Next Article in Journal
Molecular Evidence for Precursors of Sjögren’s Foci in Histologically Normal Lacrimal Glands
Previous Article in Journal
Physiological Analysis and Proteome Quantification of Alligator Weed Stems in Response to Potassium Deficiency Stress
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 20
Issue 1
10.3390/ijms20010222
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Integration Analysis of Small RNA and Degradome Sequencing Reveals MicroRNAs Responsive to Dickeya zeae in Resistant Rice

by
Wenqi Li
1,2,
Yulin Jia
3,
Fengquan Liu
4,
Fangquan Wang
1,2,
Fangjun Fan
1,2,
Jun Wang
1,2,
Jinyan Zhu
1,2,
Yang Xu
1,2,
Weigong Zhong
1,2 and
Jie Yang
1,2,*
1
Institute of Food Crops, Jiangsu Academy of Agricultural Sciences/Nanjing Branch of Chinese National Center for Rice Improvement, Nanjing 210014, China
2
Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China
3
United States Department of Agriculture-Agriculture Research Service, Dale Bumpers National Rice Research Center, Stuttgart, AR 72160, USA
4
Institute of Plant Protection, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(1), 222; https://doi.org/10.3390/ijms20010222
Submission received: 28 November 2018 / Revised: 26 December 2018 / Accepted: 31 December 2018 / Published: 8 January 2019
(This article belongs to the Section Molecular Plant Sciences)
Browse Figures
Versions Notes

Abstract

:
Rice foot rot disease caused by the pathogen Dickeya zeae (formerly known as Erwinia chrysanthemi pv. zeae), is a newly emerging damaging bacterial disease in China and the southeast of Asia, resulting in the loss of yield and grain quality. However, the genetic resistance mechanisms mediated by miRNAs to D. zeae are unclear in rice. In the present study, 652 miRNAs including osa-miR396f predicted to be involved in multiple defense responses to D. zeae were identified with RNA sequencing. A total of 79 differentially expressed miRNAs were detected under the criterion of normalized reads ≥10, including 51 known and 28 novel miRNAs. Degradome sequencing identified 799 targets predicted to be cleaved by 168 identified miRNAs. Among them, 29 differentially expressed miRNA and target pairs including miRNA396f-OsGRFs were identified by co-expression analysis. Overexpression of the osa-miR396f precursor in a susceptible rice variety showed enhanced resistance to D. zeae, coupled with significant accumulation of transcripts of osa-miR396f and reduction of its target the Growth-Regulating Factors (OsGRFs). Taken together, these findings suggest that miRNA and targets including miR396f-OsGRFs have a role in resisting the infections by bacteria D. zeae.

1. Introduction

Plant microRNAs (miRNAs) are non-protein coding RNAs with 21–24 nucleotides (nt). Most recently, miRNAs were demonstrated to be a critical component of sophisticated plant defense system [1,2,3]. Plants are known to use a multifaceted defense system to prevent infections by pathogenic microbes through a co-evolutionary mechanism with pathogens. Plant immunity is involved in two tiers of defenses where the first tier is a pathogen-associated molecular pattern (PAMP) trigged immunity (PTI) [4,5]. The second tier of defense is often involved in pathogen effector-triggered immunity (ETI) mediated mostly by R proteins with leucine rich repeat and nucleotide binding site (NLR) [4,5]. Activation of ETI often reinforces PTI to achieve plant immunity to prevent damages by invaders [6,7,8]. It was predicted that targets of plant miRNAs can be NLR genes, or genes involved in PTI [2,3]. To name a few, Li et al showed that two miRNA, nta-miR6019 and nta-miR6020 co-regulated the transcripts of R genes to regulate resistance responses and productivity in tobacco [9]. In Arabidopsis, bacterial flagellin flg22-induced miR393 promoted resistance response to virulent Pseudomonas syringae pv. tomato Pto DC3000 by inhibiting predicted target-auxin receptor transcripts [10]. Three bacterial flagellin flg22-induced miRNAs, miR160, miR398b and miR773 were shown to be involved in resistance responses via regulating PAMP-induced callose deposition in Arabidopsis [11,12]. The stress-inducible miR163 and its target histone deacetylase were demonstrated to confer resistance to P. s. pv. Tomato [13].
In rice, recent studies also showed that miRNAs were involved in disease resistance [14,15,16]. Examples are 23 miRNAs and 59 targets were predicted to be involved in resistance against rice sheath blight disease [17]. A group of miRNAs and their targets functioned in an incompatible interaction between rice and fungal pathogen Magnaporthe oryzae (M. oryzae) [18]. In addition, the large differentially expressed miRNAs and their putative target genes were involved in hormone biosynthesis and signaling pathways in rice after infections by bacterial pathogens [19,20]. The osa-miR169, miR160a, miR398b, miR7695 and miR1861k repressed the expression of the predicted targets respectively, have a role in resistance to M. oryzae or Xanthomonas oryzae pv. oryzae (Xoo) [18,21,22,23].
Rice foot rot disease caused by the pathogenic bacteria D. zeae, is a newly emerged destructive bacterial disease threatening rice yield and quality [24]. The occurrence of rice foot rot disease was first reported in Japan in 70 s of the 20th century [25]. Recently, field incidence of rice foot rot disease shows an increasing trend in rice planting areas in Southeast Asia and South China [24]. Little is known about the pathogenicity of D. zeae and the genetic resistance mechanism to D. zeae [26,27,28,29], and it is unknown if miRNAs are involved in the defense responses to D. zeae in rice. Uncovering the resistance mechanism will lead to developing better control methods to this disease.
We previously isolated and identified an isolate of D. zeae JS2012 from diseased roots of a rice variety [30]. The objectives of the present study were to (1) determine temporal transcriptome profiles of miRNAs after D. zeae infection, (2) predict miRNA targets by degradome sequencing, (3) validate differentially expressed miRNAs and targets by qRT-PCR, and (4) overexpress the osa-miR396f precursor to determine if enhanced resistance to D. zeae occurs in rice. Through these efforts we demonstrated that a set of candidate miRNAs and relevant targets including miR396 and predicted target may play an important role in the defense responses to D. zeae.

2. Results

2.1. Overview of miRNA Sequencing

A total of 4 miRNA libraries were constructed from a resistant rice variety (an incompatible interaction) at 0 h (K0), 6 h (K1), 12 h (K2) and 48 h (K3) after D. zeae inoculation. The illumina sequencings revealed 1,053,550 (K0), 1,000,514 (K1), 1,023,748 (K2), 483,076 (K3) unique clean reads respectively after removing low resolution, length less than 18 nt or greater than 25 nt, junk reads, mRNA, rRNA, tRNA, snRNA and snoRNA (Table 1). A total number of 19–24 nt from K0 miRNA library accounted for 82.82%, in which 21, 24 nt unique miRNA accounted for 16.10% and 11.03% respectively (Table S1). After inoculation with D. zeae in a resistant rice variety, the length of unique miRNAs was found between the range of 20–24 nt. Among them, 21 and 24 nt unique miRNAs accounted for 15.25% and 18.61% (K1), 17.15% and 14.83% (K2), 16.13% and 9.64%, respectively (K3; Table S1). Differences in miRNA length and number from different time points after pathogen inoculation may reflect different biological roles during different stages of rice growth and development.

