马铃薯幼苗应答高温胁迫的miRNA筛选与鉴定

doi:10.3864/j.issn.0578-1752.2025.22.003

摘要/Abstract

摘要：

【目的】马铃薯营养丰富，种植广泛，是一种重要的粮蔬兼用作物。为揭示马铃薯在苗期遭遇高温环境后对其生长发育的影响，筛选鉴定对高温胁迫特异应答的微小RNAs（miRNAs）及潜在的靶基因，为马铃薯耐热机制研究提供理论依据。【方法】以甘肃省农业科学院马铃薯研究所自主选育的1月龄陇薯7号组培苗为测试材料，在植物培养箱中分别设置正常温度（17 ℃）和高温（28 ℃）条件处理10 d后取样，进行小RNA测序（sRNA-seq）和转录组（RNA-seq）测序，在获得高质量测序数据的基础上，利用生物信息学方法分析筛选具有负调控关系的候选miRNA-靶基因对。用实时荧光定量PCR（RT-qPCR）和双荧光素酶试验（DLR）验证部分特异响应高温胁迫的miRNA-靶基因对。【结果】马铃薯幼苗在高温胁迫下，21和24 nt的miRNAs丰度均明显上升，并且所有miRNAs的表达量总体方差PC1为27.0%，而PC1与PC2占总体方差的39.4%。经miRNAs差异表达分析，共有100个miRNAs差异表达，其中62个上调表达，38个下调表达。在上调表达的miRNAs中，差异表达倍数介于1.02—6.94；下调表达的miRNAs中，差异表达倍数介于1.01—7.11。转录组分析表明，马铃薯幼苗在高温处理后，有579个差异表达基因上调，958个差异基因下调，这些基因主要参与响应生物胁迫、外部压力刺激和其他细胞组成及发育过程。同时，整合sRNA-seq和RNA-seq结果，从上调的miRNAs中得到13对候选miRNA-靶基因对，而从下调的miRNAs中得到5对候选miRNA-靶基因对。选择3对差异表达明显的调控关系（miRNA8051-Soltu.DM.10G026540、miRNA8051-Soltu.DM.10G026560和miR5072-Soltu. DM.04G010170）进行RT-qPCR验证，发现miRNAs与其潜在的靶基因呈相反表达趋势，这与生物信息学分析结果一致。同时，DLR试验证实miRNA8051对Soltu.DM.10G026540/Soltu.DM.10G026560均存在负调控作用。【结论】在高温胁迫下，马铃薯幼苗会以转录后调控的方式调控靶基因表达，从而对环境产生一定的适应性。这些候选基因可能参与转录因子、蜡质合成和甲基化表观调控等生物学过程。

关键词: 马铃薯, 高温胁迫, 小RNAs, 基因表达, 调控机制

Abstract:

【Objective】The potato is a nutrient-rich and widespread non-grain crop that can be eaten as a staple food or vegetable. To reveal the influence of high temperature stress on the growth and development of potato seedlings, this study screened and identified small RNAs (miRNAs) and potential target genes that specifically respond to heat stress, providing a theoretical basis for research on the thermotolerance mechanism in potatoes. 【Method】The experimental material used in this study was 1-month-old tissue-cultured seedlings of Longshu 7, a potato variety independently bred by the Potato Research Institute, Gansu Academy of Agricultural Sciences. After incubation in plant growth chamber under normal temperature (17 ℃) and high temperature (28 ℃) for 10 days, samples were collected and subjected to small RNA sequencing (sRNA-seq) and transcriptome sequencing (RNA-seq). Based on high-quality sequencing data, we used bioinformatics methods to analyze and screen candidate miRNA-target gene pairs exhibiting negative regulatory relationships. In addition, real-time quantitative PCR (RT-qPCR) and dual luciferase assays (DLR) were used to validate some miRNA-target pairs. 【Result】Under high temperature stress, the abundance of 21 and 24 nt miRNAs in potato seedlings increased significantly, and the overall variation of all miRNA expression levels was 27.0% for PC1, while PC1 and PC2 accounted for 39.4% of the overall variation. According to miRNAs differential expression analysis, a total of 100 miRNAs were obtained, including 62 upregulated and 38 downregulated. Among the upregulated miRNAs, the fold change ranged from 1.02 to 6.94; while the fold change ranged from 1.01 to 7.11 in the downregulated miRNAs list. Transcriptome analysis showed that 579 differentially expressed genes were upregulated and 958 differentially expressed genes were downregulated in potato seedlings under high temperature stress. These genes were mainly involved in responding to biotic stress, external stress stimuli, and other cellular components and developmental processes. Integration of sRNA-seq and RNA-seq analyses revealed that a total of 13 miRNA-target pairs were obtained in the up-regulated miRNAs, while 5 miRNA-target gene pairs were obtained in the down-regulated miRNAs. Then, we selected three pairs of differentially expressed regulatory relationships (miRNA8051-Soltu.DM.10G026540, miRNA8051- Soltu.DM.10G026560, and miR5072-Soltu.DM.04G010170) for RT-qPCR. The results revealed that miRNAs showed opposite expression trends with their potential target genes, consistent with the results of bioinformatics analysis. In addition, we analyzed the regulatory pair of miRNA8051-Soltu.DM.10G026540 or Soltu.DM.10G026560 using DLR assay, confirming that miRNA8051 has a negative regulatory effect on both Soltu.DM.10G026540 and Soltu.DM.10G026560. 【Conclusion】It was revealed that potato seedlings under high temperature stress would regulate the target genes expression in a post-transcriptional regulatory manner, thus producing certain adaptations to the adverse environment. These candidate genes may be involved in biological processes such as transcription factors, wax synthesis and epigenetic regulation of methylation.

