Comprehensive Characteristics of MicroRNA Expression Profile Conferring to Rhizoctonia solani in Rice

doi:10.1016/j.rsci.2019.04.007

Abstract

Abstract:

MicroRNAs (miRNAs) are about 22 nucleotides regulatory non-coding RNAs that play versatile roles in reprogramming plant responses to biotic and abiotic stresses. However, it remains unknown whether miRNAs confer the resistance to necrotrophic fungus Rhizoctonia solani in rice. To investigate whether miRNAs regulate the resistance to R. solani, we constructed 12 small RNA libraries from susceptible and resistant rice cultivars treated with water/pathogen at 5 h post inoculation (hpi), 10 hpi and 20 hpi, respectively. By taking the advantage of next-generation sequencing, we totally collected 400-450 known miRNAs and 450-620 novel miRNAs from the libraries. Expression analysis of miRNAs demonstrated different patterns for known and novel miRNAs upon R. solani challenge. Thirty-four miRNA families were identified to be expressed specifically in rice, and most of them were involved in plant disease resistance. A particular Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis result revealed that a great majority of target genes of regulated miRNAs belonged to the pathway of plant-pathogen interaction. Moreover, miR444b.2, miR531a, mir1861i, novel_miR1956 and novel_miR135 conferred response to R. solani infection confirmed by Northern blot. Our global understanding of miRNA profiling revealed that the regulation of miRNAs may be implicated in the control of rice immunity to R. solani. Analysis of the expression of miRNAs will offer the community with a direction to generate appropriate strategies for controlling rice sheath blight disease.

Key words: rice, microRNA, sheath blight disease, resistance, transcriptomics

Wenlei Cao, Xinxin Cao, Jianhua Zhao, Zhaoyang Zhang, Zhiming Feng, Shouqiang Ouyang, Shimin Zuo. Comprehensive Characteristics of MicroRNA Expression Profile Conferring to Rhizoctonia solani in Rice[J]. Rice Science, 2020, 27(2): 101-112.

Figures/Tables 16

References 78

[1]	Arikit S, Zhai J X, Meyers B C. 2013. Biogenesis and function of rice small RNAs from non-coding RNA precursors. Curr Opin Plant Biol, 16(2): 170-179.
[2]	Aukerman M J, Sakai H. 2003. Regulation of flowering time and floral organ identity by a microRNA and its APETALA2-like target genes. Plant Cell, 15(11): 2730-2741.
[3]	Axtell M J. 2013. Classification and comparison of small RNAs from plants. Annu Rev Plant Biol, 64: 137-159.
[4]	Baldrich P, Campo S, Wu M T, Liu T T, Hsing Y L, San Segundo B. 2015. MicroRNA-mediated regulation of gene expression in the response of rice plants to fungal elicitors. RNA Biol, 12(8): 847-863.
[5]	Barrera-Figueroa B E, Gao L, Wu Z G, Zhou X F, Zhu J H, Jin H L, Liu R Y, Zhu J K. 2012. High throughput sequencing reveals novel and abiotic stress-regulated microRNAs in the inflorescences of rice. BMC Plant Biol, 12: 132.
[6]	Bartel D P. 2004. MicroRNAs: Genomics, biogenesis, mechanism, and function. Cell, 116(2): 281-297.
[7]	Baulcombe D. 2004. RNA silencing in plants. Nature, 431: 356-363.
[8]	Boccara M, Sarazin A, Thiebeauld O, Jay F, Voinnet O, Navarro L, Colot V. 2014. The Arabidopsis miR472-RDR6 silencing pathway modulates PAMP- and effector-triggered immunity through the post-transcriptional control of disease resistance genes. PLoS Pathog, 10(1): e1003883.
[9]	Boller T, Felix G. 2009. A renaissance of elicitors: Perception of microbe-associated molecular patterns and danger signals by pattern-recognition receptors. Ann Rev Plant Biol, 60: 379-406.