2.2. Identification of Known and Novel miRNAs

A total of 652 miRNAs including 250 novel, 78 known, and 185 predicted but not mapped at the plant genome (potential novel miRNAs), and 139 mapped on the plant genome not in the miRNA database were identified (unknown miRNAs; Figure 1; Table S2). A total of 10 known miRNAs, osa-miR1425-5p, osa-miR156b-3p, osa-miR156c-3p, osa-miR171b, osa-miR156a, osa-miR535-5p, osa-miR162a, osa-miR166a-5p, osa-miR166a-3p and osa-miR166b-5p with more than 100 reads was detected (Figure 1; Table S2). Among them, three miRNA families, osa-miR156, 166 and 167, with the highest frequencies were identified (Figure 1; Table S2). The miRNAs mapped to the rice genome but did not match to known pre-miRNAs as novel miRNAs. 250 novel miRNAs were predicted in four libraries (Figure 1; Table S2). The majority of novel miRNAs was found to be slowly accumulated after inoculation with D. zeae. However, osa-MIR5083-p5 was the most abundant one with reads of 135.04 (K0), 484.00 (K1), 713.11 (K2) and 233.71 (K3) in four libraries, respectively, suggesting its unique role for responding to D. zeae infection in a resistant rice variety.

2.3. D. zeae-Responsive miRNAs in Resistant Rice

To identify miRNAs in resistant rice that respond to D. zeae, we analyzed the differential expressions of miRNAs in the four libraries with the normalized reads from deep sequencing. A total of 79 differentially expressed miRNAs was found in all four libraries including 51 known miRNAs and 28 novel miRNAs under the criterion of normalized reads ≥10 (Figure 2A; Table S3). Furthermore, 34 miRNAs were up-regulated and 32 miRNAs were down-regulated at six hours post inoculation (hpi). At 12 hpi, 36 miRNAs were up-regulated and 32 miRNAs were down-regulated. At 48 hpi, 26 miRNAs were up-regulated and 39 miRNAs were down-regulated (Figure 2B; Table S3). These results suggest that certain miRNAs were expressed at some time points after D. zeae infection in an incompatible interaction suggesting that they may play an important role during pathogen infection.
Sequence analysis revealed that there were 530 miRNAs in K0, 623 in K1, 631 in K2, and 474 in K3, respectively (Figure 3). When compared with K0, 165 unique miRNAs were identified at 6 hpi, (Figure 3A), 163 unique miRNAs were identified at 12 hpi, 94 unique miRNAs were detected at 48 hpi, respectively (Figure 3B,C). When compared among four libraries, 30 (K0), 44 (K1), 41 (K2) and 18 (K3) miRNAs were detected (Figure 3D). The presences of different miRNAs at different time points after inoculation suggest miRNAs have a role to D. zeae in an incompatible interaction.

2.4. Target Prediction of miRNAs by Degradome Sequencing and Analysis

To identify the relevant targets of miRNAs, high-throughput degradome sequencing was used and the data were analyzed with the CleavLand 3 pipeline. Degradome sequencing analysis showed that 10931903 and 8679632 raw reads were detected from the control (T0 h) and infected (T6–48 h) rice samples, respectively (Table S4). After removing the adaptor reads and mapping the rice transcriptome, 35890 and 32146 covered transcripts were identified in the control (T0 h) and infected (T6–48) rice samples, respectively (Table S4). Further, a total of 799 targets were predicted to be cleaved by the 168 identified miRNAs in an incompatible interaction (Table S5). Many transcript targets mediated by miRNAs were closely related to the signal transduction involved in plant immunity, such as PR5 (LOC_Os03g14030.1), leucine-rich repeat (LRR) kinase (LOC_Os12g10740.1), calcium-dependent kinase (LOC_Os07g22640.), WRKY transcription factor (LOC_Os04g51560.1), AUX/IAA transcriptional regulator (LOC_Os12g40900.1), ethylene-responsive binding protein (LOC_Os07g42510.1), growth-regulating factor 5 (LOC_Os02g53690.1) and etc (Table 2). These findings demonstrate that miRNAs-target pairs may be involved in resistance responses through multiple defense pathways in rice.

2.5. Functional Classification of Predicted Targets

A gene ontology (GO) analysis was used to classify the biochemical functions of predicted targets (Figure 4). A total of 858 transcript targets were predicted as protein kinase, 852 as transferase, 171 as signal transducers, and 81 as protein receptors, suggesting their potential involvements in signal recognition and transduction in plant immunity (Figure 4). Similarly, a group of targets was predicted to be involved in multiple molecular function and all of them were predicted to be located in diverse cellular components of cells, suggesting that predicted target genes may also play a role in biological processes including hormone stimulus, signal transducer, sequence-specific DNA binding, secondary metabolic process, cell growth, and multicellular organismal development (Figure 4).

2.6. Co-expression Analysis and Validation of miRNA-Targets by qRT-PCR

We analyzed the miRNAs obtained by miRNA sequencing, and verified the data with qRT-PCR and degradome sequencing. Firstly, 29 (36.71%) of 79 differentially expressed miRNAs detected by miRNA-seq were detected from the degradome sequencing (Table S6). A total of 82 genes resulting in 132 transcripts were predicted to be targets of these 29 miRNAs (Table S6). Moreover, 9 differentially expressed miRNAs based on the number of normalized reads were selected from 29 miRNAs (Table 3). Interestingly, we found that among 9 miRNAs, 3 miRNAs were induced and 6 were reduced (Table S2). Moreover, the secondary structures of 9 miRNAs precursor were predicted by RNAfold respectively (Figure 5A). Despite the difference in the fold changes, the expression patterns of 9 miRNAs analyzed by qRT-PCR were consistent with that of miRNA-seq (Figure 5B). Moreover, the degradome sequencing data revealed an opposite expression pattern of the cognate targets of these 9 miRNAs (Tables S2 and S6) suggesting that these miRNAs may regulate target gene expression at different time points post inoculation in an incompatible interaction. Reduced expressions of three target genes, LOC_Os06g02560.1, LOC_Os03g51970.1 and LOC_Os03g47140.1) predicted to be regulated by osa-miR396f were verified by qRT-PCR, suggesting that the osa-miR396f-targets pair may play a role for resistance to D. zeae in rice (Figure 6).

