat associated RNA (LOC_Os12g05260, and LOC_Os04g40780) upon and siRNA, the remaining reads: 1681359(A); wide-type libraries. In addition, the expression profiles 3829741(B); 3182403(C); and 3970132(D), were used of miR528, miR535, miR156, miR166 and miR167 for further analysis, as shown in Fig. 1c. were negatively correlated with their targets to certain extents, respectively. Similar changes could also be seen in H4 as well (Fig. 2d), demonstrating certain degrada- Expression profiles of registered miRNAs and related tive functions of miRNA- targets. Thus, they may be sRNA signatures involved in the regulation of rice immunity against M. oryzae infection. Since some miRNAs were tissue-specific, time-specific or stress-induced, only 291, 210, 164 and 220 registered miRNAs were identified in libraries A, B, C and D Identification and analysis of novel miRNAs elicited respectively, as summarized in Table 3. Particularly, by M. oryzae infection osa-miR162 and osa-miR168 were both significantly down-regulated in the wild-type, while showed an in- To identify the novel miRNAs elicited by the blast conspicuous difference in the mutant (Fig. 2a). Osa- pathogen infection, the surrounding sequences of un- miR162 and osa-miR168, which target AGO protein named sRNAs were extracted and their secondary struc- and DCL1, respectively, are more liable to be altered tures were predicted by using RNAfold and Mireap. by the pathogen in the wild-type plant than in the After being validated by Mircheck and following the mutant. Similarly, registered miRNAs showed the previously set criteria (Rajagopalan et al. 2006; Song greatest diversity between library A and C, 135 et al. 2010), a total of 50 novel miRNAs were identified Eur J Plant Pathol (2018) 151:629–647 633 Fig. 1 Primary analysis of sequencing data from wild-type and unique sRNA between different libraries. c Annotation of sRNA space- induced mutant rice lines treated with Magnaporthe oryzae. signatures of the four libraries a Size distribution of sRNAs signatures. b Common and specific 634 Eur J Plant Pathol (2018) 151:629–647 Fig. 1 (continued) and classified into three categories, as shown in Table 4. reported from a previous study were also found in our Firstly, nine novel miRNAs, named T2-T10, occurred research (Zhao et al. 2012a, b). with miRNAs* synchronously in at least one library. In plants, a majority of miRNAs are mainly derived Secondly, two sequences, T12 (osa-miR169c-a) and from intergenic regions, and a minority of miRNAs is T13, were defined as the conserved miRNAs by align- derived from introns or exons (Teune and Steger 2010). ment with osa-miRNA169 families and osa-miRNA818 Based on The Institute for Genomic Research (TIGR) families, separately, as shown in Fig. 3a, b. Thirdly, 39 database, 29 novel miRNAs are positioned in intergenic unnamed novel miRNAs, from the 43 candidate regions of the rice genome, 16 and 5 novel miRNAs were miRNAs present in at least two libraries, were grouped produced in introns and exons, respectively. They may be into the third category and named T14-T52. Addition- related to most of the non-conservative miRNAs derived ally, osa-miR2863b (T1) and osa-miR396i (T11) from the coding regions (Chen et al. 2011; Wei et al. Table 3 Summary of registered rice miRNA in four rice libraries miRNA miRNA* miRNA Unique sRNAs matched to Total sRNAs matched to precursors miRNA precursors miRNA precursors Registered miRNA 451 1 414 –– A 291 1 279 4282 1,725,059 B 210 0 204 1082 1,317,304 C 164 0 159 690 1,109,715 D 220 0 213 1091 1,240,916 Libraries: A, mock-treated wild-type; B, mock-treated space-induced mutant; C, Magnaporthe oryzae (blast)-treated wild-type; D, blast- treated