[10]	Brodersen P, Sakvarelidze-Achard L, Bruun-Rasmussen M, Dunoyer P, Yamamoto Y Y, Sieburth L, Voinnet O. 2008. Widespread translational inhibition by plant miRNAs and siRNAs. Science, 320: 1185-1190.
[11]	Campo S, Peris-Peris C, Sire C, Moreno A B, Donaire L, Zytnicki M, Notredame C, Llave C, San Segundo B. 2013. Identification of a novel microRNA (miRNA) from rice that targets an alternatively spliced transcript of the Nramp6 (Natural resistance-associated macrophage protein 6) gene involved in pathogen resistance. New Phytol, 199(1): 212-227.
[12]	Cao S J, Zhu Q H, Shen W X, Jiao X M, Zhao X C, Wang M B, Liu L X, Singh S P, Liu Q. 2013. Comparative profiling of miRNA expression in developing seeds of high linoleic and high oleic safflower (Carthamus tinctorius L.) plants. Front Plant Sci, 4: 489.
[13]	Carthew R W, Sontheimer E J. 2009. Origins and mechanisms of miRNAs and siRNAs. Cell, 136(4): 642-655.
[14]	Cheah B H, Nadarajah K, Divate M D, Wickneswari R. 2015. Identification of four functionally important microRNA families with contrasting differential expression profiles between drought-tolerant and susceptible rice leaf at vegetative stage. BMC Genom, 16(1): 692.
[15]	Chen F, Li Q, He Z H. 2007. Proteomic analysis of rice plasma membrane-associated proteins in response to chitooligo- saccharide elicitors. J Integr Plant Biol, 49(6): 863-870.
[16]	Chen X J, Chen Y, Zhang L N, Xu B, Zhang J H, Chen Z X, Tong Y H, Zuo S M, Xu J Y. 2016. Overexpression of OsPGIP1 enhances rice resistance to sheath blight. Plant Dis, 100(2): 388-395.
[17]	Fei Q L, Xia R, Meyers B C. 2013. Phased, secondary, small interfering RNAs in posttranscriptional regulatory networks. Plant Cell, 25(7): 2400-2415.
[18]	Ghildiyal M, Zamore P D. 2009. Small silencing RNAs: An expanding universe. Nat Rev Genet, 10(2): 94-108.
[19]	Ito T M, Polido P B, Rampim M C, Kaschuk G, Souza S G H. 2014. Genome-wide identification and phylogenetic analysis of the AP2/ERF gene superfamily in sweet orange (Citrus sinensis). Genet Mol Res, 13(3): 7839-7851.
[20]	Jiang S F, Wang C J Z, Shu C W, Zhou E X. 2018. Cloning and expression analysis of RsPhm gene in Rhizoctonia solani AG-1IA of rice sheath blight pathogen. Chin J Rice Sci, 32(2): 111-118. (in Chinese with English abstract)
[21]	Jones J D G, Dangl J L. 2006. The plant immune system. Nature, 444: 323-329.
[22]	Jones-Rhoades M W, Bartel D P. 2004. Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol Cell, 14(6): 787-799.
[23]	Karmakar S, Molla K A, Chanda P K, Sarkar S N, Datta S K, Datta K. 2016. Green tissue-specific co-expression of chitinase and oxalate oxidase 4 genes in rice for enhanced resistance against sheath blight. Planta, 243(1): 115-130.
[24]	Kozomara A, Griffiths-Jones S. 2014. miRBase: Annotating high confidence microRNAs using deep sequencing data. Nucl Acids Res, 42: D68-D73.
[25]	Li T, Li H, Zhang Y X, Liu J Y. 2011. Identification and analysis of seven H2O2-responsive miRNAs and 32 new miRNAs in the seedlings of rice (Oryza sativa L. ssp indica). Nucl Acids Res, 39(7): 2821-2833.
[26]	Li Z Y, Xia J, Chen Z, Yu Y, Li Q F, Zhang Y C, Zhang J P, Wang C Y, Zhu X Y, Zhang W, Chen Y Q. 2016. Large-scale rewiring of innate immunity circuitry and microRNA regulation during initial rice blast infection. Sci Rep, 6: 25493.