2.7. Overexpression of Osa-miR396f Precursor Enhanced Rice Resistance to D. zeae

To validate if osa-miR396f was involved in defense response to D. zeae, the precursor sequences of osa-miR396f were isolated from the resistant cultivar Nanjing 40 and were expressed under the control of the constitutive CMV35S promoter and transformed into susceptible japonica rice cultivar Nipponbare. Seven positive osa-miR396f precursor-overexpressing rice plants were inoculated with D. zeae. The transgenic rice plants with the osa-miR396f precursor showed increased resistance to D. zeae in comparison with that of Nipponbare (Figure 7A). Consistently, the transcripts of osa-miR396f precursor in all seven independent transgenic lines were higher than that in wild-type plants (Figure 7B). As predicted, transcripts of all three targets OsGRFs of osa-miR396f were significantly reduced in these transgenics revealed by qRT-PCR (Figure 7C). These results suggest that miRNA396f is involved in resisting D. zeae infection in rice.

3. Discussion

Recently, rice bacterial foot rot disease caused by D. zeae has the potential to become one of the most important bacterial diseases, resulting in the loss of yield and grain quality in China, in the southeast of Asia and worldwide. In the present study we identified and characterized a number of miRNAs-target pairs involved in defense responses in rice by miRNA, degradome sequencing and qRT-PCR.
The fast evolved high-throughput sequencing technology allows rapid identification of miRNAs and targets in plants that are involved in plant immunity [3,31]. Accumulated studies suggest that miRNAs played an important role for regulating the biological process and stress responses in plants [32,33]. It is known that plant miRNAs expressed differently during the infection process of pathogens [7]. In the present study, we found 652 miRNAs including 79 significantly differentially expressed miRNAs at different time points post inoculation in an incompatible interaction (Figure 2A; Table S3). For instance, two miRNAs miR827n-5p and miR164 we identified were also identified in a resistant rice variety to M. grisea, as negative and positive regulator respectively [21]. Moreover, the expression levels of seven miRNAs, miR156a, 159b, 166e-3p, 394, 396c-3p, 812 and 827 were decreased in rice in resistance response to Xoo [20]. We showed a similar responsive mode of these miRNAs to fungal and bacterial pathogens infection demonstrating that miRNAs were indeed involved in resistance response to D. zeae in rice. We also validated the expression profiles of nine miRNAs (miR2118, 393, 396, 166, 171, 156, 535, 159 and 5072) related to defense responses by qRT-PCR (Figure 5). The relevant targets of these nine miRNAs were also identified and verified by degradome sequencing and qRT-PCR (Table S6; Figure 6). These 9 miRNAs were previously shown to respond to the pathogens infection in an incompatible interaction, probably resulting in the activation of multiple defense responses [34,35,36,37,38]. Our data suggest that these nine differentially expressed miRNAs may be involved in regulating the resistance to D. zeae in rice.
In the present study, we showed that overexpression of osa-miR396f precursor in rice enhanced resistance to D. zeae, correlated with significant reduction of transcripts of three predicted targets OsGRFs (Figure 7). Therefore, miR396 may modulate resistance to D. zeae by cleaving the transcripts of its targets OsGRFs. Rice miR396 family has eight members, osa-miR396a, 396b, 396c, 396d, 396e, 396f, 396g and 396h in the miRNA database (miRBase, http://www.mirbase.org/, 27 March 2018). Rice miR396-GRFs pair was previously shown to regulate plant growth and development by positively activating plant hormones indole-3-acetic acid (IAA), gibberellin (GA) and brassinolide biosynthesis pathways in rice [39,40,41,42]. The miR396 family members, osa-miR396e-3p and osa-miR396d/e-5p were induced by blast pathogen in a susceptible rice variety, and the expression level of osa-miR396c-5p was increased in a resistant rice variety [21]. Some members of miR396 were previously predicted to be a negative factor for the infection process of southern rice black-streaked dwarf virus (SRBSDV) in rice [38]. In contrast, overexpression of miR396a or miR396b significantly compromised the susceptibility to cyst Nematode in Arabidopsis [43]. The Growth-Regulating Factors (GRFs) were also the targets of miR396 family members and positively regulated the number and size of the rice grain [44]. The blocking of OsGRFs by miR396 generated a larger grain size, auxiliary branches and spikelets then enhanced grain yield through auxin in rice [41,45,46]. We speculate that miR396-OsGRFs pair may play a role for regulating the balance between yield and disease resistance via the auxin signaling pathway. However, the biological functions of miR396 on defense responses and rice grain development still need to be explored.
Plant miRNAs were predicted to be involved in the biological process and stress responses by inhibiting the transcripts of relevant targets [1,13,33]. In the present study, 132 miRNA responsive to the infection of D. zeae in an incompatible interaction were identified, and the predicted targets of 29 differentially expressed miRNAs were identified by degradome sequencing (Table S6). These identified targets might modulate rice resistance response to D. zeae. Most of the targets we identified were predicted to be involved in multiple defense response signal pathways such as OsGRFs, Leucine-rich repeat protein kinase, MYB domain protein, and auxin efflux carrier family protein (Table 2 and Table 3; Table S6). The genes, AtGRF1 and AtGRF3 were shown to be involved in multiple resistance-related signal pathways related with cell-wall modification, cytokinin biosynthesis and the accumulation of secondary metabolites in Arabidopsis [43,47]. Overexpression of miR396a or miR396b and miR396 targets grf1/grf2/grf3 triple mutants previously significantly compromised the susceptibility to cyst nematode in Arabidopsis [43]. The miR396-GRF6 interaction network was predicted to be involved in the development of inflorescence architecture by regulating the auxin biosynthesis and signaling pathways [41]. Moreover, the auxin-responsive GH3 family gene LOC_Os07g40290.1 as the target of osa-miR172d-5p_R-2 showed more cleavages in a resistant rice variety after inoculation with D. zeae, compared with that without inoculation (Table 2). Additionally, auxin as a negative regulator was shown to contribute to resistance to bacteria pathogen Xoo, which resulted in the change of the cell wall structure [45,46]. The Mybs1 gene encoding one MYB transcription factor was involved in a broad-spectrum resistance against M. oryzae in rice by increasing the accumulation of H2O2 through inhibition of the expression level of bsr-d1 [48]. Taken together, these findings suggest that miRNA and targets are involved in multiple defense responses, including auxin and active oxygen signal pathways.
In summary, knowledge of miRNAs and targets opens a new avenue to understand the complexity of plant immunity. In the present study, a group of differentially expressed miRNAs and targets in an incompatible interaction at early interphases of host-pathogen interaction was identified by miRNA and degradome sequencing and qRT-PCR. These miRNAs and targets were predicted to be involved in multiple defense signal and diverse cellular pathways in rice. Through a transgenic approach, we demonstrated that one of these miRNAs, osa-miR396f enhanced the defense responses against D. zeae by inhibiting the transcripts of the three growth related transcription factor genes, OsGRFs. These results not only pave the road for controlling the newly emerging damaging foot rot disease but also promise a better understanding of plant innate immunity.