space-induced mutant Eur J Plant Pathol (2018) 151:629–647 635 Fig. 2 Common and specific registered miRNA in wild-type and former TPM). b Common and specific registered miRNAs be- space- induced resistant mutant elicited by Magnaporthe oryzae. a tween different libraries. c PCA (Principle component analysis) of Expression analysis of osa-miR162 and osa-miR168 in different expression of registered miRNAs between different libraries. A, libraries. The expression of miRNAs in two samples (control and mock-treated wild-type; B, mock-treated mutant; C, blast-treated treatment) was normalized to produce the expression of transcript wild-type; D, blast-treated mutant. d-e Expression profile of reg- per million (TPM), and fold-change means log (latter TPM / istered miRNA and targets in ZE and H4 libraries 2 636 Eur J Plant Pathol (2018) 151:629–647 Fig. 2 (continued) 2011). Some miRNAs can be the transcripts derived from is a feature of plant miRNA, because AGO1 protein the cluster area of the same miRNA site. In this study, we tends to be combined with U of the first base identified four miRNAs in precursor of annotated (Schreiber et al. 2011). In this study, 19 novel miRNAs miRNAs, as shown in Fig. 3c. T14 and T36 resided on started with U. In addition, the eighth base is mainly G the same arm of osa-miR169f and osa-miR167e. More- associated with the identification of RISC (Mei et al. over, T48 and T51 were derived from the complementary 1998), as shown in Fig. 3d. It is worth mentioning that arms of osa-miR399d and osa-miR2864. there are nine miRNAs being 23 nt in length, and seven The lengths of novel miRNAs are mainly distributed of which had a bias for G at the first position, suggesting in the range of 20 to 24 nt. It has been reported that 5 ‘U that they may be a new type of rice miRNAs. Eur J Plant Pathol (2018) 151:629–647 637 Fig. 2 (continued) Promoter elements play an important role in regulatory miRNAs. Moreover, the motifs of CGTCA, TGA, TCA network of plant miRNAs, and their binding sites of many and TGACG responding to the stress induced by jasmonic transcription factors, such as MYC, ARF, and LFY etc., acidmethyl (MeJA) and salicylic acid (SA) can be detect- can often be found in miRNA promoter regions. In our ed nearly in all the novel miRNAs except T33. Addition- study, diverse kinds of cis-elements of these novel ally, the upstream sequences of 27 novel miRNAs were miRNAs had been identified in their promoter regions, embedded with TC-rich repeats, which are involved in some of which are related to fungal trigger or specific defense and stress responsiveness. On the whole, a total of hormone responses (Table 5). Among them, box-w1 and 50 novel miRNA genes were predicted to be associated W-box, which belong to the fungal elicitor responsive with diverse transcription factors involved in defense elements, were found in the promoters of 19 novel against fungal infection and/or other stresses. 638 Eur J Plant Pathol (2018) 151:629–647 Table 4 Identification of novel miRNAs in rice elicited by Magnaporthe oryzae MiR-name Sequence(5′-3′) Length Loci A B C D Evidence Location AMFE (kcal/mol/nt) MFEI Transcriptional factor binding elements a b c FERE MSRE DSRE T1 (osa-miR2863b) TTCGTTTATTTGGACTAGAGT 21 1 9 11 Star/EST intergenic −39.5 1.33 √ T2 AAATTACTTGTCGTTCTAGCT 21 1 63 Star intergenic −37.1 1.17 √√ T3 TGTGTAGCCACATTGTAAGGG 21 1 72 Star sense to intron −40.9 0.79 √√ √ T4 TTGGGAGGTGGTGAGTACTAAG 22 1 361 113 50 85 Star/EST intergenic −54.8 1.14 √ T5 TGCGTGAAGTAGCAATCTTGC 21 1 12 Star intergenic −61.1 1.60 √√ T6 CTTCGGGGGAGGAGAGAAGC 20 1 16 Star intergenic −52.2 1.05 √√ T7 AGTTTGGACTTAAATTTGGTA 21 1 188 Star intergenic −45.8 1.88 √√ T8 ACAAAGGACAACAGACTGAAGA 22 1 35 Star antisense to intron −52.0 1.37 √√ T9 AGAGACGATTGACTTAAGATGGC 23 1 98 Star sense to intron −47.2 1.25 √√ √ T10 GTAAGGGCGTTTACAGGCATACA 23 1 77 62 117 Star sense to intron −72.5 1.30 √ T11(osa-miR396i) TCCACAGGCTTTCTTGAACGG 21 1 1 EST intergenic −51.4 0.70 √ T12 TAGCCAAGGACAGACTTGCCGG 22 1 9 EST sense to intron −54.1 0.95 √√ T13 TTTTGGACGGAGAGAGTATAC 21 1 30 5 28 intergenic −24.7 0.78 √√ T14 TGAGGACAAGAGCTGATTCGG 21 1 107 8 intergenic −53.4 1.14 √ T15 GGAAGAGAAGATCCAAGGGC 20 1 24 15 sense to intron −45.3 0.81 √ T16 TAGATGGCTGATCTGGTGTG 20 1 128 13 8 sense to intron −45.0 0.92 √√ √ T17 GTGGAGGGTGGATGCGGCGGCG 22 1 16 5 intergenic −67.1 0.91 √ T18 GGGCGGAGCTAGGTAGGAGAGGG 23 1 237 109 sense to intron −38.2 0.82 √√ T19 ATCTGAACGTGGACAATGCTAG 22 1 76 60 98 sense to intron −48.5 1.60 √√ T20 TATGAATTTGGATAGGGGCGTGC 23 1 32 10 intergenic −53.5 1.27 √√ √ T21 GGAGGGAGAGGAAGAAGATGGGC 23 1 43 8 EST intergenic −22.3 0.60 √√ T22 AGGACCAGGGAGGGGCGGCGC 21 1 19 22 7 intergenic −55.2 0.76 √√ T23 TTTGGACATAGATGACATAC 20 1 5 7 intergenic −41.5 1.02 √ T24 GTTGGGATGGAGGTAAGAGGAGA 23 1 12 24 intergenic −34.2 0.74 √√ T25 AGCAGTGGAAGGGGCATGCAG 21 1 17 5 20 EST antisense to exon −57.8 1.08 √ T26 TCTGAAAGAGAGGATGCATG 20 1 32 10 sense to exon −55.6 1.37 √√ T27 AGGATTGGGAGTAGTATACGA 21 1 34 32 intergenic −45.7 1.00 √√ T28 ATAAAAACCGGTACCTATGAG 21 1 15 7 antisense to intron −51.6 1.27 √√ T29 GCTGGCGTGGCAGGATAAAA 20 1 8 6 intergenic −49.9 0.92 √√ √ T30 CGTGGGCGCGGAGGGAGGCAA 21 1 12 23 intergenic −45.8 0.66 √√ Eur J Plant Pathol (2018) 151:629–647 639 Table 4 (continued) MiR-name Sequence(5′-3′) Length Loci A B C D Evidence Location AMFE (kcal/mol/nt) MFEI Transcriptional factor binding elements a b c FERE MSRE DSRE T31 TTTTGAAACGGAGGGAGCATA 21 1 8 13 intergenic −32.0 1.16 √√ T32 TGGGCAAGGAGGGTTGATGGA 21 1 29 63 14 EST intergenic −81.4 1.95 √√ T33 TGGACAATGGTTACGAGTAAT 21 1 71 40 50 intergenic −38.5 1.24 √ T34 GTGGGGCGGCGGTGGTGGCGG 21 1 200 101 56 intergenic −56.8 0.78 √√ √ T35 ATCTGACAGTGAGGAGGCTAG 21 1 12 7 45 intergenic −53.4 1.02 √√ T36 TGGTGTGAGAGAATGAAGCTG 21 1 14 9 intergenic −28.3 0.62 √√ √ T37 TCAACTCCAAAACTGGACAAA 21 1 17 6 sense to intron −29.3 0.66 √ T38 TTTGCAATTTGGGCTAGTGGC 21 1 18 19 sense to intron −45.7 0.82 √√ T39 GTGACAGTGGCTCTAGGGGCGGC 23 1 6 28 antisense to intron −29.0 0.46 √√ √ T40 ATACCTGAGGAGGATGGCAT 20 1 17 10 antisense to intron −31.8 0.73 √√ T41 GAAGAATGAGAATGAGAAGAAGG 23 2 19 23 intergenic −43.2 1.20 √ T42 GGGGGAGAAGGGGCGCGCGGC 21 1 5 6 antisense to extron −41.9 0.59 √√ √ T43 GCGGGAGAGGGAGAGGGAGGAG 22 1 5 13 antisense to intron −54.2 0.79 √√ T44 TCAGCACGGATACATTATTTT 21 1 5 7 intergenic −61.1 1.25 √√ T45 AAAACTAAAGAAGGGCAAAAC 21 1 7 6 intergenic −36.2 1.07 √√ T46 AAGAACTGTTACAGATGAGGG 21 1 12 48 sense to intron −58.3 1.62 √ T47 AAGGCGGTGGAGGTGACGGCG 21 1 9 13 EST antisense to exon −51.4 0.70 √ T48 TTGAAGACAGTAGTAGGCAGC 21 1 11 9 EST intergenic −42.6 1.01 √ T49 TGATTTTGGTTGGATAAGGCAT 22 1 8 6 intergenic −38.0 1.30 √√ √ T50 CGGGACACAGAGGCGGCAGCA 21 1 5 13 intergenic −53.4 0.76 √√ T51 AAACAAGGACAACAAGAGGTG 21 1 26 40 intergenic −45.5 1.07 √√ T52 GAAGTGGAAGGAGAGGAGCG 23 1 13 24 EST antisense to exon −44.2 0.71 √ GCA indicates fungal elicitor responsive elements: box-w1 and W-box elements indicates MeJA-responsive and Salicylic acid responsive cis-elements: CGTCA-motif; TGA-element; TCA-motif; TGA-element; TGACG-motif indicates defence and stress responsive cis-elements: TC-rich repeats Libraries: A, mock-treated wild-type; B, mock-treated space-induced mutant; C, Magnaporthe oryzae (blast)-treated wild-type; D, blast-treated space-induced mutant. And, the numbers in the columns A ~D mean normalized reads of the related novel miRNA 640 Eur J Plant Pathol (2018) 151:629–647 Fig. 3 Sequence analysis of novel miRNAs elicited by M. oryzae Magnaporthe oryzae.