Key words: potato, high temperature stress, miRNAs, gene expression, regulatory mechanism

丁宁, 齐恩芳, 贾小霞, 黄伟, 马丽荣, 李建武, 燕汝楠. 马铃薯幼苗应答高温胁迫的miRNA筛选与鉴定[J]. 中国农业科学, 2025, 58(22): 4589-4602.

DING Ning, QI EnFang, JIA XiaoXia, HUANG Wei, MA LiRong, LI JianWu, YAN RuNan. Screening and Identification of miRNAs in Potato Seedlings in Response to High Temperature Stress[J]. Scientia Agricultura Sinica, 2025, 58(22): 4589-4602.

0
/ / 推荐

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2025.22.003

https://www.chinaagrisci.com/CN/Y2025/V58/I22/4589

图/表 9

表1

表2

图1

表3

差异显著的miRNA列表"

miRNA名称 miRNA ID	miRNAs差异表达倍数 log₂(FC)	P值 P-value	miRNA名称 miRNA ID	miRNAs差异表达倍数 log₂(FC)	P值 P-value
novel-m0825-3p	6.94	0.00928592	novel-m0214-3p	1.20	0.040383
stu-miR8026	6.24	0.01507158	stu-miR7992-5p	1.19	0.009089
novel-m0760-5p	5.97	0.036320701	novel-m0449-3p	1.18	0.033509
novel-m0475-3p	3.85	0.022526637	novel-m0151-3p	1.18	0.000186
novel-m0499-5p	3.73	0.002185003	novel-m0152-3p	1.18	0.000181
novel-m0374-3p	3.53	0.023190991	novel-m0153-3p	1.18	0.000176
novel-m0409-3p	3.32	0.034725102	novel-m0311-5p	1.15	0.027759
novel-m0784-5p	3.02	0.027023613	novel-m0303-5p	1.13	0.026385
novel-m0430-3p	2.94	0.021921926	novel-m0207-3p	1.06	0.006632
novel-m0001-5p	2.76	1.02251E-11	novel-m0159-5p	1.06	0.005143
novel-m0771-3p	2.63	0.049871451	novel-m0071-5p	1.02	0.004477
novel-m0772-3p	2.63	0.049702986	novel-m0331-3p	1.02	0.029365
novel-m0364-3p	2.57	0.000123302	novel-m0023-3p	-1.01	1.07E-13
novel-m0583-3p	2.52	0.002455583	stu-miR408b-3p	-1.01	6.22E-13
novel-m0826-3p	2.44	0.048331723	miR5072-z	-1.07	5.04E-06
miR395-x	2.37	1.02044E-06	novel-m0183-3p	-1.08	4.48E-05
novel-m0730-5p	2.24	0.047301899	stu-miR166c-5p	-1.15	6.85E-11
miR8001-z	2.14	0.026716933	miR169-z	-1.16	0.014103
novel-m0429-5p	2.08	0.042388039	stu-miR408a-5p	-1.17	8.00E-12
novel-m0717-5p	2.05	0.008140195	novel-m0154-3p	-1.18	0.000177
novel-m0595-5p	1.98	0.027233458	miR164-z	-1.24	0.003203
novel-m0495-5p	1.97	0.01056079	miR530-x	-1.27	6.34E-09
miR5168-y	1.96	2.37146E-05	stu-miR390-5p	-1.28	5.92E-11
novel-m0090-3p	1.94	3.57593E-14	miR5059-z	-1.30	0.001737
novel-m0205-3p	1.79	0.000604281	stu-miR482b-5p	-1.34	2.51E-09
novel-m0100-5p	1.75	9.01756E-16	miR408-z	-1.44	1.80E-14
novel-m0372-3p	1.71	0.005205421	stu-miR482a-5p	-1.51	3.81E-08
novel-m0099-5p	1.71	4.71339E-15	stu-miR162a-5p	-1.59	6.05E-19
novel-m0272-5p	1.71	0.017362178	stu-miR162b-5p	-1.59	6.05E-19
novel-m0515-5p	1.66	0.009197853	miR408-y	-1.64	1.00E-26
novel-m0456-3p	1.65	0.042559707	novel-m0234-5p	-1.67	0.000637
novel-m0216-3p	1.57	0.00066235	miR408-x	-1.71	0.000356
novel-m0217-3p	1.57	0.000659518	miR159-x	-1.74	0.000291
stu-miR167c-3p	1.44	1.10788E-07	stu-miR171d-3p	-1.75	0.005512
novel-m0273-5p	1.41	0.004966411	novel-m0284-3p	-1.77	0.003115
novel-m0232-5p	1.39	2.99376E-07	novel-m0787-5p	-1.81	0.045229
novel-m0464-5p	1.38	0.030284811	miR162-z	-1.83	2.98E-06
novel-m0040-5p	1.36	1.57E-07	stu-miR169f-3p	-2.12	0.005891
novel-m0330-3p	1.33	0.042221297	miR9470-y	-2.20	6.10E-22
stu-miR156e	1.31	1.82E-08	novel-m0733-3p	-2.61	0.005626
stu-miR156g-3p	2.38	1.83E-08	novel-m0749-3p	-2.62	0.014988
stu-miR156h-5p	1.31	1.84E-08	miR477-z	-2.72	0.043248
stu-miR156i-5p	1.31	1.84E-08	novel-m0829-3p	-2.88	0.018293
stu-miR156j-5p	1.31	1.83E-08	novel-m0742-3p	-2.93	0.02383
stu-miR156k-5p	1.31	1.83E-08	novel-m0722-5p	-3.73	2.47E-07
miR157-x	1.30	0.010945	miR8739-z	-4.34	0.000957
novel-m0296-3p	1.27	0.030261	miR1091-z	-5.23	0.04115
novel-m0324-5p	1.22	0.026115	novel-m0682-3p	-5.74	0.028726
novel-m0325-5p	1.22	0.026033	novel-m0683-3p	-5.74	0.028642
novel-m0373-3p	1.21	0.012318	stu-miR8051-5p	-7.11	0.021868

