Skip to main content

Identification of drought-responsive and novel Populus trichocarpamicroRNAs by high-throughput sequencing and their targets using degradome analysis



MicroRNAs (miRNAs) are endogenous small RNAs (sRNAs) with a wide range of regulatory functions in plant development and stress responses. Although miRNAs associated with plant drought stress tolerance have been studied, the use of high-throughput sequencing can provide a much deeper understanding of miRNAs. Drought is a common stress that limits the growth of plants. To obtain more insight into the role of miRNAs in drought stress, Illumina sequencing of Populus trichocarpa sRNAs was implemented.


Two sRNA libraries were constructed by sequencing data of control and drought stress treatments of poplar leaves. In total, 207 P. trichocarpa conserved miRNAs were detected from the two sRNA libraries. In addition, 274 potential candidate miRNAs were found; among them, 65 candidates with star sequences were chosen as novel miRNAs. The expression of nine conserved miRNA and three novel miRNAs showed notable changes in response to drought stress. This was also confirmed by quantitative real time polymerase chain reaction experiments. To confirm the targets of miRNAs experimentally, two degradome libraries from the two treatments were constructed. According to degradome sequencing results, 53 and 19 genes were identified as targets of conserved and new miRNAs, respectively. Functional analysis of these miRNA targets indicated that they are involved in important activities such as the regulation of transcription factors, the stress response, and lipid metabolism.


We discovered five upregulated miRNAs and seven downregulated miRNAs in response to drought stress. A total of 72 related target genes were detected by degradome sequencing. These findings reveal important information about the regulation mechanism of miRNAs in P. trichocarpa and promote the understanding of miRNA functions during the drought response.


MicroRNAs (miRNAs) are one of the most abundant classes of small RNAs (sRNAs) in plants and animals. These endogenous sRNAs were first identified in a metazoan called Caenorhabditis elegans in 1994 [1] and were subsequently identified in plants [2] and viruses [3]. MiRNAs are typically 21 nucleotides (nt) in length and play regulatory roles at the post-transcriptional level by repressing translation or directly degrading target message RNAs (mRNAs) [4]. Plant miRNA genes are first transcribed into primary miRNAs, and then processed into miRNA precursors with stem-loop structures by Dicer-like proteins. Finally, they are released into the cytoplasm by cleavage into an miRNA::miRNA* duplex from the nucleus [5]. The mature miRNAs join an RNA-induced silencing complex (RISC), and the RISC targets specific mRNAs and downregulates the expression of target mRNAs [6]. MiRNAs participate in various processes such as metabolism [7], growth [8], development [9, 10], biotic [11] and abiotic [1219] stress tolerance.

An increasing body of evidence indicates that miRNAs are involved in the plant drought stress response [1315, 17, 20, 21]. In Arabidopsis, four drought-responsive miRNAs (miR396, miR168, miR167, and miR171) have been identified by microarray analysis [12]. In tobacco, nine miRNAs strongly induced by drought stress have been experimentally identified, among which miR395 and miR169 are the two miRNAs most sensitive to drought stress [14]. In rice, 30 miRNAs have been identified as significantly down- or upregulated under drought stress using a microarray platform [13]. In Medicago truncatula (M. truncatula), Wang et al. (2011) mined drought-responsive miRNAs on a genome-wide scale using the Illumina sequencing technology; 22 members from four miRNA families and 10 members of six miRNA families were identified as up- and downregulated in response to drought, respectively [17]. Li et al. (2011) reported 104 upregulated and 27 downregulated miRNAs by Illumina sequencing and microarray profiling in Populus euphratica (P. euphratica) [15]. Furthermore, Qin et al. (2011) confirmed three upregulated and two downregulated mature miRNAs in response to drought using a RT-qPCR assay [16].

Environmental stressors due to climate change, especially drought stress, could make forests increasingly vulnerable to disease and die-offs [22]. Drought may have a profound effect on forest health [23]. With its modest genome size and rapid, widespread growth, P. trichocarpa was the first model forest species sequenced [24]. Lu et al. (2005) studied miRNAs in P. trichocarpa and identified stress-responsive and novel miRNAs by Sanger sequencing technology [25]. An additional 15 novel P. trichocarpa miRNAs were further identified by Klevebring et al. (2009) using the 454 sequencing method [26]. Further study is needed to elucidate the mechanism of regulation of P. trichocarpa miRNA in general and of drought-responsive miRNAs in particular.

Only 234 P. trichocarpa miRNA precursors are annotated in the miRBase (version 18.0) [27], compared to 581 and 635 for Oryza sativa and M. truncatula, respectively, two other model organisms. Since the genome size of P. trichocarpa (423 Mbp, JGI version 3.0) is similar to that of M. truncatula (approximately 454–526 Mbp) and rice (389 Mbp), the potential for identification of new, specific miRNAs in P. trichocarpa is great. In this context, high-throughput sequencing was used to identify non-conserved miRNAs and drought-responsive miRNAs with the new version of the poplar genome (version 2.0), which has not been used in previous research on P. trichocarpa. The targets of these conserved and novel miRNAs were predicted, and some of them were confirmed by degradome sequencing. We discussed the potential regulatory mechanism between miRNAs and their targets. This may help to unravel the mechanism of drought stress tolerance in P. trichocarpa and other plants.


Illumina sequencing of P. trichocarpaleaves under control and drought conditions

According to a previous study on the relative soil moisture content (RSMC) of water-deficient soil [15, 28], P. trichocarpa plants were subjected to control levels (RSMC, 70–75%) and drought levels (RSMC, 15–20%). The two libraries were sequenced by an Illumina sequencer, yielding 27,333,282 reads for the control library (CL) and 30,806,496 reads for the drought library (DL) (Additional file 1: S1). After removing the low-quality sequences and adapter sequences, 26,229,957 clean sequences in CL and 30,233,516 clean sequences in DL were obtained, comprising 2,730,022 and 2,834,584 unique sequences, respectively (Table 1).

Table 1 Sequencing of miRNAs in Populus plants

The size distribution of all unique sRNAs is summarized in Figure 1A. The displayed length of P. trichocarpa sRNA ranged from 16 to 27 nt, and the two major size classes were 24 nt (41.06% in CL and 43.94% in DL) and 21 nt (16.64% in CL and 16.05% in DL). This is in agreement with previous studies on sRNAs of P. trichocarpa [26] and M. truncatula [17] using high-throughput sequencing. To analyze the average abundance of each length between sRNAs of CL and DL further, we measured the ratio of raw and unique reads (Figure 1B). The redundancies of sRNAs varied widely in length, and the 20 and 21 nt sRNAs displayed the highest redundancies. The average ratio of redundant and unique sequences of sRNAs of the two libraries showed obvious changes in 21 nt sRNAs; the redundancy of DL was 37.16% greater than that of CL. This may be why drought stress strongly induced the expression of these 21 nt sRNAs; most conserved miRNAs belong to this group.

Figure 1

Sequence length distribution of P. trichocarpa sRNAs. The size distribution of all unique sRNAs of the two libraries is show in panel A. The ratio of redundant and unique sequences of sRNAs of the two libraries is show in panel B.

