Soybean cyst nematode (SCN), Heterodera glycines, is the most devastating pathogen of soybean worldwide. MicroRNAs (miRNAs) are a class of small, non-coding RNAs that are known to play important role in plant stress response. However, there are few reports profiling the miRNA expression patterns during pathogen stress. We sequenced four small RNA libraries from two soybean cultivar (Hairbin xiaoheidou, SCN race 3 resistant, Liaodou 10, SCN race 3 susceptible) that grown under un-inoculated and SCN-inoculated soil. Small RNAs were mapped to soybean genome sequence, 364 known soybean miRNA genes were identified in total. In addition, 21 potential miRNA candidates were identified. Comparative analysis of miRNA profiling indicated 101 miRNAs belong to 40 families were SCN-responsive. We also found 20 miRNAs with different express pattern even between two cultivars of the same species. These findings suggest that miRNA paly important role in soybean response to SCN and have important implications for further identification of miRNAs under pathogen stress.
Citation: Li X, Wang X, Zhang S, Liu D, Duan Y, Dong W (2012) Identification of Soybean MicroRNAs Involved in Soybean Cyst Nematode Infection by Deep Sequencing. PLoS ONE 7(6): e39650. https://doi.org/10.1371/journal.pone.0039650
Editor: Baohong Zhang, East Carolina University, United States of America
Received: February 13, 2012; Accepted: May 24, 2012; Published: June 27, 2012
Copyright: © 2012 Li et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was funded by National Natural Science Foundation of China (grant numbers 30392100). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
The soybean cyst nematode (SCN), Heterodera glycines Ichinohe, is an obligate sedentary endoparasite that causes extensive damage to soybean, Glycine max (L.) Merr., worldwide and accounts for over one billion dollars loss annually in the US . Current nematode control strategies include nematicides, crop rotation and resistant cultivars, but each has serious limitations. The lack of understanding of nematode virulence has hampered the ability to devise novel management tactics . Advances in genomics during the last years provide opportunities for us to investigate molecular mechanism of the G. max-H. glycines system. Nematode genome sequencing projects and comparative analysis contribute to insight the nature of the evolution of plant parasitism , , . Laser capture microdissection (LCM), a refined approach to obtain homogeneous cell samples, are enabling the discovery of the genes and pathways contributing to the formation of unique, highly specialized feeding cells induced within plant roots by parasitic nematodes , . Emerging tools, such as microarray, RNA-seq, RNA interference hold tremendous potential to uncover the mechanism of soybean-SCN interactions , , , , . Previous studies indicate that plants reprogram gene expression at the transcriptional, post-transcriptional and post-translational levels to reduce the damage to stress. Increasing evidences unraveling that host endogenous smallRNAs are essential in this gene expression reprogramming process .
MicroRNAs (miRNAs) are a class of 21–24 nucleotide long non-coding RNAs that negatively regulate gene expression in animals and plants . MiRNAsrole in plant was first demonstrated in plant growth, development, hormone signaling, and RNA metabolism , . Recently, it was reported that miR393 was induced by a bacterial flagellin-derived PAMP, Flg22 in Arabidopsis. MiR393negatively regulates the expression level of the F-box auxin receptor genes and restricts Pseudomonas syringae growth. Combined with race-specific resistance test, the results suggest that miR393 has a role in imparting basal resistance but not race-specific resistance . Researchers find 10 miRNAs are down-regualted in loblolly pine response to rust fungus . Two miRNAs, bra-miR158 and bra-miR1885, were greatly upregulated when Brassica rapa was infected by Turnip mosaic virus . Genome-wide small RNA profiling analysis from nodules or from roots inoculated with symbiotic rhizobia identified a number of nodulation-associated miRNAs from different legumes , , , , . Mtr-miR166, Mtr-miR169 of M. truncatula and three soybean miRNAs, Gma-miR482, Gma-miR1511, Gma-miR1512 have been functionally analysed and associated to rhizobial symbiosis , , . These increasing evidences show that miRNAs are involved in plants response to biotic stresses.
To date, many conserved or novel miRNAs have been identified in soybean by computational analysis or high-throughput sequencing , , , , , . MiRNAs related to Bradyrhizobium japonicum, Phytophthora sojae response are obtained by comparative analyzed , . However, there is no report of miRNAs associated with SCN infection. Here, using solexa analyzer, we identified a diverse set of small RNAs which response to SCN infection in both resistance and susceptibility soybean roots. Totally, 21 new miRNAs belong to 19 families were obtained. Moreover, we also found that 40 soybean miRNA families showed differential expression level in response to soybean cyst nematode infection. Some potential targets of these miRNAs were stress responsive genes.
Plant material and RNA isolation
Glycine max, cultivar “Harbin xiaoheidou” (HB) were chosen as a soybean cyst nematode race 3 resistant standard and “Liaodou 10” (L10) were used as SCN race 3 sensitive standard. The plants were grown in greenhouse, cyst inoculation samples were propagated on SCN race 3 infected-soil which was collected from the pilot field of Shenyang Agriculture university, control samples were grown on autoclaved blank soil. Root samples were collected 30 days after seedlings emerge. All the tissues were harvested and immediately frozen in liquid nitrogen and stored at −80°C. 80 mg of plant tissues were ground to powder with liquid Nitrogen. Total RNA was isolated from soybean root samples with TRIzol reagent (Invitrogen, USA) according to the manufacturer's protocol. Total RNA was treated with DNaseI (NEB, USA) for 30 min at 37°C, and then purified by ethanol precipitation. The RNA samples were qualified by Agilent 2100.
Small RNA libraries construction and SBS sequencing
Small RNAs isolation and library construction were performed as described by Hafner Markus, et al . Firstly, total RNA was size fractioned on 15% TBE urea polyacrylamide gel and 18–30 nt fraction was collected. The 5′ RNA adapter was ligated to the RNA pool with T4 RNA ligase. Ligation products were gel-purified. Then ligated with 3′RNA adapter and purified. Small RNAs with both side adaptors were subjected to reverse transcription, 15 cycles of PCR reaction was performed to produce sequencing libraries, and the products were gel-purified.
Small RNAs samples were sequenced using Illumina Genome Analyze II at Beijing genomics Institute (Shenzhen). 10 pM of each sample was used for cluster generation. After hybridization of sequencing primer, 35 cycles of base incorporation were carried out on the 1G analyzer. More than 16 million 35 nt reads were generated for each sample.