4. Materials and Methods

4.1. Plant Materials and Measurement of Resistance Reactions to D. zeae

The foot rot disease resistant Japonica rice variety Nanjing 40 was used to analyze the candidate miRNAs involved in disease resistance and the relevant targets by high-through sequencing technologies. The individual positive rice lines overexpressing the osa-miR396f precursor were chosen for analyzing disease reactions to D. zeae. All the plants used in this study were grown in a greenhouse in Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu Province, China. Disease reactions to D. zeae were measured by the basal stem and root inoculation methods, respectively [30,49]. Disease reaction was classified as five groups by measuring the percentage of diseased area at 7–10 days after inoculation. No visible disease symptom in the stem and foot indicates immunity. The disease index less than or equal to five indicates highly resistance (HR). The disease index 5.1 to 12.4 indicates moderate resistance (MR). The disease index, 12.5 to 19.9 indicates moderate susceptibility (MS). The disease index greater than or equal to 20 indicates highly susceptibility (HS) [30,49].

4.2. Construction of Small RNA Library, Sequencing and Data Analysis

Total RNA was extracted from four mixed rice roots at 0 (K0), 6 (K1), 12 (K2) and 48 (K3) hpi with D. zeae using Trizol reagent (Invitrogen, Shanghai, China) following the manufacturer’s procedure respectively. The rice roots sampled with two biological repeats were combined as K0, K1, K2 and K3 for total RNA extraction, small RNA library construction and sequencing. Approximately 1 mg of total RNAs was used to prepare the miRNA library according to the protocol of TruSeq Small RNA Sample Prep Kits (Illumina, San Diego, CA, USA). The libraries were sequenced with the single-end sequencing (36 bp) on an Illumina Hiseq2500 at the LC-Bio Co., Ltd (Hangzhou, China) following the vendor’s recommended protocol.
The raw reads were analyzed with the Illumina pipeline filter (Solexa 0.3), and the dataset was further processed with an in-house program, ACGT101-miR (LC Sciences, Houston, TX, USA) to remove adapter dimers, junk, low complexity, common RNA families (rRNA, tRNA, snRNA, snoRNA) and repeats. Subsequently, unique sequences with length in 18–25 nucleotides were mapped to specific species precursors in the miRBase database (http://www.mirbase.org/, 27 March 2018) using the BLAST algorithm to identify known miRNAs and novel 3p and 5p-derived miRNAs. The unique sequences mapping to specific species mature miRNAs in hairpin arms were identified as known miRNAs; the unique sequences mapping to the other arm of known specific species precursor hairpin opposite to the annotated mature miRNA-containing arm were considered to be novel 5p or 3p-derived miRNA candidates [50,51].

4.3. Identification and Function Analysis of Targets of miRNAs

The total RNA extracted from control T0h (0 hpi) and infected T6–48h (mixtures of 6, 12, 48 hpi) rice sample respectively were used for degradome sequencing on Illumina Hiseq 2500 at the LC-Bio Co., Ltd. (Hangzhou, China). The degradome reads were mapped to the rice transcriptome data. Publicly available software packages, CleaveL and 3.0 pipeline was used to predict and identify the potentially cleaved targets [50,52]. Then, the computational target prediction algorithms (Target Finder) were used to identify miRNA binding sites [53]. All of the predicted target genes were analyzed with NCBI BLASTX algorithm. Finally, the GO terms of these differentially expressed miRNA targets were also annotated by AgriGO program. The candidate miRNAs-related to defense responses and their targets with differential expression based on normalized deep-sequencing counts were analyzed by the Fisher exact test, Chi-squared test, Student’s t test, and ANOVA based on the experiments design [54]. The level of significance threshold was set at 0.01 and 0.05 for each test.

4.4. Validation of Differentially Expressed miRNAs and Targets by qRT-PCR

To validate miRNAs and their target expressions determined by high-through sequencing technology, the root fragments at different time points after inoculation were used to extract total RNA using the Trizol reagent (TaKaRa, Dalian, China). The qRT-PCR analysis was performed with the Applied Biosystems 7500 Real Time PCR System and SYBR Premix Ex TaqTM (TaKaRa, Dalian, China) according to the manufacturer’s instructions. The rice gene EF1-a and U6 was used as the internal reference gene to standardize RNA quantity for evaluating relative expression levels. For qRT-PCR assays, three independent biological samples were carried out with three technical replicates with a gene-specific primer (Table S7).

4.5. Vector Construction and Rice Transformation

The 176nt precursor sequence of osa-miR396f was isolated from Nanjing 40 by PCR amplification, and inserted into the pCAMBIA1301 binary vector driving by the constitutive CMV35s promoter for overexpression. The T-DNA recombinant plasmids were transformed into calli derived from the mature Nipponbare embryos by the Agrobacterium mediated transformation method [46].

Supplementary Materials

Supplementary materials can be found at https://www.mdpi.com/1422-0067/20/1/222/s1.

Author Contributions

Conceived and designed the experiments: W.L., Y.J., F.L., W.Z. and J.Y. Performed the experiments: W.L., F.W. and F.F. Analyzed the data: J.W. and J.Z. Contributed reagents/materials/analysis tools: Y.X. Wrote the paper: W.L., J.Y. and Y.J.

Funding

This work was supported by the National Key Research and Development Program (No. 2017YFD0100403), Genetically Modified Organisms Breeding Major Projects of P.R. China (2018ZX0801002B-003), the Jiangsu Key Laboratory of Agrobiology Program (No. 49114042015Z006), the Jiangsu Province Key Research and Development Program (Modern Agriculture) Project (No. BE2017368), the Exploratory Project of the Jiangsu Academy of Agricultural Sciences (No. ZX(17)2014), and the Jiangsu Province Natural Science Foundation (No. BK20171326). The United States Department of Agriculture (USDA) is an equal opportunity provider and employer