[27]	Lin R M, He L Y, He J Y, Qin P G, Wang Y R, Deng Q M, Yang X T, Li S C, Wang S Q, Wang W M, Liu H N, Li P, Zheng A P. 2016. Comprehensive analysis of microRNA-seq and target mRNAs of rice sheath blight pathogen provides new insights into pathogenic regulatory mechanisms. DNA Res, 23(5): 415-425.
[28]	Liu W D, Liu J L, Triplett L, Leach J E, Wang G L. 2014. Novel insights into rice innate immunity against bacterial and fungal pathogens. Ann Rev Phytopathol, 52: 213-241.
[29]	Llave C, Kasschau K D, Rector M A, Carrington J C. 2002a. Endogenous and silencing-associated small RNAs in plants. Plant Cell, 14(7): 1605-1619.
[30]	Llave C, Xie Z X, Kasschau K D, Carrington J C. 2002b. Cleavage of scarecrow-like mRNA targets directed by a class of Arabidopsis miRNA. Science, 297: 2053-2056.
[31]	Manavella P A, Koenig D, Weigel D. 2012. Plant secondary siRNA production determined by microRNA-duplex structure. Proc Natl Acad Sci USA, 109(7): 2461-2466.
[32]	Matzke M A, Mosher R A. 2014. RNA-directed DNA methylation: An epigenetic pathway of increasing complexity. Nat Rev Genet, 15(6): 394-408.
[33]	Merico D, Isserlin R, Stueker O, Emili A, Bader G D. 2010. Enrichment map: A network-based method for gene-set enrichment visualization and interpretation. PLoS One, 5(11): e13984.
[34]	Mi S J, Cai T, Hu Y G, Chen Y M, Hodges E, Ni F R, Wu L, Li S, Zhou H Y, Long C Z, Chen S, Hannon G J, Qi Y J. 2008. Sorting of small RNAs into Arabidopsis argonaute complexes is directed by the 5'-terminal nucleotide. Cell, 133(1): 116-127.
[35]	Montgomery T A, Howell M D, Cuperus J T, Li D W, Hansen J E, Alexander A L, Chapman E J, Fahlgren N, Allen E, Carrington J C. 2008. Specificity of ARGONAUTE7-miR390 interaction and dual functionality in TAS3 trans-acting siRNA formation. Cell, 133(1): 128-141.
[36]	Mutum R D, Kumar S, Balyan S, Kansal S, Mathur S, Raghuvanshi S. 2016. Identification of novel miRNAs from drought tolerant rice variety Nagina 22. Sci Rep, 6: 30786.
[37]	Navarro L, Dunoyer P, Jay F, Arnold B, Dharmasiri N, Estelle M, Voinnet O, Jones J D. 2006. A plant miRNA contributes to antibacterial resistance by repressing auxin signaling. Science, 312: 436-439.
[38]	Nischal L, Mohsin M, Khan I, Kardam H, Wadhwa A, Abrol Y P, Iqbal M, Ahmad A. 2012. Identification and comparative analysis of microRNAs associated with low-N tolerance in rice genotypes. PLoS One, 7(12): e50261.
[39]	Ouyang S Q, Park G, Atamian H S, Han C S, Stajich J E, Kaloshian I, Borkovich K A. 2014. MicroRNAs suppress NB domain genes in tomato that confer resistance to Fusarium oxysporum. PLoS Pathog, 10(10): e1004464.
[40]	Padmanabhan C, Zhang X M, Jin H L. 2009. Host small RNAs are big contributors to plant innate immunity. Curr Opin Plant Biol, 12(4): 465-472.
[41]	Palatnik J F, Allen E, Wu X, Schommer C, Schwab R, Carrington J C, Weigel D. 2003. Control of leaf morphogenesis by microRNAs. Nature, 425: 257-263.