Data analysis and new miRNAs identification
Image analysis and base calling were performed using Illumina Pipeline. Low quality reads (tags with unknown nucleotide ‘N’), empty tags (sequence with only adaptor sequence), tags less than 17 nt, and low complexity tags were trimmed with our own perl script. Removed 3′ and 5′ adapter, purified tags were mapped to the soybean genome sequence (http://www.phytozome.net/cgi-bin/gbrowse/soybean) using SOAP v1.11 (BGI). Tags that perfectly matched soybean genome were obtained for subsequent analysis. These tags were used to search for Rfam database (http://rfam.sanger.ac.uk/), unique reads mapped to tRNA (transfer RNA), rRNA (ribosomal RNA), snRNA(small nuclear RNA), snoRNAs (small nucleolar RNAs), known miRNAs (microRNAs) were removed. Repeat overlapping sequences were annotated as repeat-associated small RNAs. Tags matched to gene exon region were excluded from further analysis.
Uni-tags resulted from the pre-processing steps, with copy number more than one and less than 24 nt were tested by Mireap developed by BGI (Shen Zhen) to predict novel miRNAs. The algorithm includes two dependent parts. First of all, candidate miRNA sites are screened out from breakpoints defined by small RNAs mapping. At each genomic locus, two sequences covering the read were extracted for secondary structure analysis, one sequence extending 160 nt upstream and 20 nt down-stream from the read, and the other covering 20 nt upstream and 160 nt downstream of the read. Then, a minimal stringent criterion was used to select miRNA candidates. The secondary structure must have a hairpin with at least 18 paired nucleotides in its stem region. The free energy of the hairpin should be less than -18 kCal/mol. MiRNA and miRNA* had to reside in different arms of a hairpin structure, each with no more than 6 unpaired bases. The maximum bulge over the miRNA/miRNA* duplex was not more than 4 bases, and asymmetry of the miRNA/miRNA* duplex equal to or less than 4. In addition, sequencing of both miRNA and miRNA* required that the miRNA/miRNA* duplex had 3′ overhangs at both ends, a typical feature of Drosha and Dicer processing . The resulting set of sequences and their respective RNA structures were further analyzed to distinguish genuine miRNA precursors from other RNAs containing similar RNA structures.
Mature miRNA sequences were used as queries to search for potential target mRNAs in the Glycine max database (http://compbio.dfci.harvard.edu/index.html, DFCI gene index release 16) using the web-based computer psRNATarget Server (http://biocomp5.noble.org/psRNATarget/). Results from these analyses were inspected on the Phytozome to obtain the loci and protein annotation. The rules used for miRNAs target gene prediction were based on suggestions by Allen et al. and Schwab et al. respectively , . No more than 4 mismatches between the sRNA and target were allowed. The minimum free energy (MFE) of the miRNA/target duplex should be > = 74% of the MFE which the miRNA bound to its perfect complement , .
Differential expression analysis of miRNAs
The frequency of miRNA was normalized to TPM (number of transcripts per million clean tags) in order to compensate for variable numbers of tags generated for each sample. The fold-change between SCN-infected and control sample were calculated. To avoid divided by 0, we use 1 instead of tag that was not detected in sample. Then the statistically analysis was performed according to Poisson distribution.
- (1) Normalization criterion:
- (2) Fold-change criterion:
- (3) The P-value calculated formula:
Quantitative PCR analysis
The profiles of 16 different expressed miRNAs were assayed by Real-time PCR. About 200ng qualified total RNA for each sample were used for this analysis.
The miRNA gene specific reverse transcript stem-loop primers were designed using Primer 5.0 following the method described by Varkonyi-Gasic et al . The reverse transcript reaction was performed with Superscript II (Invitrogen, USA) following the the manufacturer's protocol. Reverse transcript products were used as template for Real-time PCR and all reaction were assayed in triplicates. The reactions were performed in Stepone RT-PCR machine (Applied Biosystems, USA) using SYBR Premix ExTaq kit (Takara, China). PCR cycling began with template denaturation and hot start Taq activation at 95°C for 1 min, then 40 cycles of 95°C for 5 sec, and 60°C for 30 sec performed and data collected during each cycle at the 60°C extension step. The U6 snRNA was selected as a reference gene for normalization. Relative quantitation of the miRNAs amplified was performed by the comparative ΔΔCT method. The threshold cycle (CT) indicated the fractional cycle number at which the amount of amplified target reaches a fixed threshold.
Time x represents any time point and Time 0 represents the expression of the target gene normalized to U6 . The amount of relative gene expression can be obtained.
High-throughput sequencing of soybean small RNAs
To investigate the role of soybean miRNAs in response to soybean cyst nematode (SCN) infection, two soybean cultivars, Liaodou10 (L10), a soybean cyst nematode race 3 susceptible cultivar and Harbin xiaoheidou (HB) which resistant to SCN race 3 were selected for this study. Totally, four small RNA (sRNA) libraries were constructed from the two cultivars. The SCN-infected sRNA libraries from HB and L10 roots were named SHB, SL10 respectively. While sRNAs libraries from un-infected roots of HB and L10 were constructed as control, which designated as SHBC and SL10C. These four soybean root small RNA libraries were sequenced by Illumina Genome Analyze II, yielding a total of 69,861,808 sRNA raw reads. After removing the low quality reads, 11,831,532, 10,980,930, 10,494,778 and 11,238,552 clean reads were obtained for SHB, SHBC, SL10 and SL10C, respectively. These sRNAs were consisted by 11,317,750 unique sequences as shown in table 1, and these reads were searched against the public soybean genomic and expressed sequence tags using SOAP program, leading to 5,103,481 genome-matched reads .
Majority of sRNAs were sequenced only a few times although some sRNAs were highly abundant and present thousands of times in one sample. About 36% of the sRNAs were sequenced only once suggests soybean roots have large and diverse sRNAs population.
More than 90% of clean reads in this study were longer than 18 nt. Size distributions based both on total abundances and unique reads were assessed and shown in figure 1. For total abundance, more than 75% sRNAs were 19–24 nt in length, with 21, 22, 24 nt being the major length classes. For the proportion of unique reads, the 24 nt reads were prevailing in all four libraries and the 21, 22 nt reads were less abundant. Analysis revealed that there is a variation in abundance of sRNAs, the 21, 22 nt sRNAs showed higher redundancies than 24 nt sRNAs. The observation indicated the differently expression pattern of distinct categories of sRNAs in the same sample. This result was consistent with those of Citrus rifoliate, Arabidopsis, Medicago truncatula , , .
(a) Ratio of unique sRNAs reads vs size. (b) Ratio of total sRNAs reads vs size.
The common/specific sRNAs in SHB vs SHBC and SL10 vs SL10C were analyzed for total and unique tags, the results highlight the fact that sRNAs common in different libraries had more abundance than those library specific sRNAs (Table S1).
Identification of known miRNAs
In this study, miRBase release 18.0, which contains 391 soybean mature miRNAs, was searched for soybean known miRNAs . A total of 364 known soybean mature miRNAs were identified in at least one library, among which 283 were sequenced in all 4 four libraries and 53 miRNAs were detected only in one or two libraries, which was summarized in table 2. There were 27 known soybean miRNAs, containing 7 conserved miRNAs and 20 fabaceae or soybean specific miRNAs, were absent in our data set (Table S2). This is probably due to low expression levels or stage-/tissue- specific RNA transcription. Two of reported miRNA*, gma-miR1507c* and gma-miR1516* were also identified.
Gma-miR1507ab was the most abundant miRNA in all the 4 samples in our study, followed by members of MIR156. The top 30 abundant miRNAs which occupied more than 85% of expressed miRNA tags were generally unanimous between samples. Most of the top 30 abundant miRNAs were conserved miRNAs such as, members of MIR156, MIR166 and MIR168, while three legume specific miRNAs, miR1507, miR1509 and miR1510 were highly abundant in all four libraries. Further analysis revealed that conserved miRNAs had relative high abundant in general, this observation suggested that conserved miRNAs may be essential for controlling basic cellular and developmental in plants. More than half of the miRNAs showed very low levels of expression, with fewer than 100 reads.
Within the tags that mapped to known miRNAs over 55% have length 21 nt, followed by 20 nt and 22 nt reads. Among the miRNA reads, 24 nt length was less than 1%. This result was consistent with current understanding. The canonical miRNAs are 21 nt length, while canonical siRNAs are 24 nt length . About 94% of these miRNA reads start with a uracil contrast to 30% of all small RNAs. These distribution were examined in all four samples individually, there were no obvious difference among them.
We observed there are diverse isoforms of mature miRNAs from the same precursor and the 3′ ends of miRNA showed stronger heterogeneity than the 5′ ends. As exemplified by miR1509a in Figure S1, the highlighted 3′ ends isoform were highly accumulated. Some most abundance tags mapped to hairpin were not previous annotated miRNAs. Gma-miR156b is an extreme example, the former annotated miRNA were not detected in all 4 samples, indicating the tagt0006055 should be regarded as the final functional molecular.
Prediction and validation of novel soybean miRNAs
Small RNAs perfectly matched to soybean genome sequences were searched against Rfam, ncRNA (non-coding RNA) of NCBI, EST database to eliminate other ncRNAs, such as rRNA, tRNA, snRNA, snoRNA and degradation products of protein-coding transcripts. Small RNAs were aligned to repeat associated RNA to find matched tags in the data set. With the analysis, 3,571,130 tags named unann were obtained for novel miRNA detection (Table S3). Characteristic hairpin structures of those reads were explored using Mireap. Then precursors were manually checked according the criteria described by Blake C. Meyers, et al . Twenty two new soybean miRNAs not previously reported were identified by the analysis, named temporarily in the form of soy_number in table 3. Ten of the 22 new miRNAs had length 21 nt, followed by 6 miRNAs with 22 nt length. The minimum free energy (MFE) for hairpin structure of miRNA precursor were lower than -25 kcal/mol. Stem-loop structures of miRNAs precursors were predicted by MFOLD.
Novel miRNA candidates were further assigned to miRNA families using sequence similarity to other known miRNA in miRBase database. Among the 21 novel miRNAs, 18 miRNAs have not been found in eukaryotes, except soy_1, soy_2, soy_3. All the three miRNAs were members of MIR166, which conserved in both monocots and dicots. The 18 miRNAs without homolog in database, along with the known soybean miRNAs were used for multiple alignments using ClustalW and the miRNA families were assigned based on the dendogram tree . None of the miRNAs was assigned to families based on sequence similarity, while the miRNAs will get families with further study.
To validate the predicted new miRNAs, stem-loop RT-PCR were performed to examine whether the miRNAs were expressed in the soybean roots. Eight miRNAs which had more abundance were selected to confirm the analysis. Primers used in this experiment are showed in S5. The PCR products were about 60bp in length, and all the 8 miRNAs were found to be expressed in the soybean roots (Figure 2).
SCN-infection associated miRNAs
Although the absolute expression level of miRNA is useful, identification of differential expression profile at the whole genome level in response to endogenous cues or stress is often desirable to detect miRNA function in particular cell processes. The sequencing frequencies for miRNAs in the four libraries were used as an index for estimating the relative abundance, expression level between SCN-infected soybean root and un-infected root were compared based on the “transcripts per million”(TPM) of miRNAs (Figure 3).
Expression levels are normalized to TPM. Data points lower- or upper- the slope line represent down- or up- regulated miRNAs in panel. The changes in up- and down-regulated miRNAs are greater than 2 fold.
We used fold-change values ≥ 2 or ≤0.5 combined with P-value <0.01 as the threshold, a total of 101 miRNAs belong to 40 families were identified to be significantly differentially expressed in at least one cultivar, the results were showed in Figure 4. Most of the differentially expressed miRNAs were down regulated during the SCN-infection and only 6 miRNAs were up-regulated. Thus, down-regulation of miRNAs appeared to be important in SCN infection. MiR171c and miR319 were highly induced by SCN infection in both cultivars, expression of miR390b was induced in HB specific while miR862, miR5372 and four members of miR169 were SCN-infection induced only in L10. MiR159b, miR398 and miR2119 showed the highest alternation with 12, 8.5 and 8 fold changes in HB while miR862b increased to 25 fold after SCN infection was the highest changed miRNA in L10. There were 16 miRNA families which were co-regulated in both two genotypes, among them, miR156, miR162, miR166a, miR167, miR319, miR397, miR398, miR408 were conserved miRNAs between plants species, two miRNA families, miR2119 and miR3522 were conserved in fabaceae, while miR1520, miR4365, miR4387, miR4413, miR4996 and miR5671 were soybean specific miRNAs. The results indicated that both conserved miRNAs and soybean specific miRNAs participated in nematode defense. HB, the SCN race 3 resistant cultivar, specific response miRNAs contained 15 members, while there were 9 susceptible cultivar L10 specific response miRNAs.
miRNA with expression changes greater than 2 fold and P-value lower than 0.01 at least in one cultirvar.
Notably, some members of one miRNA family had varied frequencies and the members showed differential expressed pattern during SCN-infection. MiR1507 had three members, gma-miR1507ab frequency was more than 40 times of gma-miR1507c. MiR1507ab was down-regulated after SCN-infection while miR1507c was induced by nematode infection. We also found that members of MIR390 and MIR171 had different expression pattern showed in Table S4. Differential expressed pattern indicating the function divergence in miRNA families.
To confirm the Solexa sequencing results, we performed qRT-PCR to further validate several miRNAs that were detected as differential expressed. We found that most of the qRT-PCR results were consistent with the deep sequencing data (figure 5). However, there were differences between the sequencing results and qRT-pCR data, the log2 ratio of gma-miR1512 in L10 was −0.1 with sequencing data, but the qRT-PCR ratio was 0.36, this could been due to sequence bias induced by the small RNA library construction or to the different normalization methods in this two strategy.
Targets prediction of soybean miRNAs
MiRNAs regulate expression of specific gene via hybridization to mRNA transcripts to promote RNA degradation, inhibit translation or both . We predicted the potential targets of miRNAs using the webserver, psRNATarget . For the miRBase registered soybean miRNAs, a total of 293 target genes were identified showed in Table S5. Combined with previous studies, we investigated that most target of conserved miRNAs were transcript factors which involved in plant growth and stress responses. Some of miRNA families, such as MIR156, MIR160, MIR164, MIR393, MIR396, MIR1510 were conserved in their targets. The targets prediction results of MIR1507, MIR390 and MIR171 were checked manually, gma-1507a and gma-1507b had the same target genes, while potential targets of gma-1507c were totally different. This was consistent with the expression pattern between miRNAs. Members of MIR390 and MIR171 come to the same results.
Among the 21 novel identified miRNAs, 16 miRNAs belonged to 14 families had predicted targets, the results were showed in table 4. Soy_5, soy_8, soy_13, soy_17, soy_20 target only one locus, while other miRNA candidates had multiple distinct targets genes. Soy_1, soy_2 and soy_3 were members of MIR166, targets of the three miRNA candidates were HD-ZIP transcript factor which were conserved with other miRNA members. Function of new miRNA tragets were diverse and were not enriched in transcript factors compared to conserved miRNAs. Calmodulin, serves as an intracellular Ca2+ receptor and mediates the Ca2+ regulation of cyclic nucleotide and glycogen metabolism, secretion, motility and Ca2+ transport, also a dynamic component of the mitotic apparatus. Calmodulin was detected as soy_9 potential targets indicating the miRNA may participate in cellular regulation .
High-throughput sequencing is an efficient strategy to study miRNAs at the whole genome level and has dramatically expanded the number of miRNA families known to exit in plants. As to soybean, 391 miRNAs have been well annotated by deep sequencing and registered in miRBase database . In present study, we sequenced small RNA libraries of two genotype soybean root treated by soybean cyst nematode, totally got 69,788,759 reads and 11,317,750 unique reads. The large amount of small RNA data revealed a diverse and complex small RNA population. Moreover, the large data set allows us to identify miRNAs with low abundance. In total, 364 known miRNAs and 21 novel miRNAs were detected, 171 of them had frequency less than 20. Combined with the previous report, 40% the soybean miRNAs are belonged to soybean or lineage specific, while roughly 70% known Arabidopsis miRNA families lack apparent homologs in outside of the brassicasseae . This result indicates that miRNAs discovered in soybean has not reached saturation. Novel miRNA detected in this study showed relatively low expression levels, which was consistent with previous studies , , . Thus much larger dataset are needed to identify new miRNAs than previous studies.
In our study, gma-miR1507ab was the most abundant miRNAs in all four samples followed by miR1509, miR1510 and miR2118. Although miR1509a and miR2019 were annotated other than 22 nt, based our data, previous register should be corrected. In M. truncatula, peanuts and common beans, miR1507, miR1509, and miR2118 were highly abundant 22-nt miRNAs . The conservation and abundant expression of these 22 nt in legumes indicated unique evolution ways of these lineage-specific miRNAs. More interestingly, we found that all the four miRNA families were regulators of legume NB-LRR-coding genes. Furthermore, potential targets of miR5039, miR5041, miR5374, miR5376, miR5668 were plant NB-LRR transcripts. Until now, the only function of plant NB-LRR proteins is recognize pathogen effectors and activate defense responses . The results implicating that the specialized group of miRNAs in legume target NB-LRR genes are crucial regulator to some legume specific plant defense to pathogens.
MiR319 was shown to be drought-responsive in Arabidopsis, rice, and sugarcane, while we firstly report miR319 was involved in biotic stress , , . Gma-miR319 was the single up-regulated conserved miRNA family in both two cultivars. In contrast to deeply conserved miRNA families, most young miRNAs were weakly expressed, more divergent, and tend to lack targets. Thus, function information about non-conserved miRNA is rare. The 20 legume- or soybean specific miRNAs related to SCN infection were identified in current study, suggesting a number of non-conserved miRNAs function as regulators of genes in plant genomes, and play an important role in plant pathogen defense. The complex plant response to SCN infection spans the early migratory stages during penetration and migration through roots into the later sedentary stages of syncytium induction and feeding which have inspired numberous studies to identify genes involved in SCN. Here we report the miRNAs related to SCN infection. In previous study, 43 SCN-responsive miRNAs were identified. More than 80% of miRNAs were suppressed by SCN infection in both cultivars. Similarity, 10 of 11 miRNAs were down-regulated in the stem galls induced by the rust fungus Cronartium quercuum f. sp. Fusiforme . While in Arabidopsis-H. schachtii interaction study, all the 14 differentially expressed miRNAs were statistically significantly down-regulated at the 4 dpi (day post-inoculation) while at 7 dpi, 5 miRNAs were up-regulated and 7 were down-regulated . Indicating the tested miRNAs have complex expression profiles at different stage of cyst nematode infection which is consistent with gene expression pattern during SCN-infection , . Targets of SCN-responsive miRNAs include transcription factors, general stress-responsive genes, disease resistance responsive proteins, PR proteins. All these genes expression pattern were checked in previous studies, about 50% miRNAs showed negative correlations between miRNA accumulation and target gene mRNA abundance . These discrepancies suggest that other regulatory mechanisms could be involved in regulating these genes.
MiR319 was the single up-regulated conserved miRNA family in both two cultivars, which was shown to be drought-responsive in Arabidopsis, rice, and sugarcane , , . In tomato, miR319 was involved in Solanum lycopersicum, and showed opposite expression patterns in leaves . The diverse functions of miR319 were due to the target gene TCP that presumably control cell divisions in stress response. Interestingly, during the same stress, several miRNAs had differential express pattern in the two cultivars. MiR169 was up-regulated in the sensitive cultivar, but down-regulated in the tolerant. MiR169 targeted nuclear factor Y subunit, which can regulate expression level of some stress-responsive genes . MiR169 guided regulation of these transcription factors appeared to be important in variety of abiotic stress, including drought, cold, salinity, UV-B radiation, and heat stress , , , , . Furthermore, MtHAP2-1, a new transcription factor of the CCAAT-binding family identified in M. truncatula, is regulated by miR169. MtHAP2-1 is essential for nodule meristematic persistence in M. truncatula, and that miR169 confers spatial and temporal accuracy of nodules development . Contrarily, gma-miR390b was induced by SCN in HB and suppressed in L10. MiR390 was involved in auxin signaling and regulated root growth . Gma-miR390c were down-regulated in HB but without significant changes in L10. Expression patterns of MIR390 members are different even in single cultivar which indicating function diversity of this miRNA family. With other 15 miRNAs up- or down-regulated only in one cultivar, the genotype-specific regulation of miRNAs might be part of the reason why the two soybean cultivars had differential tolerance to soybean cyst nematode.
In summary, our study have identified miRNAs form soybean roots, analyzed their expression pattern during soybean cyst nematode infection and predicted the putative targets of these miRNAs. Further function characterization of SCN-responsive miRNAs and targets will contribute substantial new knowledge to our understanding of the complex soybean-SCN pathosystem.
Diversification of mature miRNA from precursors. Detected diverse isoforms of soybean miRNA, the most abundant sRNA are underline in red, sRNAs in red frame are annotated mature miRNA in miRBase.
Statistics of common sRNAs from control and SCN-infected. Statistics of common sRNAs from control and SCN-infected sample in both total and unique reads.
Known miRNAs identified in Glycine max. Soybean known miRNAs identified frequency in each sample.
Annotation results of sRNAs. Reads abundance of various classification of small RNAs in each sample.
Express pattern diverse in miRNA families. Different members of miRNA family with differential expressed pattern during soybean response to SCN.
Predicted targets of soybean miRNAs. Potential targets of miRNAs predicted using psRNATarget.
Conceived and designed the experiments: XL WD YD. Wrote the paper: XL. Carried out the small RNA cloning and library sequencing: XL SZ. Performed the quantitative RT-PCR analysis: XL XW DL. Performed the data management, processing and analysis: XL. Prepared the plant materials: XW DL. Critically revised the article: WD YD. Obtained the funding: WD YD.
- 1. Wrather JA, Koenning SR (2009) Effects of Diseases on Soybean Yields in the United States 1996 to 2007. Plant Health Progress. JA WratherSR Koenning2009Effects of Diseases on Soybean Yields in the United States 1996 to 2007.Plant Health Progress
- 2. Chitwood DJ (2002) Phytochemical based strategies for nematode control. Annual Review of Phytopathology 40(1): 221–249.DJ Chitwood2002Phytochemical based strategies for nematode control.Annual Review of Phytopathology40(1)221249
- 3. Mitreva M, Jasmer DP, Zarlenga DS, Wang Z, Abubucker S, et al. (2011) The draft genome of the parasitic nematode Trichinella spiralis. Nature Genetics 43: 228–235.M. MitrevaDP JasmerDS ZarlengaZ. WangS. Abubucker2011The draft genome of the parasitic nematode Trichinella spiralis.Nature Genetics43228235
- 4. Sommer RJ, Streit A (2011) Comparative Genetics and Genomics of Nematodes: Genome Structure, Development, and Lifestyle. Annual Review of Genetics 45: 1–20.RJ SommerA. Streit2011Comparative Genetics and Genomics of Nematodes: Genome Structure, Development, and Lifestyle.Annual Review of Genetics45120
- 5. Abad P, McCarter JP (2011) Genome Analysis of Plant Parasitic Nematodes. Genomics and Molecular Genetics of Plant-Nematode Interactions. pp. 103–117.P. AbadJP McCarter2011Genome Analysis of Plant Parasitic Nematodes.Genomics and Molecular Genetics of Plant-Nematode Interactions103117
- 6. Klink VP, Overall CC, Alkharouf NW, MacDonald MH, Matthews BF (2007) Laser capture microdissection (LCM) and comparative microarray expression analysis of syncytial cells isolated from incompatible and compatible soybean (Glycine max) roots infected by the soybean cyst nematode (Heterodera glycines). Planta 226: 1389–1409.VP KlinkCC OverallNW AlkharoufMH MacDonaldBF Matthews2007Laser capture microdissection (LCM) and comparative microarray expression analysis of syncytial cells isolated from incompatible and compatible soybean (Glycine max) roots infected by the soybean cyst nematode (Heterodera glycines).Planta22613891409
- 7. Ramsay K, Jones MGK, Wang Z (2006) Laser capture microdissection: a novel approach to microanalysis of plant? microbe interactions. Molecular Plant Pathology 7: 429–435.K. RamsayMGK JonesZ. Wang2006Laser capture microdissection: a novel approach to microanalysis of plant? microbe interactions.Molecular Plant Pathology7429435
- 8. Alkharouf NW, Klink VP, Chouikha IB, Beard HS, MacDonald MH, et al. (2006) Timecourse microarray analyses reveal global changes in gene expression of susceptible Glycine max (soybean) roots during infection by Heterodera glycines (soybean cyst nematode). Planta 224: 83.NW AlkharoufVP KlinkIB ChouikhaHS BeardMH MacDonald2006Timecourse microarray analyses reveal global changes in gene expression of susceptible Glycine max (soybean) roots during infection by Heterodera glycines (soybean cyst nematode).Planta22483
- 9. Ithal N, Recknor J, Nettleton D, Hearne L, Maier T, et al. (2007a) Parallel genome-wide expression profiling of host and pathogen during soybean cyst nematode infection of soybean. Molecular Plant-Microbe Interactions 20: 293–305.N. IthalJ. RecknorD. NettletonL. HearneT. Maier2007aParallel genome-wide expression profiling of host and pathogen during soybean cyst nematode infection of soybean.Molecular Plant-Microbe Interactions20293305
- 10. Ithal N, Recknor J, Nettleton D, Hearne L, Maier T, et al. (2007b) Developmental transcript profiling of cyst nematode feeding cells in soybean roots. Molecular Plant-Microbe Interactions 20: 510–525.N. IthalJ. RecknorD. NettletonL. HearneT. Maier2007bDevelopmental transcript profiling of cyst nematode feeding cells in soybean roots.Molecular Plant-Microbe Interactions20510525
- 11. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics 10: 57–63.Z. WangM. GersteinM. Snyder2009RNA-Seq: a revolutionary tool for transcriptomics.Nature Reviews Genetics105763
- 12. Bakhetia M, Urwin PE, Atkinson HJ (2007) q-PCR analysis and RNAi define pharyngeal Gland cell-expressed genes of Heterodera glycines required for initial interactions with the host. Molecular Plant-Microbe Interactions 20: 306–312.M. BakhetiaPE UrwinHJ Atkinson2007q-PCR analysis and RNAi define pharyngeal Gland cell-expressed genes of Heterodera glycines required for initial interactions with the host.Molecular Plant-Microbe Interactions20306312
- 13. Sunkar R (2010) MicroRNAs with macro-effects on plant stress responses. Seminars in Cell & Developmental Biology 21: 805–811.R. Sunkar2010MicroRNAs with macro-effects on plant stress responses.Seminars in Cell & Developmental Biology21805811
- 14. Carthew RW, Sontheimer EJ (2009) Origins and Mechanisms of miRNAs and siRNAs. Cell 136: 642–655.RW CarthewEJ Sontheimer2009Origins and Mechanisms of miRNAs and siRNAs.Cell136642655
- 15. Nonogaki H (2010) MicroRNA Gene Regulation Cascades During Early Stages of Plant Development. Plant and Cell Physiology 51: 1840–1846.H. Nonogaki2010MicroRNA Gene Regulation Cascades During Early Stages of Plant Development.Plant and Cell Physiology5118401846
- 16. Liu Q, Zhang Y-C, Wang C-Y, Luo Y-C, Huang Q-J, et al. (2009) Expression analysis of phytohormone-regulated microRNAs in rice, implying their regulation roles in plant hormone signaling. FEBS Letters 583: 723–728.Q. LiuY-C ZhangC-Y WangY-C LuoQ-J Huang2009Expression analysis of phytohormone-regulated microRNAs in rice, implying their regulation roles in plant hormone signaling.FEBS Letters583723728
- 17. Navarro L (2006) A Plant miRNA Contributes to Antibacterial Resistance by Repressing Auxin Signaling. Science 312: 436–439.L. Navarro2006A Plant miRNA Contributes to Antibacterial Resistance by Repressing Auxin Signaling.Science312436439
- 18. Lu S, Sun Y-H, Amerson H, Chiang VL (2007) MicroRNAs in loblolly pine (Pinus taeda L.) and their association with fusiform rust gall development. The Plant Journal 51: 1077–1098.S. LuY-H SunH. AmersonVL Chiang2007MicroRNAs in loblolly pine (Pinus taeda L.) and their association with fusiform rust gall development.The Plant Journal5110771098
- 19. He X-F, Fang Y-Y, Feng L, Guo H-S (2008) Characterization of conserved and novel microRNAs and their targets, including a TuMV-induced TIR–NBS–LRR class R gene-derived novel miRNA in Brassica. FEBS Letters 582: 2445–2452.X-F HeY-Y FangL. FengH-S Guo2008Characterization of conserved and novel microRNAs and their targets, including a TuMV-induced TIR–NBS–LRR class R gene-derived novel miRNA in Brassica.FEBS Letters58224452452
- 20. Subramanian S, Fu Y, Sunkar R, Barbazuk WB, Zhu J-K, et al. (2008) Novel and nodulation-regulated microRNAs in soybean roots. BMC Genomics 9: 160.S. SubramanianY. FuR. SunkarWB BarbazukJ-K Zhu2008Novel and nodulation-regulated microRNAs in soybean roots.BMC Genomics9160
- 21. Wang Y, Li P, Cao X, Wang X, Zhang A, et al. (2009) Identification and expression analysis of miRNAs from nitrogen-fixing soybean nodules. Biochemical and Biophysical Research Communications 378: 799–803.Y. WangP. LiX. CaoX. WangA. Zhang2009Identification and expression analysis of miRNAs from nitrogen-fixing soybean nodules.Biochemical and Biophysical Research Communications378799803
- 22. Lelandais-Briere C, Naya L, Sallet E, Calenge F, Frugier F, et al. (2009) Genome-Wide Medicago truncatula Small RNA Analysis Revealed Novel MicroRNAs and Isoforms Differentially Regulated in Roots and Nodules. The Plant Cell Online 21: 2780–2796.C. Lelandais-BriereL. NayaE. SalletF. CalengeF. Frugier2009Genome-Wide Medicago truncatula Small RNA Analysis Revealed Novel MicroRNAs and Isoforms Differentially Regulated in Roots and Nodules.The Plant Cell Online2127802796
- 23. Arenas-Huertero C, Pérez B, Rabanal F, Blanco-Melo D, la Rosa C, et al. (2009) Conserved and novel miRNAs in the legume Phaseolus vulgaris in response to stress. Plant Molecular Biology 70: 385–401.C. Arenas-HuerteroB. PérezF. RabanalD. Blanco-MeloC. la Rosa2009Conserved and novel miRNAs in the legume Phaseolus vulgaris in response to stress.Plant Molecular Biology70385401
- 24. Simon SA, Meyers BC, Sherrier DJ (2009) MicroRNAs in the Rhizobia Legume Symbiosis. Plant Physiology 151: 1002–1008.SA SimonBC MeyersDJ Sherrier2009MicroRNAs in the Rhizobia Legume Symbiosis.Plant Physiology15110021008
- 25. Boualem A, Laporte P, Jovanovic M, Laffont C, Plet J, et al. (2008) MicroRNA166 controls root and nodule development in Medicago truncatula. The Plant Journal 54: 876–887.A. BoualemP. LaporteM. JovanovicC. LaffontJ. Plet2008MicroRNA166 controls root and nodule development in Medicago truncatula.The Plant Journal54876887
- 26. Combier JP, Frugier F, de Billy F, Boualem A, El-Yahyaoui F, et al. (2006) MtHAP2-1 is a key transcriptional regulator of symbiotic nodule development regulated by microRNA169 in Medicago truncatula. Genes & Development 20: 3084–3088.JP CombierF. FrugierF. de BillyA. BoualemF. El-Yahyaoui2006MtHAP2-1 is a key transcriptional regulator of symbiotic nodule development regulated by microRNA169 in Medicago truncatula.Genes & Development2030843088
- 27. Li H, Deng Y, Wu T, Subramanian S, Yu O (2010) Misexpression of miR482, miR1512, and miR1515 Increases Soybean Nodulation. Plant Physiology 153: 1759–1770.H. LiY. DengT. WuS. SubramanianO. Yu2010Misexpression of miR482, miR1512, and miR1515 Increases Soybean Nodulation.Plant Physiology15317591770
- 28. Song Q-X, Liu Y-F, Hu X-Y, Zhang W-K, Ma B, et al. (2011) Identification of miRNAs and their target genes in developing soybean seeds by deep sequencing. BMC Plant Biology 11: 5.Q-X SongY-F LiuX-Y HuW-K ZhangB. Ma2011Identification of miRNAs and their target genes in developing soybean seeds by deep sequencing.BMC Plant Biology115
- 29. Joshi T, Yan Z, Libault M, Jeong D–H, Park S, et al. (2010) Prediction of novel miRNAs and associated target genes in Glycine max. BMC Bioinformatics 11: S14.T. JoshiZ. YanM. LibaultD–H JeongS. Park2010Prediction of novel miRNAs and associated target genes in Glycine max.BMC Bioinformatics11S14
- 30. Zeng HQ, Zhu YY, Huang SQ, Yang ZM (2010) Analysis of phosphorus-deficient responsive miRNAs and cis-elements from soybean (Glycine max L.). Journal of Plant Physiology 167: 1289–1297.HQ ZengYY ZhuSQ HuangZM Yang2010Analysis of phosphorus-deficient responsive miRNAs and cis-elements from soybean (Glycine max L.).Journal of Plant Physiology16712891297
- 31. Zhang B, Pan X, Stellwag EJ (2008) Identification of soybean microRNAs and their targets. Planta 229: 161–182.B. ZhangX. PanEJ Stellwag2008Identification of soybean microRNAs and their targets.Planta229161182
- 32. Wang Y, Li P, Cao X, Wang X, Zhang A, et al. (2009) Identification and expression analysis of miRNAs from nitrogen-fixing soybean nodules. Biochemical and Biophysical Research Communications 378: 799–803.Y. WangP. LiX. CaoX. WangA. Zhang2009Identification and expression analysis of miRNAs from nitrogen-fixing soybean nodules.Biochemical and Biophysical Research Communications378799803
- 33. Chen R, Hu Z, Zhang H (2009) Identification of MicroRNAs in Wild Soybean (Glycine soja). Journal of Integrative Plant Biology 51: 1071–1079.R. ChenZ. HuH. Zhang2009Identification of MicroRNAs in Wild Soybean (Glycine soja).Journal of Integrative Plant Biology5110711079
- 34. Guo N, Ye W, Wu X, Shen D, Wang Y, et al. (2011) Microarray profiling reveals microRNAs involving soybean resistance to Phytophthora sojae. Genome 54: 954–958.N. GuoW. YeX. WuD. ShenY. Wang2011Microarray profiling reveals microRNAs involving soybean resistance to Phytophthora sojae.Genome54954958
- 35. Hafner M, Landgraf P, Ludwig J, Rice A, Ojo T, et al. (2008) Identification of microRNAs and other small regulatory RNAs using cDNA library sequencing. Methods 44: 3–12.M. HafnerP. LandgrafJ. LudwigA. RiceT. Ojo2008Identification of microRNAs and other small regulatory RNAs using cDNA library sequencing.Methods44312
- 36. Meyers BC, Axtell MJ, Bartel B, Bartel DP, Baulcombe D, et al. (2008) Criteria for Annotation of Plant MicroRNAs. The Plant Cell Online 20: 3186–3190.BC MeyersMJ AxtellB. BartelDP BartelD. Baulcombe2008Criteria for Annotation of Plant MicroRNAs.The Plant Cell Online2031863190
- 37. Allen E, Xie Z, Gustafson AM, Carrington JC (2005) microRNA-Directed Phasing during Trans-Acting siRNA Biogenesis in Plants. Cell 121: 207–221.E. AllenZ. XieAM GustafsonJC Carrington2005microRNA-Directed Phasing during Trans-Acting siRNA Biogenesis in Plants.Cell121207221
- 38. Schwab R, Palatnik JF, Riester M, Schommer C, Schmid M, et al. (2005) Specific Effects of MicroRNAs on the Plant Transcriptome. Developmental Cell 8: 517–527.R. SchwabJF PalatnikM. RiesterC. SchommerM. Schmid2005Specific Effects of MicroRNAs on the Plant Transcriptome.Developmental Cell8517527
- 39. Alves-Junior L, Niemeier S, Hauenschild A, Rehmsmeier M, Merkle T (2009) Comprehensive prediction of novel microRNA targets in Arabidopsis thaliana. Nucleic Acids Research 37: 4010–4021.L. Alves-JuniorS. NiemeierA. HauenschildM. RehmsmeierT. Merkle2009Comprehensive prediction of novel microRNA targets in Arabidopsis thaliana.Nucleic Acids Research3740104021
- 40. Joshi T, Yan Z, Libault M, Jeong D–H, Park S, et al. (2010) Prediction of novel miRNAs and associated target genes in Glycine max. BMC Bioinformatics 11: S14.T. JoshiZ. YanM. LibaultD–H JeongS. Park2010Prediction of novel miRNAs and associated target genes in Glycine max.BMC Bioinformatics11S14
- 41. Varkonyi-Gasic E, Wu R, Wood M, Walton EF, Hellens RP (2007) Protocol: a highly sensitive RT-PCR method for detection and quantification of microRNAs. Plant Methods 3: 12.E. Varkonyi-GasicR. WuM. WoodEF WaltonRP Hellens2007Protocol: a highly sensitive RT-PCR method for detection and quantification of microRNAs.Plant Methods312
- 42. Livak K J, Schmittgen T D (2001) Analysis of relative gene expression data using real-time quantitative PCR and the2-ΔΔC method. Methods 25: 402–408.J. Livak KD. Schmittgen T2001Analysis of relative gene expression data using real-time quantitative PCR and the2-ΔΔC method.Methods25402408
- 43. Song C, Wang C, Zhang C, Korir N, Yu H, et al. (2010) Deep sequencing discovery of novel and conserved microRNAs in trifoliate orange (Citrus trifoliata). BMC Genomics 11: 431.C. SongC. WangC. ZhangN. KorirH. Yu2010Deep sequencing discovery of novel and conserved microRNAs in trifoliate orange (Citrus trifoliata).BMC Genomics11431
- 44. Schmutz J, Cannon SB, Schlueter J, Ma J, Mitros T, et al. (2010) Genome sequence of the palaeopolyploid soybean. Nature 463: 178–183.J. SchmutzSB CannonJ. SchlueterJ. MaT. Mitros2010Genome sequence of the palaeopolyploid soybean.Nature463178183
- 45. Rajagopalan R, Vaucheret H, Trejo J, Bartel DP (2006) A diverse and evolutionarily fluid set of microRNAs in Arabidopsis thaliana. Genes & Development 20: 3407–3425.R. RajagopalanH. VaucheretJ. TrejoDP Bartel2006A diverse and evolutionarily fluid set of microRNAs in Arabidopsis thaliana.Genes & Development2034073425
- 46. Szittya G, Moxon S, Santos DM, Jing R, Fevereiro MPS, et al. (2008) High-throughput sequencing of Medicago truncatula short RNAs identifies eight new miRNA families. BMC Genomics 9: 593.G. SzittyaS. MoxonDM SantosR. JingMPS Fevereiro2008High-throughput sequencing of Medicago truncatula short RNAs identifies eight new miRNA families.BMC Genomics9593
- 47. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ (2007) miRBase: tools for microRNA genomics. Nucleic Acids Research 36: D154–D158.S. Griffiths-JonesHK SainiS. van DongenAJ Enright2007miRBase: tools for microRNA genomics.Nucleic Acids Research36D154D158
- 48. Chen X (2009) Small RNAs and Their Roles in Plant Development. Annual Review of Cell and Developmental Biology 25: 21–44.X. Chen2009Small RNAs and Their Roles in Plant Development.Annual Review of Cell and Developmental Biology252144
- 49. Higgins D, Thompson J, Gibson T, Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Research 22: 4673–4680.D. HigginsJ. ThompsonT. GibsonJD ThompsonDG HigginsTJ Gibson1994CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice.Nucleic Acids Research2246734680
- 50. Krol J, Loedige I, Filipowicz W (2010) The widespread regulation of microRNA biogenesis, function and decay. Nature Reviews Genetics. J. KrolI. LoedigeW. Filipowicz2010The widespread regulation of microRNA biogenesis, function and decay.Nature Reviews Genetics
- 51. Dai X, Zhao PX (2011) psRNATarget: a plant small RNA target analysis server. Nucleic Acids Research 39: W155–W159.X. DaiPX Zhao2011psRNATarget: a plant small RNA target analysis server.Nucleic Acids Research39W155W159
- 52. Cheung WY (1980) Calmodulin plays a pivotal role in cellular regulation. Science 207(4426): 19–27.WY Cheung1980Calmodulin plays a pivotal role in cellular regulation.Science207(4426)1927
- 53. Jones-Rhoades MW (2011) Conservation and divergence in plant microRNAs. Plant Molecular Biology. MW Jones-Rhoades2011Conservation and divergence in plant microRNAs.Plant Molecular Biology
- 54. Chen L, Ren Y, Zhang Y, Xu J, Zhang Z, et al. (2011) Genome-wide profiling of novel and conserved Populus microRNAs involved in pathogen stress response by deep sequencing. Planta. L. ChenY. RenY. ZhangJ. XuZ. Zhang2011Genome-wide profiling of novel and conserved Populus microRNAs involved in pathogen stress response by deep sequencing.Planta
- 55. Kulcheski FR, de Oliveira LFV, Molina LG, Almerão MP, Rodrigues FA, et al. (2011) Identification of novel soybean microRNAs involved in abiotic and biotic stresses. BMC Genomics 12: 307.FR KulcheskiLFV de OliveiraLG MolinaMP AlmerãoFA Rodrigues2011Identification of novel soybean microRNAs involved in abiotic and biotic stresses.BMC Genomics12307
- 56. Wang T, Chen L, Zhao M, Tian Q, Zhang W–H (2011) Identification of drought-responsive microRNAs in Medicago truncatula by genome-wide high-throughput sequencing. BMC Genomics 12: 367.T. WangL. ChenM. ZhaoQ. TianW–H Zhang2011Identification of drought-responsive microRNAs in Medicago truncatula by genome-wide high-throughput sequencing.BMC Genomics12367
- 57. Zhai J, Jeong DH, De Paoli E, Park S, Rosen BD, et al. (2011) MicroRNAs as master regulators of the plant NB-LRR defense gene family via the production of phased, trans-acting siRNAs. Genes & Development 25: 2540–2553.J. ZhaiDH JeongE. De PaoliS. ParkBD Rosen2011MicroRNAs as master regulators of the plant NB-LRR defense gene family via the production of phased, trans-acting siRNAs.Genes & Development2525402553
- 58. Eitas TK, Dangl JL (2010) NB-LRR proteins: pairs, pieces, perception, partners, and pathways. Current Opinion in Plant Biology 13: 472–477.TK EitasJL Dangl2010NB-LRR proteins: pairs, pieces, perception, partners, and pathways.Current Opinion in Plant Biology13472477
- 59. Hewezi T, Howe P, Maier TR, Baum TJ (2008) Arabidopsis small RNAs and their targets during Cyst Nematode parasitism. MPMI 21(12): 1622–1634.T. HeweziP. HoweTR MaierTJ Baum2008Arabidopsis small RNAs and their targets during Cyst Nematode parasitism.MPMI21(12)16221634
- 60. Li X, Wang X, Zhang S, Liu D, Duan Y, et al. (2011) Comparative profiling of the transcriptional response to soybean cyst nematode infection of soybean roots by deep sequencing. Chinese Science Bulletin 56: 1904–1911.X. LiX. WangS. ZhangD. LiuY. Duan2011Comparative profiling of the transcriptional response to soybean cyst nematode infection of soybean roots by deep sequencing.Chinese Science Bulletin5619041911
- 61. Sunkar R (2004) Novel and Stress-Regulated MicroRNAs and Other Small RNAs from Arabidopsis. The Plant Cell Online 16: 2001–2019.R. Sunkar2004Novel and Stress-Regulated MicroRNAs and Other Small RNAs from Arabidopsis.The Plant Cell Online1620012019
- 62. Lv D–K, Bai X, Li Y, Ding X–D, Ge Y, et al. (2010) Profiling of cold-stress-responsive miRNAs in rice by microarrays. Gene 459: 39–47.D–K LvX. BaiY. LiX–D DingY. Ge2010Profiling of cold-stress-responsive miRNAs in rice by microarrays.Gene4593947
- 63. Thiebaut F, Rojas CA, Almeida KL, Grativol C, Domiciano GC, et al. (2011) Regulation of miR319 during cold stress in sugarcane. Plant, Cell & Environment. Gu M, Xu K, Chen A, Zhu Y, Tang G, et al. F. ThiebautCA RojasKL AlmeidaC. GrativolGC Domiciano2011Regulation of miR319 during cold stress in sugarcane. Plant, Cell & Environment.Gu M, Xu K, Chen A, Zhu Y, Tang G, et al(2010). (2010).
- 64. Expression analysis suggests potential roles of microRNAs for phosphate and arbuscular mycorrhizal signaling inSolanum lycopersicum. Physiologia Plantarum 138: 226–237.Expression analysis suggests potential roles of microRNAs for phosphate and arbuscular mycorrhizal signaling inSolanum lycopersicum.Physiologia Plantarum138226237
- 65. Li WX, Oono Y, Zhu J, He XJ, Wu JM, et al. (2008) The Arabidopsis NFYA5 Transcription Factor Is Regulated Transcriptionally and Posttranscriptionally to Promote Drought Resistance. The Plant Cell Online 20: 2238–2251.WX LiY. OonoJ. ZhuXJ HeJM Wu2008The Arabidopsis NFYA5 Transcription Factor Is Regulated Transcriptionally and Posttranscriptionally to Promote Drought Resistance.The Plant Cell Online2022382251
- 66. Zhou X, Wang G, Zhang W (2007) UV-B responsive microRNA genes in Arabidopsis thaliana. Molecular Systems Biology 3. X. ZhouG. WangW. Zhang2007UV-B responsive microRNA genes in Arabidopsis thaliana.Molecular Systems Biology 3
- 67. Zhao B, Ge L, Liang R, Li W, Ruan K, et al. (2009) Members of miR-169 family are induced by high salinity and transiently inhibit the NF-YA transcription factor. BMC Molecular Biology 10: 29.B. ZhaoL. GeR. LiangW. LiK. Ruan2009Members of miR-169 family are induced by high salinity and transiently inhibit the NF-YA transcription factor.BMC Molecular Biology1029
- 68. Marin E, Jouannet V, Herz A, Lokerse AS, Weijers D, et al. (2010) miR390, Arabidopsis TAS3 tasiRNAs, and Their AUXIN RESPONSE FACTOR Targets Define an Autoregulatory Network Quantitatively Regulating Lateral Root Growth. The Plant Cell Online 22: 1104–1117.E. MarinV. JouannetA. HerzAS LokerseD. Weijers2010miR390, Arabidopsis TAS3 tasiRNAs, and Their AUXIN RESPONSE FACTOR Targets Define an Autoregulatory Network Quantitatively Regulating Lateral Root Growth.The Plant Cell Online2211041117