(c) Special origin of novel rice miRNAs infection (a) Antisense sequences of miR169 family members (b) elicited by Magnaporthe oryzae (d)Nucleotide biasat eachposi- Conserved features of the novel rice miRNA T13 elicited by tion in novel rice miRNAs elicited by Magnaporthe oryzae Target analysis of novel miRNAs structural RNAs (rRNAs, tRNAs, snRNAs, and sno R NAs), 1 506 0667 (99. 53%) a nd Degradome sequencing is an effective and primary way 22988985(99.42%) clean reads from libraries S and R to find the targets of miRNAs. In our study, two libraries can be mapped to the genome, respectively. Among were constructed to further validate the novel miRNA them, 5564896(99.49%) and 7268546(99.48%) unique targets. After having removed the 3’adaptor and the mappable reads were produced, respectively (Table 6). Eur J Plant Pathol (2018) 151:629–647 641 Table 5 Transcriptional factor binding elements in promoter region of novel miRNAs Transcriptional factor binding elements Novel miRNAs with specify bingding elements Fungal elicitor responsive elements T3,T6,T9,T16,T20,T26,T28,T29,T31, T33,T34,T36,T38, T39,T42,T43,T49,T50,T51 MeJA-responsiveness and Salicylic acid responsiveness cis-elements T1-T32,T34-T52 Defence and stress responsiveness cis-elements T2,T3,T5,T7-79,T12,T13,T16, T18-T22,T24,T27,T29, T30,T32,T34, T36,T39,T40,T42,T44,T45,T49 Of which, 176 targets of 23 miRNAs were verified by the expression levels of T19, T34 and T46 showed the degradome sequencing. Then, we gathered the miRNA- unified changes between wild types and their mutations. targets to conduct GO analysis and found that their Of the three intervals 6 h, 12 h and 48 h, expression functions were multifarious (Fig. 4a and Additional files profiles of T19 and T46 displayed fluctuation as the S2), including protein binding, signal transduction, re- inoculation time went by, but the expression of T34 sponse to stress etc. Nevertheless, not all the novel kept rising. Other three miRNAs, the expression levels miRNAs were assigned with targets by degradome se- of T4, T13 and T51 changed irregularly between wild quencing. Thus, whether this was due to their low types and their mutations, which may be subjected to the expression levels and/or due to their negative regulation resistant/susceptible material between ZE and H4. via translational repression needs to be validated with further experiments. We further analyzed the expression profiles of six Discussion novel miRNAs of genes that target the receptor Ser/Leu protein kinase or transcription factors or those involved Changes in Expression Profiles of sRNAs or miRNAs in diverse biotic stress response with qRT-PCR. Of which, four novel miRNAs-targets were detected by It is an economic way to control the destructive plant degradome sequencing while the other two novel diseases such as rice blast with resistant cultivars miRNAs-targets were predicted by bioinformatics (Meyers et al. 2008). Therefore, a model of which major (Table 7). As shown in Fig. 4b, the novel miRNAs resistance gene prevents infection from a strain of displayed different kinds of expression patterns after M. oryzae carrying the corresponding avirulence gene inoculation. The first 48 HAI should be a key time (Zhao et al. 2012a, b) was proposed. However, the interval during which novel miRNAs were modulated variations of the pathogen could overcome the resis- by pathogen infection to regulate their targets in tance of the R gene and result in severe blast damage responding to biotic stress. Among the