[42]	Park W, Li J J, Song R T, Messing J, Chen X M. 2002. CARPEL FACTORY, a dicer homolog, and HEN1, a novel protein, act in microRNA metabolism in Arabidopsis thaliana. Curr Biol, 12(17): 1484-1495.
[43]	Peng T, Sun H Z, Qiao M M, Zhao Y F, Du Y X, Zhang J, Li J Z, Tang G L, Zhao Q Z. 2014. Differentially expressed microRNA cohorts in seed development may contribute to poor grain filling of inferior spikelets in rice. BMC Plant Biol, 14: 196.
[44]	Peng X X, Hu Y J, Tang X K, Zhou P L, Deng X B, Wang H H, Guo Z J. 2012. Constitutive expression of rice WRKY30 gene increases the endogenous jasmonic acid accumulation, PR gene expression and resistance to fungal pathogens in rice. Planta, 236(5): 1485-1498.
[45]	Peng X X, Wang H H, Jang J C, Xiao T, He H H, Jiang D, Tang X K. 2016. OsWRKY80-OsWRKY4 module as a positive regulatory circuit in rice resistance against Rhizoctonia solani. Rice, 9: 63.
[46]	Pooja S, Sweta K, Mohanapriya A, Sudandiradoss C, Siva R, Gothandam K M, Babu S. 2015. Homotypic clustering of OsMYB4 binding site motifs in promoters of the rice genome and cellular-level implications on sheath blight disease resistance. Gene, 561(2): 209-218.
[47]	Qiao Y L, Liu L, Xiong Q, Flores C, Wong J, Shi J X, Wang X B, Liu X G, Xiang Q J, Jiang S S, Zhang F C, Wang Y C, Judelson H S, Chen X M, Ma W B. 2013. Oomycete pathogens encode RNA silencing suppressors. Nat Genet, 45(3): 330-333.
[48]	Raghuram B, Sheikh A H, Sinha A K. 2014. Regulation of MAP kinase signaling cascade by microRNAs in Oryza sativa. Plant Signal Behav, 9(10): e972130.
[49]	Reinhart B J, Slack F J, Basson M, Pasquinelli A E, Bettinger J C, Rougvie A E, Horvitz H R, Ruvkun G. 2000. The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature, 403: 901-906.
[50]	Rubio-Somoza I, Cuperus J T, Weigel D, Carrington J C. 2009. Regulation and functional specialization of small RNA-target nodes during plant development. Curr Opin Plant Biol, 12(5): 622-627.
[51]	Ruepp A, Zollner A, Maier D, Albermann K, Hani J, Mokrejs M, Tetko I, Guldener U, Mannhaupt G, Munsterkotter M, Mewes H W. 2004. The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucl Acids Res, 32(18): 5539-5545.
[52]	Sharma N, Tripathi A, Sanan-Mishra N. 2015. Profiling the expression domains of a rice-specific microRNA under stress. Front Plant Sci, 6: 333.
[53]	Smoot M E, Ono K, Ruscheinski J, Wang P L, Ideker T. 2011. Cytoscape 2.8: New features for data integration and network visualization. Bioinformatics, 27(3): 431-432.
[54]	Stetson D B, Ko J S, Heidmann T, Medzhitov R. 2008. Trex1 prevents cell-intrinsic initiation of autoimmunity. Cell, 134(4): 587-598.
[55]	Sun Z T, He Y Q, Li J M, Wang X, Chen J P. 2015. Genome-wide characterization of rice black streaked dwarf virus-responsive microRNAs in rice leaves and roots by small RNA and degradome sequencing. Plant Cell Physiol, 56(4): 688-699.
[56]	Sunkar R, Kapoor A, Zhu J K. 2006. Posttranscriptional induction of two Cu/Zn superoxide dismutase genes in Arabidopsis is mediated by downregulation of miR398 and important for oxidative stress tolerance. Plant Cell, 18(8): 2051-2065.
[57]	Sunkar R, Chinnusamy V, Zhu J H, Zhu J K. 2007. Small RNAs as big players in plant abiotic stress responses and nutrient deprivation. Trends Plant Sci, 12(7): 301-309.
[58]	Taguchi-Shiobara F, Ozaki H, Sato H, Maeda H, Kojima Y, Ebitani T, Yano M. 2013. Mapping and validation of QTLs for rice sheath blight resistance. Breeding Sci, 63(3): 301-308.
[59]	Trapnell C, Salzberg S L. 2009. How to map billions of short reads onto genomes. Nat Biotechnol, 27(5): 455-457.
[60]	Trapnell C, Hendrickson D G, Sauvageau M, Goff L, Rinn J L, Pachter L. 2013. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol, 31(1): 46-53.
[61]	Vaucheret H. 2006. Post-transcriptional small RNA pathways in plants: Mechanisms and regulations. Genes Dev, 20(7): 759-771.
[62]	Wang H C, Jiao X M, Kong X Y, Hamera S, Wu Y, Chen X Y, Fang R X, Yan Y S. 2016. A signaling cascade from miR444 to RDR1 in rice antiviral RNA silencing pathway. Plant Physiol, 170(4): 2365-2377.
[63]	Wang H H, Meng J, Peng X X, Tang X K, Zhou P L, Xiang J H, Deng X B. 2015. Rice WRKY4 acts as a transcriptional activator mediating defense responses toward Rhizoctonia solani, the causing agent of rice sheath blight. Plant Mol Biol, 89: 157-171.
[64]	Wang R, Lu L X, Pan X B, Hu Z L, Ling F, Yan Y, Liu Y M, Lin Y J. 2015. Functional analysis of OsPGIP1 in rice sheath blight resistance. Plant Mol Biol, 87: 181-191.
[65]	Weiberg A, Wang M, Lin F M, Zhao H W, Zhang Z H, Kaloshian I, Huang H D, Jin H L. 2013. Fungal small RNAs suppress plant immunity by hijacking host RNA interference pathways. Science, 342: 118-123.
[66]	Wu J G, Yang R X, Yang Z R, Yao S Z, Zhao S S, Wang Y, Li P C, Song X W, Jin L, Zhou T, Lan Y, Xie L H, Zhou X P, Chu C C, Qi Y J, Cao X F, Li Y. 2017. ROS accumulation and antiviral defence control by microRNA528 in rice. Nat Plants, 3: 16203.
[67]	Xue X, Cao Z X, Zhang X T, Wang Y, Zhang Y F, Chen Z X, Pan X B, Zuo S M. 2016. Overexpression of OsOSM1 enhances resistance to rice sheath blight. Plant Dis, 100(8): 1634-1642.
[68]	Yadav S, Anuradha G, Kumar R R, Vemireddy L R, Sudhakar R, Donempudi K, Venkata D, Jabeen F, Narasimhan Y K, Marathi B, Siddiq E A. 2015. Identification of QTLs and possible candidate genes conferring sheath blight resistance in rice (Oryza sativa L.). Springerplus, 4: 175.
[69]	Yan Y, Jia H H, Wang F, Wang C, Liu S C, Guo X Q. 2015. Overexpression of GhWRKY27a reduces tolerance to drought stress and resistance to Rhizoctonia solani infection in transgenic Nicotiana benthamiana. Front Physiol, 6: 265.
[70]	Yang J, Zhang F, Li J, Chen J P, Zhang H M. 2016. Integrative analysis of the microRNAome and transcriptome illuminates the response of susceptible rice plants to rice stripe virus. PLoS One, 11(1): e0146946.
[71]	Zhai J X, Zhang H, Arikit S, Huang K, Nan G L, Walbot V, Meyers B C. 2015. Spatiotemporally dynamic, cell-type-dependent premeiotic and meiotic phasiRNAs in maize anthers. Proc Natl Acad Sci USA, 112(10): 3146-3151.
[72]	Zhang N, Yang J W, Wang Z M, Wen Y K, Wang J, He W H, Liu B L, Si H J, Wang D. 2014. Identification of novel and conserved microRNAs related to drought stress in potato by deep sequencing. PLoS One, 9(4): e95489.
[73]	Zhao Y T, Wang M, Wang Z M, Fang R X, Wang X J, Jia Y T. 2015. Dynamic and coordinated expression changes of rice small RNAs in response to Xanthomonas oryzae pv. oryzae. J Genet Genom, 42(11): 625-637.
[74]	Zuo S M, Zhang L, Wang H, Yin Y J, Zhang Y F, Chen Z X, Ma Y Y, Pan X B. 2008. Prospect of the QTL-qSB-9Tq utilized in molecular breeding program of japonica rice against sheath blight. J Genet Genom, 35(8): 499-505.
[75]	Zuo S M, Wang Z B, Chen X J, Gu F, Zhang Y F, Chen Z X, Pan X B, Pan C H. 2009. Evaluation of resistance of a novel rice line YSBR1 to sheath blight. Acta Agron Sin, 35(4): 608-614. (in Chinese with English abstract)
[76]	Zuo S M, Yin Y J, Pan C H, Chen Z X, Zhang Y F, Gu S L, Zhu L H, Pan X B. 2013. Fine mapping ofqSB-11(LE), the QTL that confers partial resistance to rice sheath blight. Theor Appl Genet, 126(5): 1257-1272.
[77]	Zuo S M, Zhang Y F, Yin Y J, Li G Z, Zhang G W, Wang H, Chen Z X, Pan X B. 2014a. Fine-mapping of qSB-9(TQ), a gene conferring major quantitative resistance to rice sheath blight. Mol Breeding, 34(4): 2191-2203.
[78]	Zuo S M, Zhu Y J, Yin Y J, Wang H, Zhang Y F, Chen Z X, Gu S L, Pan X B. 2014b. Comparison and confirmation of quantitative trait loci conferring partial resistance to rice sheath blight on chromosome 9. Plant Dis, 98(7): 957-964.

Library	Total read	High quality		3′ adapter null		Insert null		5′ adapter contaminant		Smaller than 18 nt		polyA		Clean read
Library	Count	Count	%	Count	%	Count	%	Count	%	Count	%	Count	%		Count	%
XC5	32 799 952	32 287 444	100	159 708	0.49	14 595	0.05	30 643	0.09	3 035 094	9.40	360	0.00		29 047 044	89.96
XC10	31 289 810	30 793 396	100	186 452	0.61	4 137	0.05	18 544	0.06	2 056 543	6.68	232	0.00		28 517 488	92.61
XC20	28 181 536	27 748 448	100	121 045	0.44	26 104	0.09	27 422	0.10	3 140 979	11.32	289	0.00		24 432 609	88.05
XT5	27 616 282	27 182 187	100	148 168	0.55	22 745	0.08	15 564	0.06	1 554 941	5.72	341	0.00		25 440 428	93.59
XT10	28 651 787	28 266 164	100	249120	0.88	51 729	0.18	24 077	0.09	2 284 965	8.08	248	0.00		25 656 025	90.77
XT20	32 747 097	32 299 479	100	218 084	0.68	44 324	0.14	24 153	0.07	2 488 524	7.70	309	0.00		29 524 085	91.41
YC5	30 759 202	30 341 902	100	184 708	0.61	47 254	0.16	34 138	0.11	3 415 914	11.26	214	0.00		26 659 674	87.86
YC10	27 189 430	26 824 480	100	157 101	0.59	45 378	0.17	18 941	0.07	1 628 709	6.07	211	0.00		24 974 140	93.10
YC20	27 268 095	26 892 808	100	202 043	0.75	65 279	0.24	34 931	0.13	2 768 115	10.29	229	0.00		23 822 211	88.58
YT5	30 414 313	29 998 079	100	216 264	0.72	35 100	0.12	38 493	0.13	3 363 923	11.21	200	0.00		26 344 099	87.82
YT10	27 931 541	27 555 540	100	210 300	0.76	53 864	0.20	30 147	0.11	2 737 133	9.93	151	0.00		24 523 945	89.00
YT20	26 721 361	26 354 686	100	147 538	0.56	39 329	0.15	30 794	0.12	2 958 468	11.23	154	0.00		23 178 403	87.95
Total	351 570 406	319 720 133		2 200 531		449 838		327 847		31 433 308		2 938		312 120 151

Library	Total read	High quality		3′ adapter null		Insert null		5′ adapter contaminant		Smaller than 18 nt		polyA		Clean read
Library	Count	Count	%	Count	%	Count	%	Count	%	Count	%	Count	%		Count	%
XC5	32 799 952	32 287 444	100	159 708	0.49	14 595	0.05	30 643	0.09	3 035 094	9.40	360	0.00		29 047 044	89.96
XC10	31 289 810	30 793 396	100	186 452	0.61	4 137	0.05	18 544	0.06	2 056 543	6.68	232	0.00		28 517 488	92.61
XC20	28 181 536	27 748 448	100	121 045	0.44	26 104	0.09	27 422	0.10	3 140 979	11.32	289	0.00		24 432 609	88.05
XT5	27 616 282	27 182 187	100	148 168	0.55	22 745	0.08	15 564	0.06	1 554 941	5.72	341	0.00		25 440 428	93.59
XT10	28 651 787	28 266 164	100	249120	0.88	51 729	0.18	24 077	0.09	2 284 965	8.08	248	0.00		25 656 025	90.77
XT20	32 747 097	32 299 479	100	218 084	0.68	44 324	0.14	24 153	0.07	2 488 524	7.70	309	0.00		29 524 085	91.41
YC5	30 759 202	30 341 902	100	184 708	0.61	47 254	0.16	34 138	0.11	3 415 914	11.26	214	0.00		26 659 674	87.86
YC10	27 189 430	26 824 480	100	157 101	0.59	45 378	0.17	18 941	0.07	1 628 709	6.07	211	0.00		24 974 140	93.10
YC20	27 268 095	26 892 808	100	202 043	0.75	65 279	0.24	34 931	0.13	2 768 115	10.29	229	0.00		23 822 211	88.58
YT5	30 414 313	29 998 079	100	216 264	0.72	35 100	0.12	38 493	0.13	3 363 923	11.21	200	0.00		26 344 099	87.82
YT10	27 931 541	27 555 540	100	210 300	0.76	53 864	0.20	30 147	0.11	2 737 133	9.93	151	0.00		24 523 945	89.00
YT20	26 721 361	26 354 686	100	147 538	0.56	39 329	0.15	30 794	0.12	2 958 468	11.23	154	0.00		23 178 403	87.95
Total	351 570 406	319 720 133		2 200 531		449 838		327 847		31 433 308		2 938		312 120 151

Library	miRNA		rRNA		Repeat		snRNA		snoRNA		tRNA		Unannotation
XC5	7 078	0.17%	671 405	16.02%	2 141	0.05%	12 063	0.29%	9 575	0.23%	51 491	1.23%	2 607 435	62.23%
XC10	6 790	0.16%	702 101	16.37%	2 102	0.05%	13 433	0.31%	11 867	0.28%	47 947	1.12%	2 706 094	63.09%
XC20	6 654	0.16%	558 686	13.49%	2 040	0.05%	12 297	0.30%	10 539	0.25%	50 108	1.21%	2 719 682	65.68%
XT5	7 012	0.16%	583 595	13.55%	2 270	0.05%	12 214	0.28%	10 942	0.25%	43 689	1.01%	2 802 198	65.04%
XT10	6 384	0.16%	624 579	15.83%	1 873	0.05%	11 641	0.29%	9 409	0.24%	43 291	1.10%	2 517 937	63.80%
XT20	7 323	0.15%	679 675	13.74%	2 400	0.05%	13 356	0.27%	12 086	0.24%	55 485	1.12%	3 294 453	66.58%
YC5	5 997	0.15%	539 626	13.27%	2 250	0.06%	7 605	0.19%	9 924	0.24%	36 877	0.91%	2 800 361	68.88%
YC10	5 556	0.14%	576 120	14.31%	2 181	0.05%	7 888	0.20%	9 264	0.23%	36 218	0.90%	2 781 134	69.06%
YC20	4 890	0.15%	548 609	16.81%	1 770	0.05%	8 298	0.25%	9 517	0.29%	36 568	1.12%	2 098 698	64.30%
YT5	5 628	0.15%	597 566	16.43%	1 969	0.05%	8 058	0.22%	8 766	0.24%	37 700	1.04%	2 388 685	65.68%
YT10	5 351	0.15%	564 840	15.75%	1 921	0.05%	8 072	0.23%	9 744	0.27%	41 557	1.16%	2 379 169	66.34%
YT20	5 509	0.16%	487 539	14.12%	2 119	0.06%	7 464	0.22%	8 455	0.24%	29 764	0.86%	2 318 216	67.16%

Library	miRNA		rRNA		Repeat		snRNA		snoRNA		tRNA		Unannotation
XC5	7 078	0.17%	671 405	16.02%	2 141	0.05%	12 063	0.29%	9 575	0.23%	51 491	1.23%	2 607 435	62.23%
XC10	6 790	0.16%	702 101	16.37%	2 102	0.05%	13 433	0.31%	11 867	0.28%	47 947	1.12%	2 706 094	63.09%
XC20	6 654	0.16%	558 686	13.49%	2 040	0.05%	12 297	0.30%	10 539	0.25%	50 108	1.21%	2 719 682	65.68%
XT5	7 012	0.16%	583 595	13.55%	2 270	0.05%	12 214	0.28%	10 942	0.25%	43 689	1.01%	2 802 198	65.04%
XT10	6 384	0.16%	624 579	15.83%	1 873	0.05%	11 641	0.29%	9 409	0.24%	43 291	1.10%	2 517 937	63.80%
XT20	7 323	0.15%	679 675	13.74%	2 400	0.05%	13 356	0.27%	12 086	0.24%	55 485	1.12%	3 294 453	66.58%
YC5	5 997	0.15%	539 626	13.27%	2 250	0.06%	7 605	0.19%	9 924	0.24%	36 877	0.91%	2 800 361	68.88%
YC10	5 556	0.14%	576 120	14.31%	2 181	0.05%	7 888	0.20%	9 264	0.23%	36 218	0.90%	2 781 134	69.06%
YC20	4 890	0.15%	548 609	16.81%	1 770	0.05%	8 298	0.25%	9 517	0.29%	36 568	1.12%	2 098 698	64.30%
YT5	5 628	0.15%	597 566	16.43%	1 969	0.05%	8 058	0.22%	8 766	0.24%	37 700	1.04%	2 388 685	65.68%
YT10	5 351	0.15%	564 840	15.75%	1 921	0.05%	8 072	0.23%	9 744	0.27%	41 557	1.16%	2 379 169	66.34%
YT20	5 509	0.16%	487 539	14.12%	2 119	0.06%	7 464	0.22%	8 455	0.24%	29 764	0.86%	2 318 216	67.16%

Library	Known miRNA				Novel miRNA
Library	miRNA	miRNA-5P	miRNA-3P	Target gene number	miRNA	Target gene number
XC5	450	81	80	2 690	450	9 648
XC10	441	81	68	2 296	487	8 213
XC20	415	73	72	1 524	557	9 653
XT5	444	76	79	2 132	495	8 794
XT10	427	76	72	1 929	509	12 010
XT20	454	79	75	1 579	617	11 257
YC5	411	74	68	2 584	581	4 857
YC10	405	69	70	1 846	539	8 177
YC20	402	75	61	1 804	457	6 881
YT5	420	77	70	2 303	515	7 737
YT10	403	74	70	1 888	571	8 974
YT20	406	72	69	1 834	560	5 542