After genomic annotation of the P. trichocarpa sRNAs, small interfering RNA (siRNA) and miRNA with various important post-transcription regulating functions were the largest of our acquired sequences. The siRNA is a 22 to 24 nt double-strand RNA, each strand of which is 2 nt longer than the other on the 3’ end [31]. These aligned sequences might represent siRNA candidates. In total, deep sequencing obtained 577,393 and 956,979 siRNA candidates after the control and drought stress treatments, respectively (Additional file 1: file S1). Interestingly, the ratio of siRNA reads to all sRNAs reads increased sharply from 2.20% (CL) to 3.17% (DL). To obtain the annotation of known miRNAs, sRNAs were aligned to the miRBase 18.0 of P. trichocarpa. In total, 10,784,410 and 15,674,365 sequencing reads were identified as known poplar miRNAs in the two libraries. Thirty-four families from 207 known miRNAs were found, which accounted for about 87.3% of the total members. The remaining 30 miRNAs were not detected (Additional file 2: S2), possibly because of the tissue specificity of expression in poplar.

Novel non-conserved miRNAs from P. trichocarpa

After identifying potential siRNAs and conserved miRNAs from the unique sRNA sequences, the remaining sRNA sequences were potential candidate miRNAs. For the identification of new miRNA, the primary criterion was a stable hairpin structure. After pooling the reads of the two libraries and analyzing the precursors of potential miRNAs using the MFOLD web server, we found 274 potential candidate miRNAs (Additional file 3: S3 and Table 2). In compliance with the plant miRNA criteria of Meyers [32], only 65 miRNAs with star sequences from 54 families were chosen as novel non-conserved miRNAs. Of these, 47 miRNAs were 21 nt long, eight miRNAs were 22 nt, five miRNAs were 20 nt and the remaining five were 19 nt long. The nucleotide bias at the first nucleotide showed a tendency to be U (41% of 65 novel non-conserved miRNAs). This would allow for easier miRNA RISC loading assisted by AGO protein [33] and is consistent with the trend of conserved miRNAs in plants [34].

Table 2 New miRNAs in P. trichocarpa

These RNA structures were predicted by MFOLD software and manually checked according to the criteria of Meyers [32]. The lowest minimum free energy (MFE) of all hairpin structures of the novel miRNAs precursors was −31.9 kcal/mol (Table 2), which is slightly lower than the threshold of −30 kcal/mol reported in a previous study [35]. All precursors of novel miRNAs had regular hairpin structures (Additional file 4: S4), and four of these (Ptc-miRn5, Ptc-miRn11, Ptc-miRn38, and Ptc-miRn50) are presented in Figure 2.

Figure 2

Predicted miRNA precursor’ hairpin structures of new miRNA precursors. Precursor structures of 4 newly identified poplar miRNAs (miRn5, miRn11, miRn38, and miRn50) were predicted by MFOLD pipeline. The MEFs were list after the miRNAs name. The mature miRNA and miRNA star sequences are highlighted in red and blue, respectively.

Differential expression of miRNAs in P. trichocarpa

To identify drought-responsive miRNAs from P. trichocarpa, the number of normalized miRNA reads of CL and DL were compared. Based on the sequencing results, the differential expression of miRNAs greater than two-fold were chosen for experimental validation by quantitative real time polymerase chain reaction (RT-qPCR) (Additional file 5: S5). As shown in Figure 3, the expression patterns of the sequencing and RT-qPCR results of drought-responsive miRNAs were consistent, both indicating that four miRNAs (Ptc-miR159a-c, Ptc-miR472a, Ptc-miR472b, and Ptc-miR473a) were upregulated after drought treatment, and that five miRNAs (Ptc-miR160a-d, Ptc-miR164a-e, Ptc-miR394a/b-5p, Ptc-miR408, and Ptc-miR1444b-c) were downregulated by drought stress [25].

Figure 3

Differential expression analysis of conserved and novel drought-responsive miRNAs. The changes in miRNAs for CL and DL are greater than 2-fold. For each miRNA, sequence reads were divided by the total sequence number then multiplied to 1,000,000 (reads per million). Differential expression of known and new miRNAs in response to drought stress by sequencing is shown in panel A. The positive and negative values mean miRNAs whose expression was stimulated and suppressed by drought stress, respectively. ** mean significant difference between control and drought stress at P ≤ 0.01. The relative expression level of miRNAs measured by RT-qPCR in response to drought stress is shown in panel B.

We further analyzed the expressions of the 65 new miRNAs under the two treatments. The drought-responsive miRNAs are listed in Figure 3; all were confirmed by the sequencing and RT-qPCR results. Among the 65 miRNAs, two novel miRNAs (Ptc-miRn6a-d and Ptc-miRn16) were downregulated by drought stress, and only miRn5 was upregulated in response to drought stress (Additional file 5: S5).

Target analysis of novel and conserved miRNAs by degradome sequencing

The previously known miRNA targets also identified in this study are available on the PopGenIE site ( For new miRNAs whose targets were not known, we predicted their targets using the plant target prediction pipeline by the P. trichocarpa genome V2.0. The rules used for target prediction were based on those suggested by Allen et al. (2005) [36] and Schwab et al. (2005), as follows: (i) no more than four mismatches between the sRNA and the target (G-U bases count as 0.5 mismatches); (ii) no more than two adjacent mismatches in the miRNA/target duplex; (iii) no adjacent mismatches in positions 2–12 of the miRNA/target duplex (5’ of miRNA); (iv) no mismatches in positions 10–11 of the miRNA/target duplex; (v) no more than 2.5 mismatches in positions 1–12 of the miRNA/target duplex (5’ of miRNA); and (vi) the minimum free energy (MFE) of the miRNA/target duplex should be equal or greater than 74% of the MFE of the miRNA bound to its perfect complement [37]. We predicted 281 targets for 53 miRNA families; the other six were not found (Additional file 6: S6).

The verification of miRNA targets would provide further evidence for the existence of new non-conserved miRNAs. To identify the miRNA targets, two parallel analyses of RNA ends (PARE) libraries were constructed for the P. trichocarpa degradome sequencing. In particular, the sRNA-cleaved mRNAs ligated by 5 RNA adapters used for degradome sequencing acquired 23,326,117 and 34,398,368 reads (longer than 18 nt) in the mRNA libraries of the two treatments after removing redundancy; 108,593 and 234,316 unique reads could be matched to the P. trichocarpa genome (version 2.0) without mismatches (Additional file 7: S7) [38]. Fifty-three conserved and 19 new miRNA-targeted transcript pairs were confirmed by degradome sequencing. The target transcripts were pooled and categorized into three classes with reference to Arabidopsis[39]. Eleven pairs of miRNAs and their targets belonged to category I, which accounts for the most abundant sequence reads at the cleavage site. A total of 8 and 53 miRNAs and transcript pairs belonged to categories II and III, respectively. In addition, 13 target transcripts were predicted previously by either PopGenIE site or us (Table 3).

Table 3 Targets of P. trichocarpa miRNAs verified by degradome sequencing

Plant miRNAs have a strong propensity for target genes with important functions [34]. According to the biological functions described by UniProt (, these target transcripts can be grouped into nine categories. The majority of targets fall into the stress-response category, suggesting that these genes are drought-responsive (Table 4). Several other groups contain genes that regulate transcription, oxidative reduction, transport, and lipid metabolism. In this study, miR396 targeted a MYB transcription factor, and Ptc-miRn30 targeted an F-box family protein. The annotation of targets not only indicated some transcription factors and F-box proteins, but also some superoxide dismutases (SODs) and other proteins involved in glucose and lipid metabolism. A Cu-Zn SOD was targeted by Ptc-miRn49. All of these results indicate that miRNAs and their targets are reliable.

Table 4 Function category of the identified target transcripts


High-throughput sequencing of Populus

In a comparison of six Populus miRNA studies (Table 1) [11, 15, 25, 26, 29, 30], two used traditional Sanger sequencing [25, 29], two others used 454-pyrosequencing [26, 30], and the remaining two used the latest Illumina sequencing technology (as in the present study) [11, 15]. Along with the rapid development of sequencing technology, CL and DL can result in more sequences and greater sequencing depths than those reported in previous publications, due to the high throughput of the Illumina sequencer. In our study, because of the in-depth search, a large number of novel non-conserved miRNAs were found. The P. trichocarpa genome of Version 2.0 was used in this study; the transcript assemblies of the P. trichocarpa genome Version 2.0 are more meticulous than those of Version 1.1. This can increase the likelihood of finding more new miRNAs in general and drought-induced novel miRNAs in particular.

Novel miRNAs

Compared to six previous studies of Populus plants [10, 11, 15, 18, 19, 26], we identified 28 novel miRNAs have been identified (Table 2). Eleven of these were found at least once. On comparing the miRNA counts, 24 had counts greater than 100. Interestingly, two of the members of the Ptc-miRn54 family are the most frequently and robustly miRNAs identified in poplar high-throughput sequencing studies. Furthermore, the counterparts of Ptc-miRn40, Ptc-miRn52, Ptc-miRn54a, and Ptc-miRn54b in P. beijingensis were verified by RT-qPCR [11]. This provides more, strong evidence for the novel miRNAs identified from P. trichocarpa.

Drought-responsive miRNAs in P. trichocarpa

Although miRNAs have been shown to play an important role in the drought stress response of P. trichocarpa[25], little information on high-throughput sequencing of P. trichocarpa is available in this area. The present study on drought-responsive miRNAs from P. trichocarpa will improve the understanding of the drought response of this species. We identified nine conserved miRNAs and three novel miRNAs that show significant changes in response to drought stress. The results were confirmed by both high-throughput sequencing and RT-qPCR. To obtain more information, we compared the identified drought-responsive miRNAs with those identified in other studies (Table 5) [1215, 17, 21, 25, 4046]. MiR159 and miR164 have not yet found to be drought-responsive in Populus plants, except in this research. In addition, miR472, miR473, and miR1444 were found to be drought-responsive only in Populus plants, including in this study. The regulatory direction of four miRNAs (miR160, miR472, miR473 and miR408) was identical in P. tomentosa and our research, which might be due to their close genetic relationship.

Table 5 MiRNAs responsive to drought stress in diverse plant species

We further studied the target genes of these drought-responsive miRNAs by sequencing of the degradome library and comparing our work to previous studies [25, 29]. We found two upregulated miRNAs (Ptc-miR472 and Ptc-miRn5) that were both predicted to target putative disease resistance proteins in P. trichocarpa (Additional file 5: S5) [25]. The cross adaptation between disease resistance and drought stress tolerance in plants exists through unknown mechanisms. Ptc-miR159 is another upregulated miRNA; its Arabidopsis homolog targets an MYB transcription factor. The ABA-induced accumulation of the miR159 homolog makes the MYB transcript degradation desensitize hormone signaling during seedling stress responses in Arabidopsis [40]. According to our degradome sequencing results, the Ptc-miR159 was confirmed to target a methionine sulfoxide reductase (MSR). The homologs of MSR were induced by biotic and abiotic stresses in plants [4750]. They catalyze the reduction of methionine sulfoxide to methionine [47] and play a major role in regulating the accumulation of reactive oxygen species (ROS), which can damage proteins in plant cells [50]. Regulation of the MSR gene by Ptc-miR159 may occur through a homeostatic mechanism in response to drought stress in P. trichocarpa.

Ptc-miR473 was also upregulated in drought stress. It targets a member of a plant-specific GRAS transcription factor gene family [29]. Another member of this family (PeSCL7) from P. euphratica was confirmed to play key roles in salt and drought stress tolerance [51]. In the present study, Ptc-miR473 was confirmed to be targeted to Vein Patterning 1 (VEP1), which belongs to a short-chain dehydrogenase/reductase (SDR) superfamily [52]. The homolog of VEP1 in Arabidopsis was confirmed to be required for vascular strand development and to be upregulated by osmotic stress [52, 53]. Ptc-miR473 regulates the expression the GRAS protein and VEP1, both of which were responsive to drought stress, this may be the drought tolerance mechanism in P. trichocarpa.

The number of downregulated miRNAs was larger than the number of upregulated miRNAs. The two downregulated miRNAs (miR160 and miR164) were both identified to be cold-responsive miRNAs in P. trichocarpa [25]. TMV-Cg virus infection in Arabidopsis causes the accumulation of miR160 and miR164 [54]. Three auxin responsive factor (ARF) genes (ARF10, ARF16, and ARF17) are the targets of miR160 [55]. Repression of ARF10 by miR160 is critical for the seed germination and post-germination stages [56]. MiR164 has been predicted targete six NAC-domain proteins (PNAC041, PNAC042, PNAC151, PNAC152, PNAC154, and PNAC155) from subfamily NAC-a [57], and NAC-domain proteins have been confirmed to be important in drought stress tolerance [58, 59]. These mechanisms may also be at work in drought-stress tolerance in P. trichocarpa for these two miRNAs.

Two downregulated miRNAs (Ptc-miR408 and Ptc-miR1444) have been reported to be Cu-responsive miRNAs in P. trichocarpa. Their targets include miR408-targeted plastocyanin-like proteins and miR1444-targeted all plastid polyphenol oxidases [60, 61]. Drought treatment may increase the relative concentration of Cu ion in the cytoplasm. When the Cu supply is sufficient, it is envisaged that the conjunction between mature miRNAs and their precursors will be suppressed, leading to the upregulation of miRNA-targeted Cu proteins [60]. Accordingly, the balance of Cu ion contributes to the healthy growth and development of poplars during stress. In P. trichocarpa, Ptc-miR1444a is reportedly downregulated by dehydration [25], and Ptc-1444b/c was also found to be downregulated by drought in this study. MiR408 is reportedly downregulated by drought stress in rice [13] and has been experimentally identified to target an early responsive dehydration-related (ERD) protein in P. trichocarpa. Drought stress might induce the expression of ERD protein by downregulating the expression of miR408 in P. trichocarpa. This may be one of the mechanisms of regulation of drought-stress tolerance [25].

Other downregulated miRNA is Ptc-miR394, whose predicted targets are annotated as dehydration-responsive protein (POPTR_0002s07760.1) and F-box proteins (POPTR_0001s13770.1 and POPTR_0003s16980.1), which were recently reported to be differentially regulated by stress conditions and to play significant roles in the abiotic stress-response pathway. In Arabidopsis, salt-induced miR394 targets the mRNA of F-box proteins [12, 56].

From the analysis of predicted targets to downregulated Ptc-miRn6, a CCCH-type zinc finger protein and two trichome birefringence-like proteins (TBLPs) were functionally predicted. Although a cotton CCCH-type zinc finger protein has been identified to enhance abiotic stress tolerance in tobacco [62], we did not find any additional possible regulatory mechanisms between CCCH-type zinc finger protein and drought tolerance in P. trichocarpa. The homolog of TBLP in Arabidopsis is important to the formation of crystalline cellulose in trichomes [63]. As previous studies have reported, trichome density increases with water shortage [64], and the thick trichome layer could prevent water loss [65]. This may be the mechanism by which miRn6 regulates the expression of TBLP to adapt to drought stress.

Degradome analysis of non-drought-responsive miRNAs

In Arabidopsis, miR390 was reported to target TAS genes [66], while in P. trichocarpa, no TAS homologs have been found [26]. From our study, the degradome sequencing data proved the adjustment mechanism of Ptc-miR390 and lipoxygenases (LOXs). The activity of LOX protein can partially reduce the production of radicals and ROS [67]. This may explain the regulatory mechanism of miR390 in poplars. Four UDPGs were found to be targeted by Ptc-miR482, and all were classified as category I. The UDP-glucosyltransferases (UDPGs) are enzymes that attach a sugar molecule to a specific acceptor in plants [68]. As in Arabidopsis, the UDPG is a key regulator of stress adaption through auxin IBA [69] and plays a role in fine-tuning nitrogen assimilation in cassava [70]. This is a novel mechanism by which miR482 regulates the UDPG gene family in P. trichocarpa.

The degradome sequencing results imply that the miRNAs with no detected targets may silence genes by repressing translation. However, we could not obtain information about translation repression by miRNA through degradome sequencing. Only 19 targets of new miRNAs were identified. The targets of these non-conserved miRNAs are difficult to detect, possibly because of low abundance or a spatial expression pattern. More studies are needed to shed light on the regulation network of these miRNAs in P. trichocarpa. Over-expressing or repressing expression of these miRNAs in P. trichocarpa may help to elucidate the regulation mechanism.


In this study, sRNA libraries and degradome libraries of control and drought treatments were constructed with poplar leaves for high-throughput sequencing. Twelve miRNA members in 11 families were confirmed to be responsive to drought stress, and 65 novel miRNAs with star sequences of 59 families were identified. Through degradome sequencing, 53 and 19 genes were identified as cleavage targets of annotated miRNAs and new miRNAs, respectively. The functions of miRNA targets were analyzed and discussed. This study provides useful information for further analysis of plant miRNAs and drought stress tolerance, particularly in Populus plants.


Plant materials and total RNA extraction

P. trichocarpa seedlings of the same size (~5 cm) from tissue culture were planted in individual pots (15 L) containing loam soil and placed in a greenhouse at Beijing Forestry University. They were well irrigated and grown under control conditions (25°C day/20°C night, 16-h photoperiod) for three months, the heights of them were about 45 cm. During the period of drought-stress treatment, P. trichocarpa seedlings were sustained at two RSMC levels (70–75% and 15–20%) for 1 month according to a previous publication [28]. The mature leaves were used as drought materials. Mature leaves from soil with sufficient irrigation (RSMC at 70–75%) were used as a control, and a relatively modest dehydration level (RSMC at 15–20%) was chosen for the drought treatment. Each treatment contained three repeat individuals. Leaf water potential (WP) was measured by PsyPro WP data logger (Wescor) (Additional file 8: S9). Photosynthetic rate, water conductance, intercellular CO2 concentration, and transpiration rate were measured by Li-6400 Photosynthesis System (Li-Cor) (Additional file 9: S10). For material harvest, mature leaves from the same position of different individual plants were collected and frozen immediately in liquid nitrogen for RNA extraction. The total RNA was extracted by the standard CTAB method for plants [71]. Then they were used for sequencing and RT-qPCR.

High-throughput sequencing and bioinformatics analysis

Illumina sequencing on sRNAs (ranged from 18 nt to 30 nt) was conducted using an Illumina Genome Analyzer, following the Illumina protocol [72]. After removing contaminants, low-quality sequences, and <18 nt sequences, clean reads were obtained and aligned against the P. trichocarpa genome (version 2.0) using SOAP software [73]. tRNA, rRNA, snRNA, snoRNA, and some other repeat sequences were removed from the sequences with a perfect match to the genome through a search of the NCBI Genbank and Rfam databases [74]. The remaining unique sequences were divided into known miRNAs and candidate miRNAs by alignment with the miRbase 18.0 [75]. The candidate miRNAs were further analyzed by MFOLD software on the RNA secondary structure of the miRNA::miRNA* and pre-miRNA hairpin energy [76]. Parameters were set to meet the criteria of plants [32].

Differentiatial expression analysis of miRNAs between the two treatments

The sequence reads of the two libraries were normalized to 1 million by the total number of sRNA reads in each sample. The calculation of the p-value for comparison of the miRNA expression between the two libraries was based on previously established methods [77, 78]. Specifically, the log2 ratio formula was: log2 ratio = log2 (miRNA reads in drought treatment/miRNA reads in control).

The following p-value formulae were used:

p x y = N 2 N 1 y x + y ! x ! y ! 1 + N 2 N 1 x + y + 1 ; p = min k = 0 k y p k x , k = y p k x .

where N1 is the total number of reads in the sequencing library of the control, N2 is the total number of reads in the sequencing library of the drought treatment, x is the number of reads for an miRNA in the control library, and y is the number of reads for an miRNA in the drought treatment library.

All calculations were performed on a BGI Bio-Cloud Computing platform ( Normalized miRNAs of <1 were filtered in both libraries.

RT-qPCR of mature miRNAs

To validate the results of miRNAs from high-throughput sequencing, RT-qPCR was performed. The RNAs were extracted from leaves using the CTAB method [71]. A poly (A) was added to the 3’ end, and reverse transcription was begun. In particular, a known sequence at the 5’ end of the oligo-dT primer was designed to be a communal reverse primer of the RT-qPCR. The One Step Prime-Script miRNA cDNA Synthesis Kit and SYBR Premix ExTag II (TaKaRa) were used. All primers used in this study are listed in Additional file 10: S8. The 5.8S ribosomal RNA was used as the internal control [25]. RT-qPCR was performed using an ABI StepOnePlus instrument. Calculation of RT-qPCR results were revised as follow: Sample cycle threshold (Ct) values were determined and then standardized based on the 5.8S gene control primer reaction, and the 2-ΔΔCT method was applied to calculate the relative changes in gene expression from RT-qPCR experiments [79].

Target prediction and confirmation by degradome sequencing

New P. trichocarpa miRNA targets were predicted as described before [36, 8082]. During the prediction, a penalty score (alignment score) criterion was induced according to the alignment between the miRNA and its potential target. Our cut-off values in both prediction and degradome sequencing data analysis were also set to <2.5 as used in previous studies on poplar miRNA target prediction. The biological function of the predicted targets was retrieved from the Universal Protein Resource (

Degradome sequencing following the PARE protocol was used [38]. Only miRNA-cleaved mRNA and other degraded mRNA could be ligated by a 5’ RNA adapter because the 5’-phosphate and intact mRNAs were protected by the 5’ cap. First, adapters and low-quality nucleotide reads were removed from raw reads using the Fastx-Toolkit. Then the clean reads were further analyzed by Cleaveland 2.0 software [83]. Briefly, the reads were first mapped to the P. trichocarpa transcripts database from JGI Phytozome 2.0. At this step, a target plot was also created to distinguish the true miRNA cleavage site from background noise. We ran Cleaveland 2.0 with default parameters using 100 randomized sequencing shuffles. The NCBI database was used to predict functions of targets that were not annotated in JGI Phytozome 2.0. The cleaved target transcripts were categorized into three categories according to the following criteria: I, the abundance of reads in its cleavage site is the maximum on the transcript; II, the abundance of reads in its cleavage site is not the maximum, but is equal to or higher than the median for the transcript; and III, the abundance of reads in its cleavage site is less than the median for the transcript.




P. trichocarpa:

Populus trichocarpa


Small RNA


RNA-induced silencing complex


Message RNA

M. truncatula:

Medicago truncatula

P. euphratica:

Populus euphratica


Control library


Drought library


Small interfering RNA


Minimum free energy


Quantitative real time polymerase chain reaction


Superoxide dismutase

P. beijingensis:

Populus beijingensis


Methionine sulfoxide reductase


Vein Patterning 1


Auxin responsive factor






Relative soil moisture content


Parallel analysis of RNA ends


Trichome birefringence-like protein


Early responsive dehydration-related.


  1. 1.

    Lee RC, Feinbaum RL, Ambros V: The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993, 75 (5): 843-854. 10.1016/0092-8674(93)90529-Y.

    Article  CAS  PubMed  Google Scholar 

  2. 2.

    Llave C, Xie Z, Kasschau KD, Carrington JC: Cleavage of scarecrow-like mRNA targets directed by a class of Arabidopsis miRNA. Science. 2002, 297 (5589): 2053-2056. 10.1126/science.1076311.

    Article  CAS  PubMed  Google Scholar 

  3. 3.

    Pfeffer S, Zavolan M, Grasser FA, Chien MC, Russo JJ, Ju JY, John B, Enright AJ, Marks D, Sander C: Identification of virus-encoded microRNAs. Science. 2004, 304 (5671): 734-736. 10.1126/science.1096781.

    Article  CAS  PubMed  Google Scholar 

  4. 4.

    Kurihara Y, Watanabe Y: Arabidopsis micro-RNA biogenesis through Dicer-like 1 protein functions. P Natl Acad Sci USA. 2004, 101 (34): 12753-12758. 10.1073/pnas.0403115101.

    Article  CAS  Google Scholar 

  5. 5.

    Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004, 116 (2): 281-297. 10.1016/S0092-8674(04)00045-5.

    Article  CAS  PubMed  Google Scholar 

  6. 6.

    Baumberger N, Baulcombe DC: Arabidopsis ARGONAUTE1 is an RNA slicer that selectively recruits microRNAs and short interfering RNAs. P Natl Acad Sci USA. 2005, 102 (33): 11928-11933. 10.1073/pnas.0505461102.

    Article  CAS  Google Scholar 

  7. 7.

    Valdes-Lopez O, Arenas-Huertero C, Ramirez M, Girard L, Sanchez F, Vance CP, Reyes JL, Hernandez G: Essential role of MYB transcription factor: PvPHR1 and microRNA: PvmiR399 in phosphorus-deficiency signalling in common bean roots. Plant Cell Environ. 2008, 31 (12): 1834-1843. 10.1111/j.1365-3040.2008.01883.x.

    Article  CAS  PubMed  Google Scholar 

  8. 8.

    Guo HS, Xie Q, Fei JF, Chua NH: MicroRNA directs mRNA cleavage of the transcription factor NAC1 to downregulate auxin signals for Arabidopsis lateral root development. Plant Cell. 2005, 17 (5): 1376-1386. 10.1105/tpc.105.030841.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  9. 9.

    Aukerman MJ, Sakai H: Regulation of flowering time and floral organ identity by a microRNA and its APETALA2-like target genes. Plant Cell. 2003, 15 (11): 2730-2741. 10.1105/tpc.016238.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  10. 10.

    Puzey JR, Karger A, Axtell M, Kramer EM: Deep Annotation of Populus trichocarpa microRNAs from Diverse Tissue Sets. PLoS One. 2012, 7 (3): e33034-10.1371/journal.pone.0033034.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  11. 11.

    Chen L, Ren Y, Zhang Y, Xu J, Zhang Z, Wang Y: Genome-wide profiling of novel and conserved Populus microRNAs involved in pathogen stress response by deep sequencing. Planta. 2012, 235 (5): 873-883. 10.1007/s00425-011-1548-z.

    Article  CAS  PubMed  Google Scholar 

  12. 12.

    Liu HH, Tian X, Li YJ, Wu CA, Zheng CC: Microarray-based analysis of stress-regulated microRNAs in Arabidopsis thaliana. RNA. 2008, 14 (5): 836-843. 10.1261/rna.895308.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  13. 13.

    Zhou LG, Liu YH, Liu ZC, Kong DY, Duan M, Luo LJ: Genome-wide identification and analysis of drought-responsive microRNAs in Oryza sativa. J Exp Bot. 2010, 61 (15): 4157-4168. 10.1093/jxb/erq237.

    Article  CAS  PubMed  Google Scholar 

  14. 14.

    Frazier TP, Sun GL, Burklew CE, Zhang BH: Salt and drought stresses induce the aberrant expression of microRNA genes in tobacco. Mol Biotechnol. 2011, 49 (2): 159-165. 10.1007/s12033-011-9387-5.

    Article  CAS  PubMed  Google Scholar 

  15. 15.

    Li BS, Qin YR, Duan H, Yin WL, Xia XL: Genome-wide characterization of new and drought stress responsive microRNAs in Populus euphratica. J Exp Bot. 2011, 62 (11): 3765-3779. 10.1093/jxb/err051.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  16. 16.

    Qin YR, Duan ZX, Xia XL, Yin WL: Expression profiles of precursor and mature microRNAs under dehydration and high salinity shock in Populus euphratica. Plant Cell Rep. 2011, 30 (10): 1893-1907. 10.1007/s00299-011-1096-9.

    Article  CAS  PubMed  Google Scholar 

  17. 17.

    Wang TZ, Chen L, Zhao MG, Tian QY, Zhang WH: Identification of drought-responsive microRNAs in Medicago truncatula by genome-wide high-throughput sequencing. BMC Genomics. 2011, 12: 367-10.1186/1471-2164-12-367.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  18. 18.

    Chen L, Ren Y, Zhang Y, Xu J, Sun F, Zhang Z, Wang Y: Genome-wide identification and expression analysis of heat-responsive and novel microRNAs in Populus tomentosa. Gene. 2012, 504 (2): 160-165. 10.1016/j.gene.2012.05.034.

    Article  CAS  PubMed  Google Scholar 

  19. 19.

    Chen L, Zhang Y, Ren Y, Xu J, Zhang Z, Wang Y: Genome-wide identification of cold-responsive and new microRNAs in Populus tomentosa by high-throughput sequencing. Biochem Biophys Res Commun. 2012, 417 (2): 892-896. 10.1016/j.bbrc.2011.12.070.

    Article  CAS  PubMed  Google Scholar 

  20. 20.

    Monclus R, Dreyer E, Villar M, Delmotte FM, Delay D, Petit JM, Barbaroux C, Thiec D, Brechet C, Brignolas F: Impact of drought on productivity and water use efficiency in 29 genotypes of Populus deltoides x Populus nigra. New Phytol. 2006, 169 (4): 765-777. 10.1111/j.1469-8137.2005.01630.x.

    Article  PubMed  Google Scholar 

  21. 21.

    Ren Y, Chen L, Zhang Y, Kang X, Zhang Z, Wang Y: Identification of novel and conserved Populus tomentosa microRNA as components of a response to water stress. Funct Integr Genomic. 2012, 12 (2): 327-339. 10.1007/s10142-012-0271-6.

    Article  CAS  Google Scholar 

  22. 22.

    Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Kitzberger T, Rigling A, Breshears DD, Hogg EH: A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecol Manage. 2010, 259 (4): 660-684. 10.1016/j.foreco.2009.09.001.

    Article  Google Scholar 

  23. 23.

    Hamanishi ET, Campbell MM: Genome-wide responses to drought in forest trees. Forestry. 2011, 84 (3): 273-283. 10.1093/forestry/cpr012.

    Article  Google Scholar 

  24. 24.

    Tuskan GA, DiFazio S, Jansson S, Bohlmann J, Grigoriev I, Hellsten U, Putnam N, Ralph S, Rombauts S, Salamov A: The genome of black cottonwood, Populus trichocarpa (Torr. & Gray). Science. 2006, 313 (5793): 1596-1604. 10.1126/science.1128691.

    Article  CAS  PubMed  Google Scholar 

  25. 25.

    Lu S, Sun YH, Chiang VL: Stress-responsive microRNAs in Populus. Plant J. 2008, 55 (1): 131-151. 10.1111/j.1365-313X.2008.03497.x.

    Article  CAS  PubMed  Google Scholar 

  26. 26.

    Klevebring D, Street NR, Fahlgren N, Kasschau KD, Carrington JC, Lundeberg J, Jansson S: Genome-wide profiling of Populus small RNAs. BMC Genom. 2009, 10: 620-10.1186/1471-2164-10-620.

    Article  Google Scholar 

  27. 27.

    Kozomara A, Griffiths-Jones S: miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 2011, 39: D152-D157. 10.1093/nar/gkq1027.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  28. 28.

    Hasio T: Plant responses to water stress. Annu Rev Plant Physiol Plant Mol Biol. 1973, 24: 519-570. 10.1146/annurev.pp.24.060173.002511.

    Article  Google Scholar 

  29. 29.

    Lu S, Sun YH, Shi R, Clark C, Li L, Chiang VL: Novel and mechanical stress-responsive microRNAs in Populus trichocarpa that are absent from Arabidopsis. Plant Cell. 2005, 17 (8): 2186-2203. 10.1105/tpc.105.033456.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  30. 30.

    Barakat A, Wall PK, Diloreto S, Depamphilis CW, Carlson JE: Conservation and divergence of microRNAs in Populus. BMC Genom. 2007, 8: 481-10.1186/1471-2164-8-481.

    Article  Google Scholar 

  31. 31.

    Hamilton AJ, Baulcombe DC: A species of small antisense RNA in posttranscriptional gene silencing in plants. Science. 1999, 286 (5441): 950-952. 10.1126/science.286.5441.950.

    Article  CAS  PubMed  Google Scholar 

  32. 32.

    Meyers BC, Axtell MJ, Bartel B, Bartel DP, Baulcombe D, Bowman JL, Cao X, Carrington JC, Chen X, Green PJ: Criteria for annotation of plant microRNAs. Plant Cell. 2008, 20 (12): 3186-3190. 10.1105/tpc.108.064311.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  33. 33.

    Mi SJ, Cai T, Hu YG, Chen Y, Hodges E, Ni FR, Wu L, Li S, Zhou H, Long CZ: Sorting of small RNAs into Arabidopsis argonaute complexes is directed by the 5 ’ terminal nucleotide. Cell. 2008, 133 (1): 116-127. 10.1016/j.cell.2008.02.034.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  34. 34.

    Voinnet O: Origin, biogenesis, and activity of plant microRNAs. Cell. 2009, 136 (4): 669-687. 10.1016/j.cell.2009.01.046.

    Article  CAS  PubMed  Google Scholar 

  35. 35.

    Bonnet E, Wuyts J, Rouze P, Van de Peer Y: Evidence that microRNA precursors, unlike other non-coding RNAs, have lower folding free energies than random sequences. Bioinformatics. 2004, 20 (17): 2911-2917. 10.1093/bioinformatics/bth374.

    Article  CAS  PubMed  Google Scholar 

  36. 36.

    Allen E, Xie Z, Gustafson AM, Carrington JC: MicroRNA-directed phasing during trans-acting siRNA biogenesis in plants. Cell. 2005, 121 (2): 207-221. 10.1016/j.cell.2005.04.004.

    Article  CAS  PubMed  Google Scholar 

  37. 37.

    Schwab R, Palatnik JF, Riester M, Schommer C, Schmid M, Weigel D: Specific effects of microRNAs on the plant transcriptome. Dev Cell. 2005, 8 (4): 517-527. 10.1016/j.devcel.2005.01.018.

    Article  CAS  PubMed  Google Scholar 

  38. 38.

    German MA, Luo SJ, Schroth G, Meyers BC, Green PJ: Construction of Parallel Analysis of RNA Ends (PARE) libraries for the study of cleaved miRNA targets and the RNA degradome. Nat Protoc. 2009, 4 (3): 356-362. 10.1038/nprot.2009.8.

    Article  CAS  PubMed  Google Scholar 

  39. 39.

    Addo-Quaye C, Eshoo TW, Bartel DP, Axtell MJ: Endogenous siRNA and miRNA targets identified by sequencing of the Arabidopsis degradome. Curr Biol. 2008, 18 (10): 758-762. 10.1016/j.cub.2008.04.042.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  40. 40.

    Reyes JL, Chua NH: ABA induction of miR159 controls transcript levels of two MYB factors during Arabidopsis seed germination. Plant J. 2007, 49 (4): 592-606. 10.1111/j.1365-313X.2006.02980.x.

    Article  CAS  PubMed  Google Scholar 

  41. 41.

    Sun GL, Stewart CN, Xiao P, Zhang BH: MicroRNA Expression Analysis in the Cellulosic Biofuel Crop Switchgrass (Panicum virgatum) under Abiotic Stress. PLoS One. 2012, 7 (3):

  42. 42.

    Ni ZY, Hu Z, Jiang QY, Zhang H: Overexpression of gma-MIR394a confers tolerance to drought in transgenic Arabidopsis thaliana. Biochem Biophys Res Commun. 2012, 427 (2): 330-335. 10.1016/j.bbrc.2012.09.055.

    Article  CAS  PubMed  Google Scholar 

  43. 43.

    Kantar M, Unver T, Budak H: Regulation of barley miRNAs upon dehydration stress correlated with target gene expression. Funct Integr Genom. 2010, 10 (4): 493-507. 10.1007/s10142-010-0181-4.

    Article  CAS  Google Scholar 

  44. 44.

    Ferreira TH, Gentile A, Vilela RD, Costa GGL, Dias LI, Endres L, Menossi M: microRNAs associated with drought response in the bioenergy crop sugarcane (Saccharum spp.). PLoS One. 2012, 7 (10): e46703-10.1371/journal.pone.0046703.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  45. 45.

    Barrera-Figueroa BE, Gao L, Wu ZG, Zhou XF, Zhu JH, Jin HL, Liu RY, Zhu JK: High throughput sequencing reveals novel and abiotic stress-regulated microRNAs in the inflorescences of rice. BMC Plant Biol. 2012, 12: 132-10.1186/1471-2229-12-132.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  46. 46.

    Trindade I, Capitao C, Dalmay T, Fevereiro MP, dos Santos DM: miR398 and miR408 are up-regulated in response to water deficit in Medicago truncatula. Planta. 2010, 231 (3): 705-716. 10.1007/s00425-009-1078-0.

    Article  CAS  PubMed  Google Scholar 

  47. 47.

    Oh JE, Hong SW, Lee Y, Koh EJ, Kim K, Seo YW, Chung N, Jeong M, Jang CS, Lee B: Modulation of gene expressions and enzyme activities of methionine sulfoxide reductases by cold, ABA or high salt treatments in Arabidopsis. Plant Sci. 2005, 169 (6): 1030-1036. 10.1016/j.plantsci.2005.05.033.

    Article  CAS  Google Scholar 

  48. 48.

    Oh SK, Baek KH, Seong ES, Joung YH, Choi GJ, Park JM, Cho HS, Kim EA, Lee S, Choi D: CaMsrB2, Pepper Methionine Sulfoxide Reductase B2, Is a Novel Defense Regulator against Oxidative Stress and Pathogen Attack. Plant Physiol. 2010, 154 (1): 245-261. 10.1104/pp.110.162339.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  49. 49.

    Guo XL, Wu YR, Wang YQ, Chen YM, Chu CC: OsMSRA4.1 and OsMSRB1.1, two rice plastidial methionine sulfoxide reductases, are involved in abiotic stress responses. Planta. 2009, 230 (1): 227-238. 10.1007/s00425-009-0934-2.

    Article  CAS  PubMed  Google Scholar 

  50. 50.

    Romero HM, Berlett BS, Jensen PJ, Pell EJ, Tien M: Investigations into the role of the plastidial peptide methionine sulfoxide reductase in response to oxidative stress in Arabidopsis. Plant Physiol. 2004, 136 (3): 3784-3794. 10.1104/pp.104.046656.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  51. 51.

    Ma HS, Liang D, Shuai P, Xia XL, Yin WL: The salt- and drought-inducible poplar GRAS protein SCL7 confers salt and drought tolerance in Arabidopsis thaliana. J Exp Bot. 2010, 61 (14): 4011-4019. 10.1093/jxb/erq217.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  52. 52.

    Herl V, Fischer G, Reva VA, Stiebritz M, Muller YA, Muller-Uri F, Kreis W: The VEP1 gene (At4g24220) encodes a short-chain dehydrogenase/reductase with 3-OXO-Delta(4,5)-steroid 5 beta-reductase activity in Arabidopsis thaliana L. Biochimie. 2009, 91 (4): 517-525. 10.1016/j.biochi.2008.12.005.

    Article  CAS  PubMed  Google Scholar 

  53. 53.

    Jun JH, Ha CM, Nam HG: Involvement of the VEP1 gene in vascular strand development in Arabidopsis thaliana. Plant Cell Physiol. 2002, 43 (3): 323-330. 10.1093/pcp/pcf042.

    Article  CAS  PubMed  Google Scholar 

  54. 54.

    Tagami Y, Inaba N, Kutsuna N, Kurihara Y, Watanabe Y: Specific enrichment of miRNAs in Arabidopsis thaliana infected with Tobacco mosaic virus. DNA Res. 2007, 14 (5): 227-233. 10.1093/dnares/dsm022.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  55. 55.

    Wang JW, Wang LJ, Mao YB, Cai WJ, Xue HW, Chen XY: Control of root cap formation by microRNA-targeted auxin response factors in Arabidopsis. Plant Cell. 2005, 17 (8): 2204-2216. 10.1105/tpc.105.033076.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  56. 56.

    Liu PP, Montgomery TA, Fahlgren N, Kasschau KD, Nonogaki H, Carrington JC: Repression of AUXIN RESPONSE FACTOR10 by microRNA160 is critical for seed germination and post-germination stages. Plant J. 2007, 52 (1): 133-146. 10.1111/j.1365-313X.2007.03218.x.

    Article  CAS  PubMed  Google Scholar 

  57. 57.

    Hu RB, Qi GA, Kong YZ, Kong DJ, Gao QA, Zhou GK: Comprehensive Analysis of NAC Domain Transcription Factor Gene Family in Populus trichocarpa. BMC Plant Biol. 2010, 10: 145-10.1186/1471-2229-10-145.

    PubMed Central  Article  PubMed  Google Scholar 

  58. 58.

    Jeong JS, Kim YS, Baek KH, Jung H, Ha SH, Do Choi Y, Kim M, Reuzeau C, Kim JK: Root-specific expression of OsNAC10 improves drought tolerance and grain yield in rice under field drought conditions. Plant Physiol. 2010, 153 (1): 185-197. 10.1104/pp.110.154773.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  59. 59.

    Hu H, Dai M, Yao J, Xiao B, Li X, Zhang Q, Xiong L: Overexpressing a NAM, ATAF, and CUC (NAC) transcription factor enhances drought resistance and salt tolerance in rice. P Natl Acad Sci USA. 2006, 103 (35): 12987-12992. 10.1073/pnas.0604882103.

    Article  CAS  Google Scholar 

  60. 60.

    Lu S, Yang C, Chiang VL: Conservation and diversity of microRNA-associated copper-regulatory networks in Populus trichocarpa. J Integr Plant Biol. 2011, 53 (11): 879-891. 10.1111/j.1744-7909.2011.01080.x.

    Article  CAS  PubMed  Google Scholar 

  61. 61.

    Ravet K, Danford FL, Dihle A, Pittarello M, Pilon M: Spatiotemporal analysis of copper homeostasis in Populus trichocarpa reveals an integrated molecular remodeling for a preferential allocation of copper to plastocyanin in the chloroplasts of developing leaves. Plant Physiol. 2011, 157 (3): 1300-1312. 10.1104/pp.111.183350.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  62. 62.

    Guo YH, Yu YP, Wang D, Wu CA, Yang GD, Huang JG, Zheng CC: GhZFP1, a novel CCCH-type zinc finger protein from cotton, enhances salt stress tolerance and fungal disease resistance in transgenic tobacco by interacting with GZIRD21A and GZIPR5. New Phytol. 2009, 183 (1): 62-75. 10.1111/j.1469-8137.2009.02838.x.

    Article  CAS  PubMed  Google Scholar 

  63. 63.

    Bischoff V, Nita S, Neumetzler L, Schindelasch D, Urbain A, Eshed R, Persson S, Delmer D, Scheible WR: TRICHOME BIREFRINGENCE and its homolog AT5G01360 encode plant-specific DUF231 proteins required for cellulose biosynthesis in Arabidopsis. Plant Physiol. 2010, 153 (2): 590-602. 10.1104/pp.110.153320.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  64. 64.

    Gonzalez WL, Negritto MA, Suarez LH, Gianoli E: Induction of glandular and non-glandular trichomes by damage in leaves of Madia sativa under contrasting water regimes. Acta Oecol. 2008, 33 (1): 128-132. 10.1016/j.actao.2007.10.004.

    Article  Google Scholar 

  65. 65.

    Bacelar EA, Correia CM, Moutinho-Pereira JM, Goncalves BC, Lopes JI, Torres-Pereira JMG: Sclerophylly and leaf anatomical traits of five field-grown olive cultivars growing under drought conditions. Tree Physiol. 2004, 24 (2): 233-239. 10.1093/treephys/24.2.233.

    Article  PubMed  Google Scholar 

  66. 66.

    Felippes FF, Weigel D: Triggering the formation of tasiRNAs in Arabidopsis thaliana: the role of microRNA miR173. EMBO Rep. 2009, 10 (3): 264-270. 10.1038/embor.2008.247.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  67. 67.

    Gong HJ, Chen KM, Zhao ZG, Chen GC, Zhou WJ: Effects of silicon on defense of wheat against oxidative stress under drought at different developmental stages. Biol Plantarum. 2008, 52 (3): 592-596. 10.1007/s10535-008-0118-0.

    Article  CAS  Google Scholar 

  68. 68.

    Sepulveda-Jimenez G, Rueda-Benitez P, Porta H, Rocha-Sosa M: A red beet (Beta vulgaris) UDP-glucosyltransferase gene induced by wounding, bacterial infiltration and oxidative stress. J Exp Bot. 2005, 56 (412): 605-611. 10.1093/jxb/eri036.

    Article  CAS  PubMed  Google Scholar 

  69. 69.

    Tognetti VB, Van Aken O, Morreel K, Vandenbroucke K, de Cotte BV, De Clercq I, Chiwocha S, Fenske R, Prinsen E, Boerjan W: Perturbation of indole-3-butyric acid homeostasis by the UDP-Glucosyltransferase UGT74E2 modulates Arabidopsis architecture and water stress tolerance. Plant Cell. 2010, 22 (8): 2660-2679. 10.1105/tpc.109.071316.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  70. 70.

    Kannangara R, Motawia MS, Hansen NKK, Paquette SM, Olsen CE, Moller BL, Jorgensen K: Characterization and expression profile of two UDP-glucosyltransferases, UGT85K4 and UGT85K5, catalyzing the last step in cyanogenic glucoside biosynthesis in cassava. Plant J. 2011, 68 (2): 287-301. 10.1111/j.1365-313X.2011.04695.x.

    Article  CAS  PubMed  Google Scholar 

  71. 71.

    Chang S, Puryear J, Cairney J: A simple and efficient method for isolating RNA from pine trees. Plant Mol Biol Rep. 1993, 11: 113-116. 10.1007/BF02670468.

    Article  CAS  Google Scholar 

  72. 72.

    Quail MA, Kozarewa I, Smith F, Scally A, Stephens PJ, Durbin R, Swerdlow H, Turner DJ: A large genome center’s improvements to the Illumina sequencing system. Nat Methods. 2008, 5 (12): 1005-1010. 10.1038/nmeth.1270.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  73. 73.

    Li R, Yu C, Li Y, Lam TW, Yiu SM, Kristiansen K, Wang J: SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics. 2009, 25 (15): 1966-1967. 10.1093/bioinformatics/btp336.

    Article  CAS  PubMed  Google Scholar 

  74. 74.

    Gardner PP, Daub J, Tate J, Moore BL, Osuch IH, Griffiths-Jones S, Finn RD, Nawrocki EP, Kolbe DL, Eddy SR: Rfam: Wikipedia, clans and the “decimal” release. Nucleic Acids Res. 2011, 39: D141-D145. 10.1093/nar/gkq1129.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  75. 75.

    Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ: miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008, 36: D154-D158. 10.1093/nar/gkn221.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  76. 76.

    Zuker M: Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 2003, 31 (13): 3406-3415. 10.1093/nar/gkg595.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  77. 77.

    Audic S, Claverie JM: The significance of digital gene expression profiles. Genome Res. 1997, 7 (10): 986-995.

    CAS  PubMed  Google Scholar 

  78. 78.

    Man MZ, Wang X, Wang Y: POWER_SAGE: comparing statistical tests for SAGE experiments. Bioinformatics. 2000, 16 (11): 953-959. 10.1093/bioinformatics/16.11.953.

    Article  CAS  PubMed  Google Scholar 

  79. 79.

    Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(−Delta Delta C(T)) Method. Methods. 2001, 25 (4): 402-408. 10.1006/meth.2001.1262.

    Article  CAS  PubMed  Google Scholar 

  80. 80.

    Mallory AC, Reinhart BJ, Jones-Rhoades MW, Tang GL, Zamore PD, Barton MK, Bartel DP: MicroRNA control of PHABULOSA in leaf development: importance of pairing to the microRNA 5 ’ region. EMBO J. 2004, 23 (16): 3356-3364. 10.1038/sj.emboj.7600340.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  81. 81.

    Brennecke J, Stark A, Russell RB, Cohen SM: Principles of MicroRNA-target recognition. PLoS Biol. 2005, 3 (3): 404-418.

    Article  CAS  Google Scholar 

  82. 82.

    Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM, Castle J, Bartel DP, Linsley PS, Johnson JM: Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature. 2005, 433 (7027): 769-773. 10.1038/nature03315.

    Article  CAS  PubMed  Google Scholar 

  83. 83.

    Addo-Quaye C, Miller W, Axtell MJ: CleaveLand: a pipeline for using degradome data to find cleaved small RNA targets. Bioinformatics. 2009, 25 (1): 130-131. 10.1093/bioinformatics/btn604.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

Download references


The authors would like to thank Feng Chen for providing convenience for use of BGI Bio-Cloud Computing platform. This research was supported by the Hi-Tech Research and Development Program of China (2013AA102701), the National Natural Science Foundation of China (31070597, 31270656), the Ministry of Science and Technology of China (2009CB119101), and the Scientific Research and Graduate Training Joint Programs from BMEC (Stress Resistance Mechanism of Poplar).

Author information



Corresponding authors

Correspondence to Weilun Yin or Xinli Xia.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

PS DL designed and conducted the experiments, PS DL ZZ analyzed the data, PS DL WY XX drafted the manuscript, WY XX supervised the project. All authors have read and approved the final version of this manuscript.

Peng Shuai, Dan Liang contributed equally to this work.

Electronic supplementary material

Additional file 1: S1: Summary of P. trichocarpa small RNAs sequencing. (XLSX 12 KB)

Additional file 2: S2: 30 undetected miRNAs in this study. (XLSX 11 KB)

Additional file 3: S3: Potential miRNA candidates without miRNA*s found only in one library of P. trichocarpa.(XLSX 50 KB)

Additional file 4: S4: The predicted hairpin structures of all the 65 new miRNAs precursors. (PDF 202 KB)

Additional file 5: S5: MiRNAs in response to drought stress. (XLSX 12 KB)

Additional file 6: S6: Targets prediction of new P. trichocarpa miRNAs. (XLSX 43 KB)

Additional file 7: S7: Summary of P. trichocarpa degradome sequencing. (XLSX 12 KB)

Additional file 8: S9: Leaf water potential. (XLSX 11 KB)

Additional file 9: S10: Leaf photosynthetic data. (XLSX 13 KB)

Additional file 10: S8: The primers designed for RT-qPCR. (XLSX 11 KB)

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

Authors’ original file for figure 3

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Shuai, P., Liang, D., Zhang, Z. et al. Identification of drought-responsive and novel Populus trichocarpamicroRNAs by high-throughput sequencing and their targets using degradome analysis. BMC Genomics 14, 233 (2013).

Download citation


  • Populus trichocarpa
  • microRNA
  • Drought
  • Target identification