first 48 intervals, (Lu et al. 2008). To effectively address this challenge, Table 6 Overview of reads from Degradome sequencing Sample S(number) S(ratio) R(number) R(ratio) Sum(number) Sum(ratio) Raw Reads 15131343 / 23122999 / 38254342 / reads <15 nt after removing 3′ adaptor 70676 0.47% 134014 0.58% 204690 0.54% Mappable Reads 15060667 99.53% 22988985 99.42% 38049652 99.46% Unique Raw Reads 5593533 / 7306777 / 10842084 / Unique reads <15 nt after removing 3′ adaptor 28637 0.51% 38231 0.52% 55527 0.51% Unique Mappable Reads 5564896 99.49% 7268546 99.48% 10786557 99.49% Transcript Mapped Reads 10697265 70.70% 17471674 75.56% 28168939 73.64% Unique Transcript Mapped Reads 3498125 62.54% 4724399 64.66% 6563882 60.54% Number of input Transcript 49061 / 49061 / 49061 / Number of Coverd Transcript 36668 74.74% 36361 74.11% 38987 79.47% 642 Eur J Plant Pathol (2018) 151:629–647 Fig. 4 Analysis of novel miRNAs-targets (a)GOTerm ofnovel lines. X-axis indicates the time after being treated with pathogen, miRNAs targets (b) Relative expression levels of six novel rice and black lines and red lines represents wild type and the mutant, miRNAs in Magnaporthe oryzae -treated mutant and wild-type respectively the development of new sources of resistant germplasm susceptible indica rice cv. ZE. H4 was found to be and pyramiding different genes related to blast resis- resistant to more isolates than twelve monogenic lines tance are urgently needed. A mutant, H4, conferring (Xiao et al. 2011), suggesting that H4 can be an excel- stable and high-level resistance to blast at both the lent source for the development of blast resistant seedling and maturity stages was derived from a varieties. Eur J Plant Pathol (2018) 151:629–647 643 Fig. 4 (continued) In the current study, we attempted to explore the A new type of 23 nt miRNA in rice miRNAs induced by blast. It should be noted that under the mock-treated condition, H4 exhibited significantly In plant, most of miRNAs are typically 21 nt or 22 nt in diverse sRNA components different from those of the length. The 23 nt miRNAs are rare and it occurs only by wild-type ZE, indicating that the variation in expression bioinformatics prediction at an early stage (Willmann may be caused not only by stress but also by morphologic and Poethig 2007). However, in the past several years, and material differences during normal growth. Since some 23 nt rice miRNAs, such as osa-miR1863b.2 sRNA components of the wild-type are much more vari- (Zhao et al. 2012a, b), the osa-miR2275c (Lu et al. able than those of the mutant after pathogen inoculation, 2008), the osa-miR5073 (Wei et al. 2006) and osa- the variation in sRNAs might also be another key factor in miR5340 (Willmann and Poethig 2007), were registered enhancing the resistance of mutant to the pathogen, and in miRbase. In our study, nine novel miRNAs were some sRNAs could be involved in the blast-stress re- identified from a large number of 23 nt sRNA signa- sponse. For instance, Osa-miR162 and osa-miR168, tures, which had been reported in Brassica napus. They which target AGO protein and DCL1, respectively, could might be a specific class of functional miRNAs lasting interfere with the genesis of miRNAs (Morel et al. 2002; for certain period of time or under certain conditions Zhou et al. 2007). The proteins required for sRNA bio- (Vaucheret et al. 2004). Seven of the nine novel genesis and function have been shown to be required for miRNAs in rice preferred to be initiated with a G bias, resistance to pathogens (Lindow et al. 2007). Herein, we but they were inconsistent with the 23 nt signatures from found that blast-induced osa-miR162 and osa-miR168 B. napus, which