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Navarro, L.; Dunoyer, P.; Jay, F.; Arnold, B.; Dharmasiri, N.; Estelle, M.; Voinnet, O.; Jones, J.D. A plant miRNA contributes to antibacterial resistance by repressing auxin signaling. Science 2006, 312, 436–439. [Google Scholar] [CrossRef] [PubMed]
  2. Omidbakhshfard, M.A.; Proost, S.; Fujikura, U.; Mueller-Roeber, B. Growth-Regulating Factors (GRFs): A small transcription factor family with important functions in plant biology. Mol. Plant 2015, 16, 998–1010. [Google Scholar] [CrossRef] [PubMed]
  3. Baldrich, P.; San, S.B. MicroRNAs in rice innate immunity. Rice 2016, 9, 6. [Google Scholar] [CrossRef] [PubMed]
  4. Jones, J.D.; Dangl, J.L. The plant immune system. Nature 2006, 444, 323–329. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Bellincampi, D.; Cervone, F.; Lionetti, V. Plant cell wall dynamics and wall-related susceptibility in plant-pathogen interactions. Front Plant Sci. 2014, 5, 228. [Google Scholar] [CrossRef]
  6. Guo, W.; Wang, C.; Zuo, Z.L.; Qiu, J.L. The roles of anion channels in Arabidopsis immunity. Plant Signal Behav. 2014, 9, e29230. [Google Scholar] [CrossRef]
  7. Liu, W.D.; Liu, J.; Triplett, L.; Leach, J.E.; Wang, G.L. Novel insights into rice innate immunity against bacterial and fungal pathogens. Annu. Rev. Phytopathol. 2014, 52, 213–241. [Google Scholar] [CrossRef]
  8. Gao, Q.M.; Zhu, S.; Kachroo, P.; Kachroo, A. Signal regulators of systemic acquired resistance. Front Plant Sci. 2015, 6, 228. [Google Scholar] [CrossRef]
  9. Li, F.; Pignatta, D.; Bendix, C.; Brunkard, J.O.; Cohn, M.M.; Tung, J.; Sun, H.; Kumar, P.; Baker, B. MicroRNA regulation of plant innate immune receptors. Proc. Natl. Acad. Sci. USA 2012, 109, 1790–1795. [Google Scholar] [CrossRef] [Green Version]
  10. Jones-Rhoades, M.W.; Bartel, D.P. Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol. Cell 2004, 14, 787–799. [Google Scholar] [CrossRef]
  11. Li, Y.; Zhang, Q.Q.; Zhang, J.G.; Wu, L.; Qi, Y.J.; Zhou, J.M. Identification of microRNAs involved in pathogen-associated molecular pattern-triggered plant innate immunity. Plant Physiol. 2010, 152, 2222–2231. [Google Scholar] [CrossRef] [PubMed]
  12. Pumplin, N.; Voinnet, O. RNA silencing suppression by plant pathogens: Defence, counter-defence and counter-counter-defence. Nat. Rev. Microbiol. 2013, 11, 745–760. [Google Scholar] [CrossRef] [PubMed]
  13. Chow, H.T.; Ng, D.W. Regulation of miR163 and its targets in defense against Pseudomonas syringae in Arabidopsis thaliana. Sci. Rep. 2017, 7, 46433. [Google Scholar] [CrossRef] [PubMed]
  14. Eckardt, N.A. A microRNA cascade in plant defense. Plant Cell 2012, 24, 840. [Google Scholar] [CrossRef] [PubMed]
  15. Maudet, C.; Mano, M.; Eulalio, A. MicroRNAs in the interaction between host and bacterial pathogens. FEBS Lett. 2014, 588, 4140–4147. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Yang, L.; Huang, H. Roles of small RNAs in plant disease resistance. J. Integr. Plant Biol. 2014, 56, 962–970. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. 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.; et al. Comprehensive analysis of microRNA-Seq and target mRNAs of rice sheath blight pathogen provides new insights into pathogenic regulatory mechanisms. DNA Res. 2016, 23, 415–425. [Google Scholar] [CrossRef] [PubMed]
  18. Campo, S.; Peris-Peris, C.; Siré, C.; Moreno, A.B.; Donaire, L.; Zytnicki, M.; Notredame, C.; Llave, C.; San, S.B. 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. 2013, 199, 212–227. [Google Scholar] [CrossRef]
  19. Zhang, W.X.; Gao, S.; Zhou, X.; Chellappan, P.; Chen, Z.; Zhou, X.F.; Zhang, X.M.; Fromuth, N.; Coutino, G.; Coffey, M.; et al. Bacteria-responsive microRNAs regulate plant innate immunity by modulating plant hormone networks. Plant Mol. Biol. 2011, 75, 93–105. [Google Scholar] [CrossRef]
  20. Hong, H.M.; Liu, Y.Y.; Zhang, H.T.; Xiao, J.H.; Li, X.H.; Wang, S.P. Small RNAs and gene network in a durable disease resistance gene-mediated defense responses in rice. PLoS ONE 2015, 10, e0137360. [Google Scholar] [CrossRef]
  21. Li, Y.; Lu, Y.G.; Shi, Y.; Wu, L.; Xu, Y.J.; Huang, F.; Guo, X.Y.; Zhang, Y.; Fan, J.; Zhao, J.Q.; et al. Multiple rice microRNAs are involved in immunity against the blast fungus Magnaporthe oryzae. Plant Physiol. 2014, 164, 1077–1092. [Google Scholar] [CrossRef] [PubMed]
  22. Zhang, S.; Mei, J.; Wang, T.; Wang, C.C.; Zhang, W.L.; Yang, L. Identification and expression analysis of OsmiR1861k in rice leaves in response to Xanthomonas oryzae pv. oryzae. J. Gen. Plant Pathol. 2015, 81, 108–117. [Google Scholar] [CrossRef]
  23. Li, Y.; Zhao, S.L.; Li, J.L.; Hu, X.H.; Wang, H.; Cao, X.L.; Xu, Y.J.; Zhao, Z.X.; Xiao, Z.Y.; Yang, N.; et al. Osa-miR169 negatively regulates rice immunity against the blast fungus Magnaporthe oryzae. Front. Plant Sci. 2017, 17, 2. [Google Scholar] [CrossRef] [PubMed]
  24. Liu, Q.G.; Zhang, Q.; Wei, C.D. Advances in research of rice bacterial foot rot. Sci. Agric. Sin. 2013, 46, 2923–2931. [Google Scholar]
  25. Goto, M. Bacterial foot rot of rice caused by a strain of Erwinia chrysanthemi. Phytopathology 1979, 69, 213–216. [Google Scholar] [CrossRef]
  26. Yang, S.; Peng, Q.; Francisco, M.S.; Wang, Y.; Zeng, Q.; Yang, C.H. Type III secretion system genes of Dickeya dadantii 3937 are induced by plant phenolic acids. PLoS ONE 2008, 3, e2973. [Google Scholar] [CrossRef]
  27. Yang, S.; Peng, Q.; Zhang, Q.; Yi, X.; JaeChoi, C.; Reedy, R.M.; Charkowski, A.O.; Yang, C.H.; et al. Dynamic regulation of GacA in type III secretion, pectinase gene expression, pellicle formation, and pathogenicity of Dickeya dadantii (Erwinia chrysanthemi 3937). Mol. Plant-Microbe Interact. 2008, 21, 133–142. [Google Scholar] [CrossRef]
  28. Zhou, J.N.; Zhang, H.B.; Wu, J.; Liu, Q.G.; Xi, P.G.; Lee, J.; Liao, J.L.; Jiang, Z.D.; Zhang, L.H.; et al. A novel multidomain polyketide synthase is essential for zeamine antibiotics production and the virulence of Dickeya zeae. Mol. Plant-Microbe Interact. 2011, 24, 1156–1164. [Google Scholar] [CrossRef]
  29. Chen, X.F.; Wei, C.D.; Zhang, Q.; Liu, Q.G. Functional analysis of HrpX/HrpY in Dickeya zeae virulence. Sci. Agric. Sin. 2014, 47, 675–684. [Google Scholar]
  30. Li, W.Q.; Wang, J.; Fan, F.J.; Zhu, J.Y.; Wang, F.Q.; Yang, J.; Zhong, W.G. The isolation of rice bacterial foot rot disease pathogen in Jiangsu Province and identification of resistant rice varieties. Jiangsu Agric. Sci. 2014, 42, 139–142. [Google Scholar]
  31. Padmanabhan, C.; Zhang, X.M.; Jin, H.L. Host small RNAs are big contributors to plant innate immunity. Curr. Opin. Plant Biol. 2009, 12, 465–472. [Google Scholar] [CrossRef] [PubMed]
  32. Sunkar, R.; Girke, T.; Jain, P.K.; Zhu, J.K. Cloning and characterization of microRNAs from rice. Plant Cell 2005, 17, 1397–1411. [Google Scholar] [CrossRef] [PubMed]
  33. Li, S.J.; Castillo-González, C.; Yu, B.; Zhang, X.R. The functions of plant small RNAs in development and in stress responses. Plant J. 2017, 90, 654–670. [Google Scholar] [CrossRef] [PubMed]
  34. Xin, M.M.; Wang, Y.; Yao, Y.Y.; Xie, C.J.; Peng, H.R.; Ni, Z.F.; Sun, Q.X. Diverse set of microRNAs are responsive to powdery mildew infection and heat stress in wheat (Triticum aestivum L.). BMC Plant Biol. 2010, 10, 123. [Google Scholar] [CrossRef] [PubMed]