表3

表4

图2

图3

表5

图4

参考文献 30

[1]	ZHAO C, LIU B, PIAO S L, WANG X H, LOBELL D B, HUANG Y, HUANG M T, YAO Y T, BASSU S, CIAIS P, et al. Temperature increase reduces global yields of major crops in four independent estimates. Proceedings of the National Academy of Sciences of the United States of America, 2017, 114(35): 9326-9331.
[2]	ADEKANMBI T, WANG X Q, BASHEER S, LIU S Q, YANG A L, CHENG H Y. Climate change impacts on global potato yields: A review. Environmental Research: Climate, 2024, 3(1): 012001.
[3]	DING N, ZHANG B L. microRNA production in Arabidopsis. Frontiers in Plant Science, 2023, 14: 1096772.
[4]	苏小雨, 谭政委, 李春明, 李磊, 鲁丹丹, 余永亮, 董薇, 安素妨, 杨青, 孙瑶, 等. 高温胁迫下芝麻全基因组甲基化差异及关联基因表达分析. 中国农业科学, 2024, 57(24): 4825-4838. doi: 10.3864/j.issn.0578-1752.2024.24.001.
	SU X Y, TAN Z W, LI C M, LI L, LU D D, YU Y L, DONG W, AN S F, YANG Q, SUN Y, et al. Analysis of genome-wide methylation differences and associated gene expression of sesame varieties under high temperature stress. Scientia Agricultura Sinica, 2024, 57(24): 4825-4838. doi: 10.3864/j.issn.0578-1752.2024.24.001. (in Chinese)
[5]	ZHANG Y Y, ZHOU Y, ZHU W M, LIU J Z, CHENG F. Non-coding RNAs fine-tune the balance between plant growth and abiotic stress tolerance. Frontiers in Plant Science, 2022, 13: 965745.
[6]	STIEF A, ALTMANN S, HOFFMANN K, PANT B D, SCHEIBLE W R, BÄURLE I. Arabidopsis miR156 regulates tolerance to recurring environmental stress through SPL transcription factors. The Plant Cell, 2014, 26(4): 1792-1807.
[7]	ÖZTÜRK GÖKÇE Z N, AKSOY E, BAKHSH A, DEMIREL U, ÇALIŞKAN S, ÇALIŞKAN M E. Combined drought and heat stresses trigger different sets of miRNAs in contrasting potato cultivars. Functional & Integrative Genomics, 2021, 21(3): 489-502.
[8]	HE M, LIU J, TAN J, JIAN Y Q, LIU J G, DUAN Y F, LI G C, JIN L P, XU J F. A comprehensive interaction network constructed using miRNAs and mRNAs provides new insights into potato tuberization under high temperatures. Plants, 2024, 13(7): 998.
[9]	ROBINSON M D, MCCARTHY D J, SMYTH G K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 2010, 26(1): 139-140.
[10]	YAN T, YOO D, BERARDINI T Z, MUELLER L A, WEEMS D C, WENG S, CHERRY J M, RHEE S Y. PatMatch: A program for finding patterns in peptide and nucleotide sequences. Nucleic Acids Research, 2005, 33(suppl_2): W262-W266.
[11]	KIM D, LANGMEAD B, SALZBERG S L. HISAT: A fast spliced aligner with low memory requirements. Nature Methods, 2015, 12(4): 357-360.
[12]	LI B, DEWEY C N. RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics, 2011, 12: 323.
[13]	GAO W W, LI M K, CHENG H P, XIA K F, ZHANG M Y. The miR5810/OsMRLP6 regulatory module affects rice seedling photosynthesis. The Crop Journal, 2023, 11(6): 1686-1695.
[14]	ZHANG G D, TANG R M, NIU S Y, SI H J, YANG Q, RAJORA O P, LI X Q. Heat-stress-induced sprouting and differential gene expression in growing potato tubers: Comparative transcriptomics with that induced by postharvest sprouting. Horticulture Research, 2021, 8(1): 226.
[15]	MUTHONI J, KABIRA J N. Potato production in the hot tropical areas of Africa: Progress made in breeding for heat tolerance. Journal of Agricultural Science, 2015, 7(9): 220-227.
[16]	LI Y, HE Y Z, QIN T, GUO X L, XU K, XU C X, YUAN W Y. Functional conservation and divergence of miR156 and miR529 during rice development. The Crop Journal, 2023, 11(3): 692-703.