preferred to end with an A bias were much more variable in the wild-type than in the (Vaucheret et al. 2004). This difference implicates that mutant, and the variability was consistent with the greater they may be related to specific functions in different changes in miRNA components or their expression levels organisms. in the wild-type (Fig. 2a). Consequently, the stable expres- Moreover, the presence of 23 nt novel miRNAs was sion profiles of some miRNAs might help to enhance the validated by the following lines of evidence: 1) two blast resistance in the mutant, and is, therefore, feasible to 23 nt miRNAs, T9 and T10, were supported by the presence of miRNA* and appropriate myeloid Elf-1 like identify the novel miRNAs that respond to the pathogen. 644 Eur J Plant Pathol (2018) 151:629–647 Table 7 GO Term of the 6 novel miRNAs MiRNA Degradome Detected GO Term Targets T13 Yes GO:0007165(signal transduction); GO:0009606(tropism); GO:0009628 (response to abiotic stimulus); GO:0004871(signal transducer activity); GO:0005515(protein binding); GO:0009416(response to light stimulus) T34 Yes GO:0006412(translation); GO:0005515(protein binding); GO:0006950 (response to stress); GO:0009607(response to biotic stimulus); GO:0003700 (sequence-specific DNA binding transcription, DNA-templated); GO:0003700 (sequence-specific DNA binding transcription factor activity) T46 Yes GO:0015979(photosynthesis); GO:0055114(oxidation-reduction process) T51 Yes GO:0007165(signal transduction); GO:0035556(intracellular signal transduction) T4 No GO:0016740(transferase activity); GO:0005515(protein binding); GO:0016301(kinase activity) T19 No GO:0016787(hydrolase activity); GO:0003824(catalytic activity); factors (MEFIs), thus they are authentic miRNAs 2010;Ryu et al. 2006;Wuet al. 2005). Rac Immunity (Lampard et al. 2008; Ledent et al. 2002); and 2) T21 1(RAI1) encodes a putative bHLH transcription factor and T52 were matched to a registered EST; and 3) the involved in the rice defense responses through regulating MEFI values of T20 and T41 were 1.27 and 1.20, the elicitor-responsive genes encoding phenylalanine respectively. The precursors of T20 and T41 were de- ammonia-lyase 1 (PAL1) and OsWRKY19 (Qiu and Yu rived from repetitive loci, but these sequences with 2009). In our study, LOC_Os10g42430.1 and predicted fold-back structures could still be annotated LOC_Os04g41229.1 were indicated as the possible tar- as authentic loci for miRNA in rice (Lampard et al. gets of T34 and T42, respectively, which encode bHLH 2008). Thus, they could be annotated. However, the transcription factors. In addition, the expression level of other three novel miRNAs without define evidence, T34 was significantly increased in the 48 HAI, suggest- T18, T24 and T39, might not be bona fide miRNAs. ing that T34 is induced by pathogen and responds to biotic stress by regulating its targets. WRKY proteins, characterized by the WRKY domain MiRNA-targeted genes involved in resistance variation that binds to a consensus cis-element termed W-box (TTGACT/C) (Shimono et al. 2007), are a family of Since more comprehensive functional genes are affected zinc-finger transcription factors involved in plant response by the non-conserved miRNAs rather than by the con- to pathogen infection and a variety of environmental stress- served miRNAs (Sonnenfeld et al. 2005), genes with diverse functions were predicted to be the targets of the es. To date, more than 100 WRKY genes have been identified in rice genome (Qiu et al. 2007), and many of novel miRNAs. According to GO and KEGG analysis, the targeted genes may be involved in the blast-stress them are involved in the immune response (Abbruscato et al. 