  35. Chen, L.; Ren, Y.Y.; Zhang, Y.Y.; Xu, J.C.; Zhang, Z.Y.; Wang, Y.W. Genome-wide profiling of novel and conserved Populus microRNAs involved in pathogen stress response by deep sequencing. Planta 2012, 235, 873–883. [Google Scholar] [CrossRef]
  36. Gupta, O.P.; Permar, V.; Koundal, V.; Singh, U.D.; Praveen, S. MicroRNA regulated defense responses in Triticum aestivum L. during Puccinia graminis f.sp. tritici infection. Mol. Biol. Rep. 2012, 39, 817–824. [Google Scholar] [CrossRef]
  37. Yin, Z.J.; Li, Y.; Han, X.L.; Shen, F.F. Genome-wide profiling of miRNAs and other small non-coding RNAs in the Verticillium dahliae-inoculated cotton roots. PLoS ONE 2012, 7, e35765. [Google Scholar] [CrossRef]
  38. Xu, D.G.; Mou, G.P.; Wang, K.; Zhou, G.H. MicroRNAs responding to southern rice black-streaked dwarf virus infection and their target genes associated with symptom development in rice. Virus Res. 2014, 190, 60–68. [Google Scholar] [CrossRef]
  39. Choi, D.; Kim, J.H.; Kende, H. Whole genome analysis of the Os-GRF gene family encoding plant-specific putative transcription activators in rice (Oryza sativa L.). Plant Cell Physiol. 2004, 45, 897–904. [Google Scholar] [CrossRef]
  40. Duan, P.G.; Ni, S.; Wang, J.M.; Zhang, B.L.; Xu, R.; Wang, Y.X.; Chen, H.Q.; Zhu, X.D.; Li, Y.H. Regulation of OsGRF4 by OsmiR396 controls grain size and yield in rice. Nat. Plants 2015, 2, 15203. [Google Scholar] [CrossRef]
  41. Gao, F.; Wang, K.; Liu, Y.; Chen, Y.P.; Chen, P.; Shi, Z.Y.; Luo, J.; Jiang, D.Q.; Fan, F.F.; Zhu, Y.G.; et al. Blocking miR396 increases rice yield by shaping inflorescence architecture. Nat. Plants 2015, 2, 15196. [Google Scholar] [CrossRef]
  42. Hu, J.; Wang, Y.X.; Fang, Y.X.; Zeng, L.J.; Xu, J.; Yu, H.P.; Shi, Z.Y.; Pan, J.J.; Zhang, D.; Kang, S.J.; et al. A rare allele of GS2 enhances grain size and grain yield in rice. Mol. Plant 2015, 8, 1455–1465. [Google Scholar] [CrossRef] [PubMed]
  43. Hewezi, T.; Maier, T.R.; Nettleton, D.; Baum, T.J. The Arabidopsis microRNA396-GRF1/GRF3 regulatory module acts as a developmental regulator in the reprogramming of root cells during cyst nematode infection. Plant Physiol. 2012, 159, 321–335. [Google Scholar] [CrossRef]
  44. Debernardi, J.M.; Rodriguez, R.E.; Mecchia, M.A.; Palatnik, J.F. Functional specialization of the plant miR396 regulatory network through distinct microRNA-target interactions. PLoS Genet. 2012, 8, e1002419. [Google Scholar] [CrossRef] [PubMed]
  45. Ding, X.H.; Cao, Y.L.; Huang, L.L.; Zhao, J.; Xu, C.G.; Li, X.H.; Wang, S.P. Activation of the indole-3-acetic acid-amido synthetase GH3-8 suppresses expansin expression and promotes salicylate- and jasmonate-independent basal immunity in rice. Plant Cell 2008, 20, 228–240. [Google Scholar] [CrossRef] [PubMed]
  46. Li, W.Q.; Wang, F.Q.; Wang, J.; Fan, F.J.; Zhu, J.Y.; Yang, J.; Liu, F.Q.; Zhong, W.G. Overexpressing CYP71Z2 enhances resistance to bacterial blight by suppressing auxin biosynthesis in rice. PLoS ONE 2015, 10, e0119867. [Google Scholar] [CrossRef] [PubMed]
  47. Liu, J.Y.; Rice, J.H.; Chen, N.N.; Baum, T.J.; Hewezi, T. Synchronization of developmental processes and defense signaling by growth regulating transcription factors. PLoS ONE 2014, 9, e98477. [Google Scholar] [CrossRef] [PubMed]
  48. Li, W.T.; Zhu, Z.W.; Chern, M.S.; Yin, J.J.; Yang, C.; Ran, L.; Cheng, M.P.; Wang, K.; Wang, J.; Zhou, X.G.; et al. A natural allele of a transcription factor in rice broad-spectrum blast resistance. Cell 2017, 170, 114–126. [Google Scholar] [CrossRef]
  49. Pu, X.M.; Zhou, J.N.; Lin, B.R.; Shen, H.F. First report of bacterial foot rot of rice caused by a Dickeya zeae in China. Plant Dis. 2012, 96, 1818. [Google Scholar] [CrossRef]
  50. Wu, L.; Zhang, Q.Q.; Zhou, H.Y.; Ni, F.R.; Wu, X.Y.; Qi, Y.J. Rice microRNA effector complexes and targets. Plant Cell 2009, 213, 3421–3435. [Google Scholar] [CrossRef]
  51. Taylor, R.S.; Tarver, J.E.; Hiscock, S.J.; Donoghue, P.C. Evolutionary history of plant microRNAs. Trends Plant Sci. 2014, 19, 175–182. [Google Scholar] [CrossRef] [PubMed]
  52. Li, Y.F.; Zheng, Y.; Addo-Quaye, C.; Zhang, L.; Saini, A.; Jagadeeswaran, G.; Axtell, M.J.; Zhang, W.; Sunkar, R. Transcriptome-wide identification of microRNA targets in rice. Plant J. 2010, 62, 742–759. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Zheng, Y.; Li, Y.F.; Sunkar, R.; Zhang, W.X. SeqTar: An effective method for identifying microRNA guided cleavage sites from degradome of polyadenylated transcripts in plants. Nucleic Acids Res. 2012, 40, e28. [Google Scholar] [CrossRef] [PubMed]
  54. Zhou, M.; Gu, L.F.; Li, P.C.; Song, X.W.; Wei, L.Y.; Chen, Z.Y.; Cao, X.F. Degradome sequencing reveals endogenous small RNA targets in rice (Oryza sativa L. ssp. indica). Front. Biol. 2010, 5, 67–90. [Google Scholar] [CrossRef]
Ijms 20 00222 g001 550
Figure 1. The four indicated groups of miRNAs identified from an incompatible interaction. CountsMIRb, the counts of miRNAs from miRBase. Expression level, low indicates <10, middle indicates >10 but less than average, high indicates over average.
Figure 1. The four indicated groups of miRNAs identified from an incompatible interaction. CountsMIRb, the counts of miRNAs from miRBase. Expression level, low indicates <10, middle indicates >10 but less than average, high indicates over average.
Ijms 20 00222 g001
Ijms 20 00222 g002 550
Figure 2. A summary of unique miRNAs detected in four libraries. (A–C) The miRNAs responded specifically the D. zeae infection in an incompatible interaction at 6, 12 and 48 h compared with at 0 h respectively. (D) The specific miRNAs were detected among four libraries.
Figure 2. A summary of unique miRNAs detected in four libraries. (A–C) The miRNAs responded specifically the D. zeae infection in an incompatible interaction at 6, 12 and 48 h compared with at 0 h respectively. (D) The specific miRNAs were detected among four libraries.
Ijms 20 00222 g002
Ijms 20 00222 g003 550
Figure 3. D. zeae-responsive miRNAs in resistant rice. (A) Heat plot showing D. zeae-responsive miRNAs identified in an incompatible interaction. (B) The number of differentially expressed miRNAs in four groups.
Figure 3. D. zeae-responsive miRNAs in resistant rice. (A) Heat plot showing D. zeae-responsive miRNAs identified in an incompatible interaction. (B) The number of differentially expressed miRNAs in four groups.
Ijms 20 00222 g003
Ijms 20 00222 g004 550
Figure 4. GO functional classification of identified target genes.
Figure 4. GO functional classification of identified target genes.
Ijms 20 00222 g004
Ijms 20 00222 g005 550
Figure 5. Predicted secondary structures and verification of expression of miRNAs precursor. (A,C,E) Predicted secondary structures of nine miRNAs precursor by RNAfold (http://rna.tbi.univie.ac.at//cgi-bin/RNAWebSuite/RNAfold.cgi, 19 March 2018) respectively. (B,D,F) The expression of nine miRNAs precursor were tested by qRT-PCR. K0, K1, K2 and K3 represent a resistance rice variety at 0, 6, 12 and 48 hpi respectively. Data represent means of three replicates ± standard deviation for each miRNA in the four miRNAs libraries.
Figure 5. Predicted secondary structures and verification of expression of miRNAs precursor. (A,C,E) Predicted secondary structures of nine miRNAs precursor by RNAfold (http://rna.tbi.univie.ac.at//cgi-bin/RNAWebSuite/RNAfold.cgi, 19 March 2018) respectively. (B,D,F) The expression of nine miRNAs precursor were tested by qRT-PCR. K0, K1, K2 and K3 represent a resistance rice variety at 0, 6, 12 and 48 hpi respectively. Data represent means of three replicates ± standard deviation for each miRNA in the four miRNAs libraries.