[17]	YU N, NIU Q W, NG K H, CHUA N H. The role of miR156/SPLs modules in Arabidopsis lateral root development. The Plant Journal, 2015, 83(4): 673-685.
[18]	XU M L, HU T Q, ZHAO J F, PARK M Y, EARLEY K W, WU G, YANG L, POETHIG R S. Developmental functions of miR156-regulated Squamosa promoter binding protein-like (spl) genes in Arabidopsis thaliana. PLoS Genetics, 2016, 12(8): e1006263.
[19]	YUAN J, WANG X, QU S T, SHEN T, LI M J, ZHU L C. The roles of miR156 in abiotic and biotic stresses in plants. Plant Physiology and Biochemistry, 2023, 204: 108150.
[20]	MATTHEWS C, ARSHAD M, HANNOUFA A. Alfalfa response to heat stress is modulated by microRNA156. Physiologia Plantarum, 2019, 165(4): 830-842.
[21]	XIE Y R, ZHOU Q, ZHAO Y P, LI Q Q, LIU Y, MA M D, WANG B B, SHEN R X, ZHENG Z G, WANG H Y. FHY3 and FAR1 integrate light signals with the miR156-SPL module-mediated aging pathway to regulate Arabidopsis Flowering. Molecular Plant, 2020, 13(3): 483-498.
[22]	WANG H, ZHU S Y, YANG C, ZENG D Y, LUO C F, DAI C H, CHENG D Y, LV X H. Expression and functional identification of SPL6/7/9 genes under drought stress in sugarbeet seedlings. International Journal of Molecular Sciences, 2024, 25(16): 8989.
[23]	LUO H Y, YANG J W, LIU S Y, LI S G, SI H J, ZHANG N. Control of plant height and lateral root development via stu-miR156 regulation of SPL9 transcription factor in potato. Plants, 2024, 13(5): 723.
[24]	CHAO L M, LIU Y Q, CHEN D Y, XUE X Y, MAO Y B, CHEN X Y. Arabidopsis transcription factors SPL1 and SPL12 confer plant thermotolerance at reproductive stage. Molecular Plant, 2017, 10(5): 735-748.
[25]	GREER S, WEN M, BIRD D, WU X M, SAMUELS L, KUNST L, JETTER R. The cytochrome P450 enzyme CYP96A15 is the midchain alkane hydroxylase responsible for formation of secondary alcohols and ketones in stem cuticular wax of Arabidopsis. Plant Physiology, 2007, 145(3): 653-667.
[26]	NEGIN B, HEN-AVIVI S, ALMEKIAS-SIEGL E, SHACHAR L, JANDER G, AHARONI A. Tree tobacco (Nicotiana glauca) cuticular wax composition is essential for leaf retention during drought, facilitating a speedy recovery following rewatering. New Phytologist, 2023, 237(5): 1574-1589.
[27]	HE X J, HSU Y F, PONTES O, ZHU J H, LU J, BRESSAN R A, PIKAARD C, WANG C S, ZHU J K. NRPD4, a protein related to the RPB4 subunit of RNA polymerase II, is a component of RNA polymerases IV and V and is required for RNA-directed DNA methylation. Genes & Development, 2009, 23(3): 318-330.
[28]	DUTTA M, RATURI V, GAHLAUT V, KUMAR A, SHARMA P, VERMA V, GUPTA V K, SOOD S, ZINTA G. The interplay of DNA methyltransferases and demethylases with tuberization genes in potato (Solanum tuberosum L.) genotypes under high temperature. Frontiers in Plant Science, 2022, 13: 933740.
[29]	任茂, 李博, 徐延浩, 张文英. 高温胁迫诱导棉花甲基化变化分析. 分子植物育种, 2017, 15(3): 1069-1076.
	REN M, LI B, XU Y H, ZHANG W Y. Methylation-sensitive amplified polymorphism analysis of epigenetic changes in cotton (Gossypium hirsutum L.) under heat stress. Molecular Plant Breeding, 2017, 15(3): 1069-1076. (in Chinese)
[30]	宋爽, 刘宇, 高琪, 赵爽, 王守现, 宋庆港. 高温胁迫下香菇基因组甲基化差异分析. 中国食用菌, 2019, 38(10): 9-11, 16.
	SONG S, LIU Y, GAO Q, ZHAO S, WANG S X, SONG Q G. Analysis of genomic DNA methylation of Lentinula edodes under heat stress. Edible Fungi of China, 2019, 38(10): 9-11, 16. (in Chinese)

引物 Primer	正向引物 Forward primer (5′-3′)	反向引物 Reverse primer (5′-3′)
miR8051	GTGGACTGGAATCGGCAGTAT	CTCTCAGCTTCCACTTCTCCC
miR5072	GGTTAAAACCTAAGTAAGATTCCCC	TTTCGGTTTGTTCGAAATTGGGT
Soltu.DM.10G026540	TGATGTTGGCTGGGAAGGAC	GACCGCTTGGAAGGATGTCA
Soltu.DM.10G026560	GCGGGTCCAAGGACTTGTTT	TCAAAAGAGCTAAAGATTTGCCCA
Soltu.DM.04G010170	CAGCATGGCGTTTCTGATGG	CTGCCTTTTCTCAGCTTGCTC
EF1a-F	GATGAAATCGTGAAGGAAGTTTCTTC	CAGTCAAGGTTGGTAGACCT
pCAMBIA1300-miR8051	CTATTTACAATTACAGTCGAAAAAGGATCCGATGAAATCCA	GCTCCTCGCCCTTGCTCACCTCTCAGCTTCCACTTCTCCC
Soltu.DM.10G026540_Target	GGAGAGGACACGCTGAGGATCCATGGATTTTCTTGAAT	GTTTTTGGCGTCTTCCATTGTTTGATCCCTCCACGCTCG
Soltu.DM.10G026540_Target_MUT	AGAATGACCTTATTGTTCTATGTAATGGGGAGAAT	TAGAACAATAAGGTCATTCTTGTTTTTGGACTAACACGATG
Soltu.DM.10G026560_Target	GGAGAGGACACGCTGAGATGGATTTCCTTGAATATTCTC	GTTTTTGGCGTCTTCCATTTATATCTTTTTTACAACATTAAC