2012). The expression of OsWRKY45 is markedly response pathway, and the novel miRNAs may play important roles in plant defense responses through reg- induced in response to ABA and positively regulates the resistance to blast and bacterial blight (Han et al. 2013). ulation of their targeted genes. Overexpression of OsWRKY13 (Wei et al. 2013), Large families of basic helix–loop–helix (bHLH) tran- OsWRKY22 (Chujo et al. 2007), OsWRKY30 (Wang scription factors play essential roles in physiological and et al. 2007), OsWRKY31 (Nishizawa et al. 2016), developmental processes (Fujiwara et al. 2006; OsWRKY47 (Sunkar et al. 2006), OsWRKY53 (Sunkar Nishimura et al. 2009; Ono et al. 2001; Suharsono et al. et al. 2008)and OsWRKY89 (Dugas and Bartel 2008) 2002). In M. oryzae, Mstu 1, an APSES protein with highly conserved bHLH DNA binding domains, is im- clearly enhanced the resistance against blast fungus. In our study, Zinc-finger proteins LOC_Os06g47850.1 and portant for pathogenicity and asexual development (Kim et al. 2012); OsRac1 is a key regulator involved in the WRKY DNA-binding protein LOC_Os02g08440, which were targeted by T31 and T34, repectively, were also basal resistance and defense responses (Rushton et al. Eur J Plant Pathol (2018) 151:629–647 645 Campo, S., Peris-Peris, C., Sire, C., Moreno, A. B., Donaire, L., identified, suggesting their possible roles in rice response Zytnicki, M., Notredame, C., Llave, C., & San, S. B. (2013). to blast fungus. Identification of a novel microRNA (miRNA) from rice that targets an alternatively spliced transcript of the Nramp6 (Natural resistance-associated macrophage protein 6) gene Deposition of data involved in pathogen resistance. The New Phytologist, 199(1), 212–227. The sRNA sequences data and GeneChip rice genome Chen, C. J., Liu, Q., Zhang, Y. C., Qu, L. H., Chen, Y. Q., & Gautheret, D. (2011). Genome-wide discovery and analysis array data are accessible through NCBI’s GEO Series of microRNAs and other small RNAs from rice embryogenic accession number GSE36205 and GSE36013. callus. RNA Biology, 8(3), 538–547. Chujo, T., Takai, R., Akimoto-Tomiyama, C., Ando, S., Minami, E., Nagamura, Y., Kaku, H., Shibuya, N., Yasuda, M., Author contributions SYD, JXZ, QYY, HW, ZQC and JFW Nakashita, H., Umemura, K., Okada, A., Okada, K., Nojiri, conceived and designed the experiments. SYD, JXZ and HL H., & Yamane, H. (2007). Involvement of the elicitor- performed the experiments. SYD, JXZ and DYS analyzed the induced gene OsWRKY53 in the expression of defense- data. QQY, HW and ZQC contributed reagents and materials. related genes in rice. Biochimica et Biophysica Acta, SYD, JXZ, QQY, HW, ZQC and JFW wrote and modified the 1769(7-8), 497–505. manuscript. All the authors read and approved the final Deng, Y., Zhu, X., Shen, Y., & He, Z. (2006). Genetic character- manuscript.FundingThis work was supported by the National ization and fine mapping of the blast resistance locus Pigm(t) Natural Science Foundation of China (31401722) and the Natural tightly linked to Pi2 and Pi9 in a broad-spectrum resistant Science Foundation of Guangdong Province of China Chinese variety. Theoretical and Applied Genetics, 113(4), (2014A030313463). 705–713. Compliance with ethical standards Dugas, D. V., & Bartel, B. (2008). Sucrose induction of Arabidopsis miR398 represses two Cu/Zn superoxide dismutases. Plant Molecular Biology, 67(4), 403–417. Conflict of interest The authors declare that they have no con- Eulgem, T., & Somssich, I. E. (2007). Networks of WRKY tran- flict of interest. scription factors in defense signaling. Current Opinion in Plant Biology, 10(4), 366–371. 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