Ijms 20 00222 g005
Ijms 20 00222 g006 550
Figure 6. The expression levels of three targets OsGRFs of osa-miR396f analyzed by qRT-PCR in an incompatible interaction. Data represent means of three replicates ± standard deviation for each target in the four miRNAs libraries from an incompatible interaction.
Figure 6. The expression levels of three targets OsGRFs of osa-miR396f analyzed by qRT-PCR in an incompatible interaction. Data represent means of three replicates ± standard deviation for each target in the four miRNAs libraries from an incompatible interaction.
Ijms 20 00222 g006
Ijms 20 00222 g007 550
Figure 7. Overexpression of the osa-miR396f precursor in rice enhanced resistance to D. zeae. (A) Resistant phenotype to D. zeae in overexpressing transgenic plants; (B) The relative expression level of osa-miR396f precursor and resistant reactions in overexpressing rice. HS, highly susceptibility. HR, highly resistance; (C) The expression levels of three targets of osa-miR396f were inhibited in overexpressing rice. WT, wild-type rice Nipponbare. OE1-7, the osa-miR396f precursor- overexpressing rice plants.
Figure 7. Overexpression of the osa-miR396f precursor in rice enhanced resistance to D. zeae. (A) Resistant phenotype to D. zeae in overexpressing transgenic plants; (B) The relative expression level of osa-miR396f precursor and resistant reactions in overexpressing rice. HS, highly susceptibility. HR, highly resistance; (C) The expression levels of three targets of osa-miR396f were inhibited in overexpressing rice. WT, wild-type rice Nipponbare. OE1-7, the osa-miR396f precursor- overexpressing rice plants.
Ijms 20 00222 g007
Table
Table 1. Overview of sequencing reads from raw data to cleaned sequences in four libraries.
Table 1. Overview of sequencing reads from raw data to cleaned sequences in four libraries.
Sequence TypeK0K1K2K3
Total CountsUnique CountsTotal CountsUnique CountsTotal CountsUnique CountsTotal CountsUnique Counts
Raw reads6,166,8782,434,3808,949,3702,363,3667,312,8712,301,9875,565,7011,511,820
3ADT&length filter1,909,4031,024,3422,458,847982,4582,232,137922,1592,525,190803,851
Junk reads18,77813,76521,77214,65825,06416,13212,7829,015
Non-coding RNA559,796169,225600,364129,071625,544132,521359,13191,856
Rfam888,552201,5221,146,506162,3991,071,523164,209637,768115,524
mRNA1,279,886170,2572,684,763234,4241,778,770204,5321,146,100122,153
Repeats13,4793,24119,2572,24114,2992,8959,8751,869
rRNA641,748127,477857,965102,851749,37698,155491,40173,325
tRNA176,21357,003173,57639,418228,12445,15391,91027,900
snoRNA12,1624,40833,5166,86221,2755,82910,5433,854
snRNA9,5862,84524,7583,63814,3293,4417,7132,389
Other Rfam RNA48,8439,78956,6919,63058,41911,63136,2018,056
Clean reads2,385,5361,053,5503,164,3671,000,5142,637,0571,023,7481,512,623483,076
Table
Table 2. The identified targets involved in rice defense responses by degradome sequencing.
Table 2. The identified targets involved in rice defense responses by degradome sequencing.
No.TargetsTarget AnnotationUP/DownCleavage Site
1LOC_Os08g40900.1Auxin response factor 6up3466
2LOC_Os07g40290.1Auxin-responsive GH3 family proteinup2091
3LOC_Os06g39590.1AUX/IAA transcriptional regulator up502
4LOC_Os03g51970.1Growth-regulating factor 1up430
5LOC_Os11g35030.1Growth-regulating factor 2up880
6LOC_Os02g53690.1Growth-regulating factor 5up581
7LOC_Os04g04330.1Leucine-rich repeat kinasedown3509
8LOC_Os10g33940.1Auxin response factor 16down2332
9LOC_Os12g10740.1Leucine-rich repeat protein kinasedown1219
10LOC_Os11g38440.1NB-ARC disease resistance proteindown3382
11LOC_Os04g59430.1Auxin response factor 16down1345
12LOC_Os04g38720.1NAC domain containing protein 80down811
13LOC_Os08g10080.1NAC domain containing protein 1down783
14LOC_Os12g40900.1AUX/IAA transcriptional regulator down536
15LOC_Os07g42510.1Ethylene-responsive binding proteindown721
16LOC_Os03g08050.1GTP binding Elongation factor Tu proteindown969
17LOC_Os03g08020.1GTP binding Elongation factor Tu proteindown969
18LOC_Os03g08010.1GTP binding Elongation factor Tu proteindown969
19LOC_Os06g01620.1GRAS family transcription factordown464
20LOC_Os02g44360.1GRAS family transcription factordown1362
21LOC_Os02g09060.1Zinc finger family proteindown401
22LOC_Os08g41290.1Auxin-responsive family proteindown178
23LOC_Os04g51560.1WRKY DNA-binding protein 11down623
24LOC_Os10g01100.1Lectin protein kinase family proteindown2638
25LOC_Os02g50960.1Auxin efflux carrier family proteindown2109
26LOC_Os12g37760.1NB-ARC disease resistance proteindown3101
27LOC_Os07g33480.1Cytochrome P450 family 716down1606
28LOC_Os02g48080.1S-locus lectin protein kinase down1135
29LOC_Os02g11980.1Leucine-rich repeat protein kinase down3002
30LOC_Os12g43640.1Leucine-rich receptor-like protein kinasedown3203
31LOC_Os01g11340.1Cytochrome P450, family 710down1694
32LOC_Os02g14120.1Leucine-rich repeat protein kinase down2050
33LOC_Os12g01510.1Leucine-rich repeat protein kinasedown2123
34LOC_Os12g37980.1Leucine-rich repeat transmembrane kinasedown2614
Table
Table 3. The miRNA-target pairs in multi-defense responses detected by miRNA and degradome sequencing.
Table 3. The miRNA-target pairs in multi-defense responses detected by miRNA and degradome sequencing.
No.miR_nameTargetsTarget AnnotationT0 hT6–48 hUp/DownCleavage Site
1Osa-miR2118e-LOC_Os03g06680.1Protein of unknown function 50691.480down1608
p5_1ss13TALOC_Os02g50960.1Auxin efflux carrier protein91.480down2109
2Osa-miR393aLOC_Os03g36080.1–1829.510down741
LOC_Os05g05800.1F-box/RNI-like protein182.95460.85up1708
LOC_Os04g32460.1Auxin signaling F-box 21234.92172.82down2235
3Osa-miR396fLOC_Os02g53690.1Growth-regulating factor 50460.85up581
LOC_Os03g51970.1Growth-regulating factor 10115.21up430
LOC_Os11g35030.1Growth-regulating factor 20115.21up880
4Osa-miR166m_R-1LOC_Os12g41860.1Leucine zipper family protein640.3376.81down888
LOC_Os03g43930.1Leucine zipper family protein548.8576.81down966
LOC_Os10g33960.1Leucine zipper family protein922.38249.63down935
LOC_Os03g01890.1Leucine zipper family protein899.51249.63down1100
5Osa-miR171bLOC_Os02g44370.1GRAS family transcription factor457.380down1537
LOC_Os02g44360.1GRAS family transcription factor274.430down1362
LOC_Os10g40390.1GRAS family transcription factor182.950down179
LOC_Os06g01620.1GRAS family transcription factor1189.18460.85down467
LOC_Os04g46860.1GRAS family transcription factor2378.36691.27down1337
6Osa-miR156aLOC_Os08g41940.1SBP transcription factor457.380down1064
LOC_Os01g69830.1SBP transcription factor182.95115.21down1163
LOC_Os02g04680.1SBP transcription factor457.3857.61down1975
LOC_Os11g30370.1SBP transcription factor91.480down1101
LOC_Os09g31438.1SBP transcription factor91.480down819
LOC_Os02g07780.1SBP transcription factor45.740down997
LOC_Os06g49010.1SBP transcription factor45.740down1696
LOC_Os06g45310.1SBP transcription factor274.43115.21down864
7Osa-miR535-5p LOC_Os06g45310.1Squamosa promoter-like 1191.480down863
8Osa-miR159a_1R-3 LOC_Os01g59660.1Myb domain protein 33411.64288.03down135
LOC_Os10g05230.1RING/U-box superfamily protein274.430down1271
LOC_Os12g10740.1Leucine-rich repeat protein kinase182.950down346
LOC_Os06g40330.1Myb domain protein 65182.950down1219
LOC_Os01g47530.1MAP kinase 2068.6128.80down2333
9Osa-miR5072 _L-4LOC_Os05g07050.1Pre-mRNA processing splicing factor45.740down5043