样本 Sample	纯净reads Clean reads	高质量reads High quality reads	纯净miRNAs Clean miRNAs	核糖体RNA丰度 rRNA abundance	胞质小RNA丰度 scRNA_ abundance	核小RNA丰度 snRNA_ abundance	核仁小RNA丰度 snoRNA_ abundance	转运RNA丰度 tRNA abundance
TCN-1	16392447 (100%)	16355263 (99.7732%)	15288010 (93.2625%)	3301996 (21.60%)	5671 (0.04%)	25450 (0.17%)	53877 (0.35%)	50606 (0.33%)
TCN-2	14459735 (100%)	14239278 (98.4754%)	13594436 (94.0158%)	2836799 (20.87%)	5398 (0.04%)	21904 (0.16%)	47287 (0.35%)	45777 (0.34%)
TCN-3	13314924 (100%)	12989482 (97.5558%)	12440010 (93.4291%)	2702969 (21.73%)	4754 (0.04%)	22063 (0.18%)	44538 (0.36%)	35753 (0.29%)
TCH-1	14459799 (100%)	14423958 (99.7521%)	13251235 (91.6419%)	1927858 (14.55%)	4535 (0.03%)	19379 (0.15%)	50906 (0.38%)	45752 (0.35%)
TCH-2	14618069 (100%)	14583155 (99.7612%)	10791142 (73.8206%)	1852233 (17.16%)	3781 (0.04%)	18999 (0.18%)	63230 (0.59%)	43899 (0.41%)
TCH-3	15497004 (100%)	15069982 (97.2445%)	14518489 (93.6858%)	2472050 (17.03%)	4907 (0.03%)	22620 (0.16%)	46271 (0.32%)	47418 (0.33%)

样本 Sample	原始data Raw data	纯净data Clean data	比对率 Unique mapped
TCN-1	36596200	36480366 (99.68%)	29898162 (82.19%)
TCN-2	50979834	50814196 (99.68%)	42957666 (84.83%)
TCN-3	50663170	50512048 (99.70%)	42795691 (85.03%)
TCH-1	53813724	53673574 (99.74%)	45085569 (84.38%)
TCH-2	51021820	50870562 (99.70%)	42776280 (84.36%)
TCH-3	44572552	44448784 (99.72%)	36244428 (81.83%)

差异表达miRNA differExp miRNAs	miRNAs名称 miRNAs ID	miRNAs差异表达倍数log₂(FC)	基因ID Gene ID	基因差异表达倍数 log₂(FC)
上调的miRNAs UP-regulated miRNAs	novel-m0499-5p	3.73	Soltu.DM.10G021470	-3.81
	novel-m0499-5p	3.73	Soltu.DM.10G021480	-3.24
	novel-m0001-5p	2.76	Soltu.DM.10G020770	-2.52
	novel-m0001-5p	2.76	Soltu.DM.10G020780	-1.77
	novel-m0583-3p	2.52	Soltu.DM.08G004390	-1.40
	miR395-x	2.37	Soltu.DM.03G009610	-1.22
	miR156e/h/i/j/k	1.31	Soltu.DM.03G028990	-1.34
	miR156g-3p	2.38	Soltu.DM.04G015140	-1.76
	miR156e/h/i/j/k	1.31	Soltu.DM.05G001040	-1.14
	miR156e/h/i/j/k	1.31	Soltu.DM.05G011910	-1.27
	miR156e/h/i/j/k	1.31	Soltu.DM.07G005930	-1.82
	miR156e/h/i/j/k	1.31	Soltu.DM.10G029290	-1.15
	miR156e/h/i/j/k	1.31	Soltu.DM.12G014160	-1.23
下调的miRNAs Down-regulated miRNAs	miR8051-5p	-7.11	Soltu.DM.10G026540	9.13
	miR8051-5p	-7.11	Soltu.DM.10G026560	7.23
	miR1091-z	-5.23	Soltu.DM.11G003400	1.63
	miR9470-y	-2.20	Soltu.DM.12G022800	1.16
	miR5072-z	-1.07	Soltu.DM.04G010170	1.05