Share and Cite

MDPI and ACS Style

Li, W.; Jia, Y.; Liu, F.; Wang, F.; Fan, F.; Wang, J.; Zhu, J.; Xu, Y.; Zhong, W.; Yang, J. Integration Analysis of Small RNA and Degradome Sequencing Reveals MicroRNAs Responsive to Dickeya zeae in Resistant Rice. Int. J. Mol. Sci. 2019, 20, 222. https://doi.org/10.3390/ijms20010222

AMA Style

Li W, Jia Y, Liu F, Wang F, Fan F, Wang J, Zhu J, Xu Y, Zhong W, Yang J. Integration Analysis of Small RNA and Degradome Sequencing Reveals MicroRNAs Responsive to Dickeya zeae in Resistant Rice. International Journal of Molecular Sciences. 2019; 20(1):222. https://doi.org/10.3390/ijms20010222

Chicago/Turabian Style

Li, Wenqi, Yulin Jia, Fengquan Liu, Fangquan Wang, Fangjun Fan, Jun Wang, Jinyan Zhu, Yang Xu, Weigong Zhong, and Jie Yang. 2019. "Integration Analysis of Small RNA and Degradome Sequencing Reveals MicroRNAs Responsive to Dickeya zeae in Resistant Rice" International Journal of Molecular Sciences 20, no. 1: 222. https://doi.org/10.3390/ijms20010222

APA Style

Li, W., Jia, Y., Liu, F., Wang, F., Fan, F., Wang, J., Zhu, J., Xu, Y., Zhong, W., & Yang, J. (2019). Integration Analysis of Small RNA and Degradome Sequencing Reveals MicroRNAs Responsive to Dickeya zeae in Resistant Rice. International Journal of Molecular Sciences, 20(1), 222. https://doi.org/10.3390/ijms20010222

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop