Open Access

Identification of novel microRNAs in Hevea brasiliensisand computational prediction of their targets

  • Virginie Gébelin1,
  • Xavier Argout1,
  • Worrawat Engchuan1, 2,
  • Bertrand Pitollat1,
  • Cuifang Duan1, 3,
  • Pascal Montoro1Email author and
  • Julie Leclercq1
BMC Plant Biology201212:18

DOI: 10.1186/1471-2229-12-18

Received: 5 October 2011

Accepted: 13 February 2012

Published: 13 February 2012

Abstract

Background

Plants respond to external stimuli through fine regulation of gene expression partially ensured by small RNAs. Of these, microRNAs (miRNAs) play a crucial role. They negatively regulate gene expression by targeting the cleavage or translational inhibition of target messenger RNAs (mRNAs). In Hevea brasiliensis, environmental and harvesting stresses are known to affect natural rubber production. This study set out to identify abiotic stress-related miRNAs in Hevea using next-generation sequencing and bioinformatic analysis.

Results

Deep sequencing of small RNAs was carried out on plantlets subjected to severe abiotic stress using the Solexa technique. By combining the LeARN pipeline, data from the Plant microRNA database (PMRD) and Hevea EST sequences, we identified 48 conserved miRNA families already characterized in other plant species, and 10 putatively novel miRNA families. The results showed the most abundant size for miRNAs to be 24 nucleotides, except for seven families. Several MIR genes produced both 20-22 nucleotides and 23-27 nucleotides. The two miRNA class sizes were detected for both conserved and putative novel miRNA families, suggesting their functional duality. The EST databases were scanned with conserved and novel miRNA sequences. MiRNA targets were computationally predicted and analysed. The predicted targets involved in "responses to stimuli" and to "antioxidant" and "transcription activities" are presented.

Conclusions

Deep sequencing of small RNAs combined with transcriptomic data is a powerful tool for identifying conserved and novel miRNAs when the complete genome is not yet available. Our study provided additional information for evolutionary studies and revealed potentially specific regulation of the control of redox status in Hevea.

Keywords

Gene expression miRNA MIR gene Next-generation sequencing Rubber tree Transcription Transcriptome Abiotic stress miRNA editing

Background

Hevea brasiliensis, the sole commercial source of natural rubber, is a tropical perennial species native to the Amazon basin. South-East Asian countries supplied 92% of the 10 million tons of natural rubber produced in 2010, but ever-growing worldwide demand calls for enhanced tree productivity. This goal is increasingly becoming a crucial challenge for the corresponding research activities in a context of global climate change and redistribution of land for food crops. Natural rubber is a cis-1,4 polyisoprene polymer biosynthesized in rubber particles located in specialized latex cells. Latex cells are periodically emitted from the cambium and then anastomosed to form latificer mantels [1]. Natural rubber is harvested by tapping. The cytoplasm of laticifers, containing 30-50% of rubber particles, is then expelled by high turgor pressure maintained in the soft bark tissues. An ethylene generator, 2-chloroethylphosphonic acid (ethephon), is applied to the tapping panel to stimulate latex production. Ethephon increases the duration of latex flow after tapping and its regeneration between two tappings [2]. Tapping Panel Dryness (TPD) is a physiological disease that causes 10-40% annual rubber production losses over the 30 years of a rubber cultivation cycle. This physiological disorder, triggered by oxidative stress, is partially induced by excessive environmental and harvesting stresses [3]. The generation of reactive oxygen species (ROS) in latex cells leads to in situ coagulation of rubber particles [4, 5]. Research on ROS in plants initially focused on their cytotoxicity only, but nowadays ROS are also considered as signalling molecules [6, 7]. A ROS signalling network emerges and is conserved in all aerobic organisms [8]. The ROS-scavenging system acts both to maintain redox homeostasis and to protect cell components from oxidative damage. Biotic and abiotic stress, such as drought, salinity, strong light, temperature, heavy metals, UV radiation, atmospheric contamination, mechanical wounding, nutrient starvation and pathogen attacks are major sources of ROS in plants [9].

MicroRNAs (miRNAs) particularly play a key function in responses to abiotic stress (for a review see [1013]). Indeed, small RNAs are involved in fine-tuning gene expression in response to physiological, external and developmental stimuli [14, 15]. Of these, miRNAs have been shown to be a large group of small endogenous RNAs that exist in animals, plants and viruses. Since their discovery in Caenorrhabditis elegans [16] and in Arabidopsis thaliana in 2002 [17], miRNAs have been described as negatively regulating gene expression by targeting specific messenger RNAs (mRNAs) for cleavage [18, 19] or translational inhibition [20]. The biogenesis of miRNAs has been amply described in plants and animals [2124]. Briefly, a MIR gene is first transcribed to primary miRNAs (pri-miRNA) by RNA polymerase II, the 70-500 bp long pri-miRNA forms an imperfectly paired hairpin which is processed by RNAse III-like DICER-like I (DCL1) to generate miR precursors (pre-miRNAs) [25, 26]. Further cleavage of the pre-miRNA by DCLI releases a miRNA/miRNA* duplex. The duplex is then translocated into the cytoplasm by HASTY (plant ortholog to exportin 5 [27]), the canonical mature miRNA of 20-22 nucleotides is selectively incorporated into the RNA-induced silencing complex (RISC) associated with Argonaute 1 (AGO1). In the RISC complex, miRNAs bind to mRNA and inhibit gene expression through perfect or near-perfect complementarity between the miRNA and the mRNA [18]. In plants, the recent discovery of MIR genes generating both canonical miRNA (20-22 nucleotides) and long miRNA (lmiRNA, 23-27 nucleotides) suggests their bifunctionality [28]. The lmiRNAs derived from MIR genes are selectively processed by DCL3, can associate with AGO4 and guide DNA methylation at some of their target loci in trans in rice and moss [28, 29]. The recent and major discovery of the role played by post-transcriptional modifications of miRNA precursors highlighted the complexity of miRNA biogenesis and the way miRNAs act [30, 31]. Indeed, adding an oligo-uridine tail to the 3' end of the miRNA precursor is a mechanism that controls miRNA production in mice [31]. In plants, it has been reported that either a combination of 5' deletion and 3' uridylation of miRNA, or RNA editing events, alter the targeting and preference of Argonaute association [30]. Several miRNAs have been described as being up-regulated or down-regulated by high salinity, drought and low temperatures [32, 33]. Furthermore, the miRNA-targeted gene copper zinc superoxide dismutase (CuZnSOD), which plays a major role in maintaining redox homeostasis, is cleaved by miR398 under stress-free conditions in Arabidopsis [34, 35].

Currently, 15,172 mature miRNAs have been discovered and deposited in the public database, miRBase (Release 16, 2010, http://www.mirbase.org) [36, 37], and 9,277 mature plant miRNAs in the Plant MicroRNA Database, PMRD http://bioinformatics.cau.edu.cn/PMRD[38]. Most miRNAs have been identified in model species such as Arabidopsis, Oryza [39, 40], Populus [12, 41], Physcomitrella [42], Vitis [43], whose genomes have been sequenced. Some miRNAs have also been identified in Glycine max [44], Arachis hypogaea [45], Solanum lycopersicum [46], Brassica napus [47], Phaseolus vulgaris [48] and in the euphorbiaceous species Ricinus communis [49]. Twenty-three miRNA families predicted in Ricinus communis were experimentally validated in four euphorbiaceous plants (Ricinus communis, Jatropha curcas, Manihot esculenta and Hevea brasiliensis) [49]. However, the strategy used did not allow exhaustive identification of miRNA families in Hevea.

The availability of large collections of Hevea Expressed Sequence Tags (ESTs; http://bassigny/cgi-bin/esttik_dev/quick_search.cgi) and next-generation sequencing techniques offers prospects for understanding the post-transcriptional regulation of key functions involved in responses to stress and in latex production in Hevea brasiliensis. This study involved the deep sequencing of small RNAs from Hevea plants grown under various conditions. We report here on the identification of 48 conserved miRNA families, sharing very high homology with that already known in other species. MiRNA precursor sequences were identified for nine families. In addition, ten putative novel miRNA families were also identified with their precursors. A scan of the Hevea EST databases with miRNA sequences revealed their putative targets. Of them, the predicted targets involved in responses to stimuli, the ROS-scavenging systems and transcription regulation are presented, and new insights into the control of redox status in Hevea are proposed.

Results

Classification of small RNAs

A total of 4,223,792 raw reads was generated by Solexa sequencing from the small RNA library prepared from pooled juvenile and mature plant materials subjected to various types of abiotic stress (Table 1 and Table 2). After removing adapters, cleaned reads amounted to 2,378,135 sequences corresponding to 670,645 unique sequences. The sequence lengths ranged from 17 to 32 nucleotides. Although some small RNAs were present about a thousand times in our dataset, most were sequenced only a few times. Small RNAs consisting of only one or two reads accounted for 32% and 53% of clean sequences, respectively.
Table 1

Statistics of small RNA sequences from the Hevea brasiliensis small RNA library

Stress small RNA library

Sequence number

Analysis

read sequences

4,223,792

 

clean sequences

2,378,135

Solexa sequencing

unique clean sequences of small RNA

670,645

 

no hit

629,795

 

hit, unknown

26,721

 

coding mRNA

6,618

 

transposons/transposable element

6,800

 

rRNA

399

Blast against Arabidopsis

pseudo mRNA

138

genome

ncRNA

73

 

miRNA

57

 

tRNA

39

 

sn/snoRNA

5

 

no hit

652,215

 

hit

18,430

Blast against PMRD

conserved miRNA family

48

 

putative novel miRNA family

10

LeARN pipeline

Table 2

List of the treatments applied to Hevea plants before small RNA isolation

Treatments

Plant material

Tissue

Condition

Ethylene

in vitro plantlet (2 years old)

leaf, bark, root

5 ppm for 4 hours

 

budded plant

leaf, bark

 

Methyl-jasmonate

in vitro plantlet (2 years old)

leaf, bark, root

0.3 μM for 4 hours

 

budded plant

leaf, bark

 

Wounding

in vitro plantlet (2 years old)

leaf, bark, root

2 hours

 

budded plant

leaf, bark

 

Drought

in vitro plantlet (2 years old)

leaf, bark, root

10 days

 

budded plant

leaf, bark

4 weeks

Flooding

in vitro plantlet (2 years old)

leaf, bark, root

4 days

 

budded plant

leaf, bark

 

Cold

in vitro plantlet (2 years old)

leaf, bark, root

4°C for 8 hours

 

budded plant

leaf, bark

 

Light

in vitro plantlet (2 years old)

leaf, bark, root

PAR: 1000-1500 μmol/m2/s for

 

budded plant

leaf, bark

4 hours

NaCl

in vitro plantlet (2 years old)

leaf, bark, root

300 mM for 4 days

 

budded plant

leaf, bark

 
 

in vitro plantlet (2 years old)

leaf, bark, root

 

Control

budded plant

leaf, bark

 
 

in vitro plantlet (1 month old)

leaf, bark, root

 
 

callus

callus

 

Annotation of the Hevea small RNAs was attempted by BLASTN on the Arabidopsis genome. Most of the sequences did not map onto the Arabidopsis genome sequence. Of the mapped sequences, 26,721 were not annotated. Annotated sequences predicted 6,618 products of mRNA degradation, 6,800 transposable elements, 399 rRNAs, 138 products of pseudogene degradation, 73 ncRNAs, 39 tRNAs, 5 sn/snoRNAs and 57 miRNAs corresponding to 14 families.

Further mapping was carried out with all the miRNA sequences available in the PMRD database. In all, 18,430 sequences matched (0 or 1 mismatch) to conserved miRNAs in other species. The accessions were classified into 48 miRNA families (Table 1). Since most sequences did not map against PMRD, the Hevea EST database was scanned using the LeARN pipeline. This strategy led to the identification of 10 additional miRNA families, based on the stem-loop structure, which were not described in other species (Table 1). The distribution of the 670,645 unique accessions showed that the most abundant accessions had in descending order 27, 19, 24, 26 and 17 nucleotides (Figure 1). Accessions with 17 and 19 nucleotides came from products of mRNA degradation and transposable elements according to the mapping results against the Arabidopsis genome sequence (data not shown).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-12-18/MediaObjects/12870_2011_Article_1016_Fig1_HTML.jpg
Figure 1

Length distribution of unique accessions in the small RNA dataset in Hevea brasiliensis.

Characteristics of conserved and putative novel miRNA families in Hevea brasiliensis

Reads of conserved and putative novel miRNAs were distributed according to their nucleotide lengths (Figure 2). The 48 conserved miRNA families mostly had 24 and 23 nucleotides (Figure 2A). For the 10 putative novel miRNA families, the most represented lengths were 24, 27 and 26 nucleotides (Figure 2B).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-12-18/MediaObjects/12870_2011_Article_1016_Fig2_HTML.jpg
Figure 2

Length distribution of reads matching miRNA families in Hevea brasiliensis. (A) conserved miRNA (B) putatively novel families.

Of the 48 conserved miRNA families, 12 were predominant with more than 1,000 reads, including 3 families that displayed more than 20,000 reads. The latter corresponded to HbmiR159/319 and HbmiR408 (Figure 3A). By contrast, 36 families were less represented in the dataset with 18 families having fewer than 100 reads: HbmiR1310, HbmiR162, HbmiR168, HbmiR1863, HbmiR2118, HbmiR2910, HbmiR2914, HbmiR2915, HbmiR2916, HbmiR393, HbmiR394, HbmiR395, HbmiR399, HbmiR444, HbmiR476, HbmiR482, HbmiR828 and HbmiR845 (Figure 3B).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-12-18/MediaObjects/12870_2011_Article_1016_Fig3_HTML.jpg
Figure 3

Abundance of the 48 conserved miRNA families in Hevea brasiliensis with 0 and 1 mismatch on a minimum length of 19 nucleotides. A) The sequencing frequency of a conserved miRNA family in the small RNA library above 1000 reads. B) The sequencing frequency of a conserved miRNA family in the small RNA library below 1000 reads.

A detailed analysis of length distribution within the Hevea miRNA families revealed some discrepancies (Figure 4 and Figure 5). Firstly, the 20-22 nucleotide and 23-27 nucleotide size classes were detected for both conserved and novel miRNA families. Secondly, the most abundant size for miRNAs was 21-24 nucleotides, except for HbmiR169 (27 nucleotides), HbmiR2911 (26 nucleotides), HbmiR482 (25 nucleotides), HbmiR472 (25 nucleotides), HbmiRn8 (25 nucleotides) and HbmiRn9 (27 nucleotides). Thirdly, the putative novel miRNAs occurred at low levels with fewer than 500 reads, and at extremely low levels for HbmiRn1 and HbmiRn2, with only three sequences (Figure 5).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-12-18/MediaObjects/12870_2011_Article_1016_Fig4_HTML.jpg
Figure 4

Number of reads according to the length distribution of accessions of conserved miRNA families in Hevea brasiliensis.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-12-18/MediaObjects/12870_2011_Article_1016_Fig5_HTML.jpg
Figure 5

Number of reads according to the length distribution of accessions of putative novel miRNA families in Hevea brasiliensis.

Detection of single-nucleotide mutations in miRNA sequences

For all the conserved miRNA families, single-nucleotide modifications in the sequences were analysed in detail based on the data presented in [30]. In Arabidopsis, seventeen families are concerned by RNA editing events and nine families by 5' deletion and 3' uridylation modifications (called below -1 + UU modification). In Hevea, of the seventeen families, four families displayed single-nucleotide mutations at the sites of post-transcriptional RNA modifications described in Arabidopsis, namely AtmiR156, AtmiR159a, AtmiR164a, and AtmiR172a (Table 3). By contrast, no -1 + UU modification was observed in Hevea (Table 3).
Table 3

Frequency of single-nucleotide mutation and of a combination of 5' deletion and 3' uridylation (-1 + UU) for four miRNA families from Hevea

Family name in Arabidopsis

Single-nucleotide modification

-1 + UU modification

 

Arabidopsis

Hevea

Arabidopsis

Hevea

AtmiR156

G-A: 14.091%

A-U: 49.382%

A-C: 3.571%

yes

no

 

G-A: 10.407%

G-A: 2.127%

  

AtmiR159a

G-A: 8.597%

G-A: 4.166%

no

no

 

G-A: 5.882%

G-C: 2.127%

G-U: 1.398%

  

AtmiR164a

G-A: 3.967%

G-A: 7.692%

no

no

AtmiR172a

G-U: 0.599%

G-U: 6.153%

A-U: 7.692%

C-U: 7.692%

no

no

Sites of post-transcriptional modification identified in Arabidopsis [50] were analysed in the Hevea small RNA dataset. Only sequences present at > 0.5% are listed

Identification of precursor transcripts for conserved and putative novel miRNA families from the hevea brasiliensistranscriptome

Conserved and putative novel miRNA precursors were sought in the Hevea clone PB260 transcript sequence databases obtained from various organs using the LeARN pipeline [51]. Eight conserved miRNA families that mapped against RNA sequences displayed a stem-loop structure (Table 4 and Additional Table 1). A single miRNA precursor transcript was found for the HbmiR156, HbmiR159, HbmiR396, HbmiR476 and HbmiR2910 families. Two miRNA precursor transcripts were identified for the HbmiR166, HbmiR319 and HbmiR408 families. In addition, the LeARN pipeline predicted 10 putative novel miRNAs with their unique precursors (Table 5 and Additional Table 2). Mapping the sequences of the small RNA dataset on the precursor sequences led to the identification of miRNA*for all eight conserved families (HbmiR156, HbmiR159, HbmiR166, HbmiR319, HbmiR396, HbmiR408, HbmiR476 and HbmiR2910) and for the five putatively new families HbmiRn3, HbmiRn4, HbmiRn8, HbmiRn9 and HbmiRn10. Stem-loop reverse transcription polymerase chain reactions (RT-PCR) were successfully performed on seven precursors for the conserved families and on nine for the putatively new families (Figure 6).
Table 4

List of precursors for conserved microRNAs identified in the Hevea clone PB260 transcriptome sequences

New miR name

Accession Name

Mature sequence

Mature length

miRNA* (Accession No.)

Precursor length

EST

HbmiRn1

acc_584359

CAGGAACUGGUAUCAACCCAGC

22

nd

130

CL1Contig1853_r1

HbmiRn2

acc_495109

UAUUGUAGAAAUUUUCAGGAUC

22

nd

116

CL1Contig490_r1

HbmiRn3

acc_185377

UAAUGGGCUCUGCAUAGAUGG

21

Yes (3)

108

CL27Contig1_r1

HbmiRn4

acc_108425

UUGCAUAUCUCAGGAGCUUCA

21

Yes (4)

214

CL19922Contig1_r1

HbmiRn5

acc_644211

AAACGGCUACCACAUCCAA

19

nd

87

FYGXE6I01B755W_r1

HbmiRn6

acc_501419

UAGGAUGUAGAAGAGCAUAA

20

nd

90

F5VNCTM02H9XL3_r1

HbmiRn7

acc_35715

UAGUUUGUUUGAUGGUAUC

19

nd

113

FYGXE6I01EKFYA_r1

HbmiRn8

acc_246127

GAUUGACAGACUGAGAGCUC

20

Yes (16)

93

FYGXE6I01B7GPL_r1

HbmiRn9

acc_103082

UUUAUGAAAGACGAACAACUG

21

Yes (6)

87

CL1Contig2798_r1

The presence of the miRNA* sequence in the small RNA dataset is indicated. Pictures of the stem-loop structures are presented in Additional Table 1.

Table 5

List of putatively new miRNA precursors identified in the Hevea clone PB260 transcriptome sequences

MiRNA name

Accession Name

Mature sequence

Mature length

miRNA* (Accession No.)

Precursor length

EST

Hbmir156

acc_462135

UUGACAGAAGAUAGAGAGC

19

Yes (10)

119

FYGXE6I01DJH4X_r1

Hbmir159

acc_17403

UUUGGAUUGAAGGGAGCUCUA

21

Yes (5)

221

GETTCZF01AWUSL_r1

Hbmir166

acc_28021

UCGGACCAGGCUUCAUUCC

19

Yes (14)

127

CL2671Contig1_r1

 

acc_150486

UCGGACCAGGCUUCAUUCCCCC

22

Yes (14)

120

CL1Contig15359_r1

Hbmir319

acc_19786

UUGGACUGAAGGGAGCUCCCU

21

Yes (7)

221

FRZQES201D34W3_r1

Hbmir396

acc_112787

UUCCACAGCUUUCUUGAACUG

21

Yes (15)

154

CL1Contig13811_r1

Hbmir408

acc_393516

AAGACUGGGAACAGGCAGAGCA

22

Yes (294)

128

FYGXE6I01CVDV9_r1

 

acc_39581

ACUGGGAACAGGCAGAGCAUGA

22

Yes (294)

120

CL3908contig1_r1

Hbmir476

acc_508145

UAAUCCUUCUUUGCAAAGUC

20

Yes (1)

126

CL1Contig11471_r1

Hbmir2910

acc_251816

GAGCGAUUUGUCUGGUUAAUC

21

Yes (5)

126

FYGXE6I01EDXGX_r1

The presence of the miRNA* sequence in the small RNA dataset is indicated (nd: not detected). Pictures of the stem-loop structures are presented in Additional Table 2.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-12-18/MediaObjects/12870_2011_Article_1016_Fig6_HTML.jpg
Figure 6

Gel electrophoresis of stem-loop RT-PCR. A) Stem-loop RT-PCR from seven precursors of conserved miRNA family. B) Stem-loop RT-PCR from nine precursors of putatively new miRNAs.

Comparison of the gene ontology classification of predicted targets for conserved and putative novel miRNA families

Computational prediction of miRNA targets led to the identification of 1,083 sequences for the 48 conserved families and 705 for the 10 putative novel miRNA families. Their involvement in biological processes and their molecular functions were attributed using Gene Ontology (Figure 7A, B, C and 7D). Most of the GO terms were represented in the same proportions for predicted targets from conserved and putative novel miRNA families. Target genes of the putative novel miRNA families all had GO terms of conserved miRNA families for biological processes and molecular functions. In biological process terms, the putative novel miRNA families had two additional terms (growth, cell wall organization or biogenesis). The number of miRNA target genes was smaller for putative novel than for conserved miRNAs for some GO terms: cellular process (25% as opposed to 27%), metabolic process (22% as opposed to 27%) and biological regulation (10% as opposed to 12%) (Figures 7A and 7C). This number was larger for putative novel miRNA targets compared to conserved targets for cellular component organization (5% as opposed to 3%), localization (7% as opposed to 4%) and response to stimulus (10% as opposed to 8%) (Figures 7A, B, C and 7D). As regards the GO terms for molecular function, although the distribution for antioxidant activity was the same for the 2 classes of miRNAs (1%), a decrease in the proportion of transcription regulator activity (4% as opposed to 7%) and an increase in structural molecule activity (4% as opposed to 2%) and transporter activity (7% as opposed to 4%) were observed for the putative novel miRNA families (Figures 7B and 7D).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-12-18/MediaObjects/12870_2011_Article_1016_Fig7_HTML.jpg
Figure 7

Pie chart representation of Gene Ontology terms at level 2 for putative targets of conserved (A and B) and novel miRNAs (C and D). A and C) Gene Ontology terms for biological processes, B and D) Gene Ontology terms for molecular functions.

Comparison of predicted target genes for conserved and putative novel miRNA families involved in "response to stimulus", and in "antioxidant" and "transcription regulation" activities

As the management of oxidative stress is crucial for rubber productivity, we set out to more effectively understand the mechanisms of redox homeostasis. We therefore gave priority to predicted miRNA-targeted genes involved in the "response to stimulus" (biological process) and in "antioxidant" and "transcription regulator" activities (molecular process) according to the GO annotations (Tables 6 and 7).
Table 6

List of predicted targets for conserved miRNAs involved in response to stimulus, and in antioxidant and transcription regulator activities according to GO terms

MiRNA family

GO term

Predicted target function

mfe kcal/mol

Target name

 

Transcription regulation activity

Squamosa promoter-binding protein

-45.50

CL2120Contig2

HbmiR156/157

 

APETALA2-like protein

-24.41

hevea_454_rep_c24306

 

Antioxidant activity

flavonoid 3',5'-hydroxylase

-26.53

CL2495Contig2

  

AKR (aldo keto reductase)

-32.90

hevea_454_rep_c15059

 

Transcription regulation activity

APETALA2-like protein

-27.80

hevea_454_rep_c17780

  

sarcosine oxidase

-29.70

CL11607Contig1

  

serine/threonine protein kinase

-21.39

CL11Contig15

  

cinnamoyl-CoA reductase

-32.88

CL1Contig7212

  

CuZnSOD peroxysomal

-32.14

CL1Contig3818

HbmiR159

Antioxidant activity

ABC transporter C family

-28.02

CL1Contig3612

  

cytochrome b6f complex

-36.18

CL1Contig475

  

TATA box binding protein

-29.38

CL1Contig760

  

oxidoreductase family protein

-32.88

CL781Contig2

  

ATP Synthase

-27.31

CL1613Contig1

  

HMG-CoA_reductase (HMGR)

-36.39

CL8048Contig1

  

6-phosphogluconate dehydrogenase family protein

-33.44

CL1Contig1588

HbmiR319

Antioxidant activity

ferritin putative

-35.86

CL1Contig17258

HbmiR160

Transcription regulation activity

ARF

-52.20

CL6582Contig1

 

Antioxidant activity

FAD/NAD(P)-binding oxidoreductase-like protein

-23.10

hevea_454_rep_c15774

HbmiR162

 

ABC transporter C family

-26.47

hevea_454_rep_c28843

 

Response to stimulus

zinc finger family protein

-31.40

hevea_454_rep_c20758

HbmiR164

Transcription regulation activity

NAC-domain protein

-44.12

hevea_454_rep_c10543

  

amine oxidase

-32.67

CL21795Contig1

 

Transcription regulation activity

NAC-domain protein

-32.43

CL7990Contig1

  

PHAVOLUTA-like HD-ZIPIII protein

-46.68

hevea_454_rep_c12680

HbmiR165/166

Antioxidant activity

LRR protein

-30.08

hevea_454_c28164

 

Antioxidant activity/Response to stimulus

malate deshydrogenase

-38.95

hevea_454_rep_c94776

 

Response to stimulus

abscisic acid insensitive protein

-36.33

hevea_454_rep_c69184

Hbmir167

Response to stimulus

cap binding protein

-33.60

hevea_454_c94262

 

Transcription regulation activity

CCAAT-binding transcription factor

-32.79

CL435Contig5

HbmiR169

 

Laccase

-26.51

CL1Contig13876

 

Antioxidant activity

aldehyde dehydrogenase

-26.56

CL1Contig16398

  

annexin-like protein

-32.71

CL1Contig2060

HbmiR170

Transcription regulation activity

GRAS domain-containing protein

-37.90

hevea_454_rep_c11063

 

Transcription regulation activity

GRAS domain-containing protein

-39.50

CL3142Contig4

HbmiR171

 

ribonucleoside-diphosphate reductase

-26.99

CL1031Contig2

 

Antioxidant activity

oxophytodienoate reductase (OPR)

-27.23

CL3790Contig2

  

glyceraldehyde-3-phosphate dehydrogenase

-24.88

CL150Contig1

 

Transcription regulation activity

APETALA2-like protein

-24.25

hevea_454_rep_c45080

  

HD-ZIP protein

-33.63

hevea_454_rep_c13497

HbmiR172

Antioxidant activity

NAD(P)H-quinone oxidoreductase

-24.54

hevea_454_rep_c13256

  

CLAVATA1 putative

-31.73

hevea_454_rep_c2026

  

Squalene monooxygenase

-29.04

hevea_454_rep_c32943

 

Response to stimulus

syntaxin

-31.00

hevea_454_c68550

  

aspartate aminotransferase

-33.44

hevea_454_rep_c4007

HbmiR2111

Antioxidant activity

laccase

-26.57

hevea_454_rep_c5912

HbmiR2910

Antioxidant activity

2OG-Fe(II) -dependent-oxygenase-like protein

-30.83

hevea_454_rep_c1596

  

ketol-acid reductoisomerase

-31.31

hevea_454_rep_c5152

HbmiR2914

Antioxidant activity

FAD Binding domain-containing protein

-58.64

hevea_454_rep_c15793

  

Rboh

-31.88

hevea_454_rep_c23550

 

Transcription regulation activity

Heat shock protein

-30.89

CL11591Contig1

Hbmir390

 

WRKY transcription factor

-33.09

CL338Contig4

 

Antioxidant activity

Phosphoenolpyruvate carboxylase

-19.81

CL362Contig2

  

hydroxy-acid oxidase

-23.58

CL8468Contig1

HbmiR393

Transcription regulation activity

APETALA2-like protein

-22.79

hevea_454_c60993

 

Transcription regulation activity

APETALA2-like protein

-24.47

hevea_454_c37716

Hbmir395

Antioxidant activity

2OG-Fe(II) -dependent-oxygenase-like protein

-30.00

hevea_454_rep_c21129

 

Transcription regulation activity

APETALA2-like protein

-22.40

hevea_454_rep_c13430

  

Transparent TESTA protein

-34.16

hevea_454_rep_c14652

  

GRAS domain-containing protein

-27.01

hevea_454_rep_c8477

HbmiR396

Antioxidant activity

zinc finger family protein

-31.84

hevea_454_c64598

  

Lignin-forming anionic peroxidase

-26.08

hevea_454_rep_c20896

 

Antioxidant activity/Response to stimulus

Glutamyl-tRNA reductase

-35.76

hevea_454_rep_c14169

  

fatty acyl-coenzyme A reductases (FAR)

-25.41

hevea_454_rep_c16435

  

cysteine protease

-33.18

CL1Contig374

HbmiR397

Antioxidant activity

laccase

-40.97

hevea_454_c73766

 

Antioxidant activity

4-coumarate-coa ligase

-37.60

hevea_454_rep_c6270

HbmiR398

 

CuZnSOD chloroplastique

-37.30

CL4308Contig2

 

Response to stimulus

HVA22-like protein

-36.03

hevea_454_rep_c27557

 

Transcription regulation activity

APETALA2-like protein

-28.43

hevea_454_rep_c64305

  

serine/threonine protein kinase

-32.93

CL23251Contig1

  

Heat shock protein

-26.98

CL530Contig7

  

cytochrome P450

-37.63

CL1Contig8196

HbmiR408

 

enolase

-42.49

CL5843Contig1

 

Antioxidant activity

ribonucleoside-diphosphate reductase

-37.34

CL1031Contig6

  

thioredoxin reductase

-32.80

CL1401Contig3

  

gibberellin 20-oxidase

-37.63

CL1Contig12612

  

4-hydroxy-3-methylbut-2-enyl diphosphate reductase

-30.00

CL1Contig6885

HbmiR444

Transcription regulation activity

WRKY

-36.82

hevea_454_rep_c7550

  

Heat shock protein

-29.45

CL2547Contig3

HbmiR476

Antioxidant activity

Rboh

-27.76

CL1Contig1292

  

cationic peroxidase 2 precursor

-31.63

CL257Contig2

HbmiR535

Antioxidant activity

LRR protein

-29.18

hevea_454_c18080

HbmiR827

Response to stimulus

fructose-bisphosphate aldolase

-26.90

hevea_454_rep_c59114

HbmiR828

Transcription regulation activity

MYB class transcription factor

-26.76

hevea_454_rep_c48615

Table 7

List of predicted targets for putative novel miRNAs involved in response to stimulus, and in antioxidant and transcription regulator activities according to GO terms

miRNA family

Accession number

GO term

Predicted target function

mfe kcal/mol

Target name

HbmiRn1

Acc_584359

Response to stimulus

Aquaporin like protein

-47.70

CL1Contig2288

  

Transcription regulation activity

Helicase like protein

-31.10

CL18570Contig1

HbmiRn2

Acc_495109

Response to stimulus

glycogen synthase kinase

-21.10

CL1Contig2247

  

Transcription regulation activity

TAZ zinc finger

-22.40

CL5132Contig2

HbmiRn3

Acc_185377

Transcription regulation activity

MYC

-27.14

CL1Contig2024

HbmiRn4

Acc_108425

Transcription regulation activity

bZIP domain trnscription factor

-29.10

CL11387Contig1

   

Peroxidase

-28.30

CL1Contig15444

  

Response to stimulus

gamma-glutamylcysteine synthetase

-27.80

CL786Contig2

HbmiRn5

Acc_644211

 

CTR1

-29.18

CL3295Contig2

  

Transcription regulation activity

Auxin-responsive protein

-29.20

CL16079Contig1

   

GRAS family protein

-33.17

CL2606Contig1

HbmiRn6

Acc_501419

Response to stimulus

ubiquitin fusion protein

-37.74

CL1Contig4048

  

Response to stimulus

heat shock protein

-27.50

CL4213Contig3

   

heat shock protein

-23.79

CL1Contig6482

   

glycerol kinase

-26.20

CL6130Contig1

HbmiRn7

Acc_35715

 

glycine-rich RNA-binding protein

-22.90

CL2515Contig2

   

ubiquitin-protein ligase

-21.10

CL7863Contig1

  

Transcription regulation activity

NAC domain protein

-27.06

CL678Contig1

   

LRR receptor-like protein kinase

-26.16

CL6991Contig1

HbmiRn8

Acc_246127

Response to stimulus

SAUR family protein

-27.40

CL3428Contig1

HbmiRn9

Acc_103082

Response to stimulus

Peroxidase

-35.30

CL1Contig7525

HbmiRn10

Acc_482742

Response to stimulus

calcium-dependent protein kinase

-23.30

CL1Contig9241

   

longevity assurance factor

-22.15

CL8027Contig1

Twelve members of the AP2/ERF domain-containing transcription factor family were putatively targeted by HbmiR156, HbmiR159, HbmiR172, HbmiR393, HbmiR395, HbmiR396, and HbmiR408. Two HD-ZIP III domain-containing protein members (HbmiR165/166, HbmiR172), a transcription factor with a bZIP domain (HbmiRn4), three NAC domain-containing proteins (HbmiR164, HbmiR165 and HbmiRn7), three Squamosa promoter binding proteins (HbmiR156/157), four WRKY transcription factors (HbmiR156/157, HbmiR390, HbmiR444), four Auxin Response Factor (ARF); HbmiR160 and HbmiRn5) and five GRAS family transcription factors (HbmiR170, HbmiR171, HbmiR396 and HbmiRn5) were also potentially targeted.

Sequences encoding protein involved in protein degradation, such as cysteine protease (HbmiR396) and ubiquitin protein ligase (HbmiRn6 and HbmiRn7), were identified for genes classified in the "response to stimulus" category.

Three sequences related to ABA signalling were also identified: an ABA-insensitive protein (HbmiR166, and HbmiR2910), and an HVA22-like protein (HbmiR398). In addition, one sequence encoding CTR1, a negative regulator of the ethylene signalling pathway, was targeted by HbmiRn5. MiRNA biogenesis was also highlighted by the presence of a cap binding protein predicted to be targeted by HbmiR167. Interestingly, two enzymes involved in natural rubber biosynthesis, HMG-CoA reductase and 4-hydroxyl-3-methylbut-2-enyl diphosphate reductase, were predicted to be targeted by HbmiR159 and HbmiR444, respectively. For the "antioxidant activity" category, several ROS-scavenging enzymes were identified. The chloroplastic and peroxisomal isoforms of CuZnSOD were putatively targeted by HbmiR398 and HbmiR159, respectively. Another enzyme involved in glutathione biosynthesis, gamma glutamyl cysteine ligase (GCL), was predicted to be targeted by HbmiRn5. Rboh (or NADPH oxidase) is an enzyme involved in ROS production, which is likely to be targeted by two miRNAs (HbmiR2914 and HbmiR476). In addition, targets that are subjected to or are involved in the redox status of proteins, such as genes encoding proteins involved in lignin synthesis, cynnamoyl CoA reductase, lignin forming anionic peroxidase precursor and 4-coumarate:CoA ligase, were also found to be targeted by HbmiR159, HbmiR408 and HbmiR398, respectively.

The cleavage site for three targets has been experimentally validated for a chloroplastic CuZnSOD, a Squamosa promoter binding protein and an ARF (Table 8). For these targets, the cleavage site is located at the canonical 10th nucleotide.
Table 8

List of target genes experimentally cleaved by miRNA

miRNA

family

Target name

Heveasequence

name

miRNA accession

mfe kcal/mol

Alignment

Hbmir156

Squamosa promoter binding protein (HbSquamosa)

CL2120Contig2

acc_480780

-45.5

miRNA

Target

23

766

CUACACGAGAGAGAGAAGACAGU

: :::::::::::::::::::::

GUUGUGCUCUCUCUCUUCUGUCA

8/14

1

788

Hbmir160

Auxin Response Factor (HbARF)

CL6582Contig1

acc_370

-52.2

miRNA

Target

21

524

ACCGUAUGUCCCUCGGUCCGU

::::::.::::::::::::::

UGGCAUGCAGGGAGCCAGGCA

27/30

1

544

HbmiR398

Chloroplastic copper zinc superoxide dismutase

(HbCuZnSODchl)

CL4308Contig2

acc_420

-37.3

miRNA

Target

23

456

UAGU-C-CCCGCUGGACUCU-UGUGU

.::: : :::.::::::.:: :::.:

GUCAUGCGGGUGACCUGGGAAACAUA

6/14

1

480

The number of sequences presenting the same cleavage site in indicated below the cleavage site located at the 10th nucleotide (arrow)

Discussion

Size distribution of small RNAs in Heveamay differ from others species

Next-generation high-throughput sequencing techniques are powerful tools for miRNA identification. The sequencing and analysis of the Hevea small RNA library generated more than 2 million sequences. In their vast majority (86%), the sequenced small RNAs were only represented by one or two reads. Similarly, 65% of unique miRNA sequences were found in Arabidopsis by 454 sequencing techniques [52]. Interestingly, we found that the size distribution of Hevea small RNAs differed from other species. The most abundant small RNAs had 17, 19 and 27 nucleotides in Hevea whereas they have 21 and 24 nucleotides in other species. Indeed, in Arabidopsis thaliana [52], Arachis hypogea [45] and Oryza sativa [53], the fraction of 24 nucleotides has proved to be predominant, accounting for 60%, 45% and 36% of unique small RNAs, respectively. The predominant size has been found to be 21 nucleotides in Pinus contorta [53] and Taxus chiniensis [54]. In Hevea, the high percentage of 17- and 19-nucleotide small RNAs resulted from the increased amount of RNA degradation products and transposable elements in response to abiotic treatments according to the mapping results against the Arabidopsis genome (data not shown), as previously observed in Brachypodium [55]. Indeed, a change in small RNA size distribution has been reported in Brachypodium in response to cold treatment. Under normal growth conditions, the 21- and 24-nucleotide classes were the most abundant small RNA families, whereas it was the 19-nucleotide class after cold treatment [55]. Our dataset was generated from pooled small RNAs of various tissues subjected to several types of abiotic stress. The size distribution may have been differentially affected by each type of abiotic stress and may explain the difference in size distribution in Hevea.

Long conserved and novel miRNAs of 23-27 nucleotides consisted of short miRNA sequences with an extension. This lmiRNA structure suggested that they were generated from the same MIR genes. Our observation suggests dual functions for 14 canonical MIR genes producing 20-22 nt and 23-27 nt lmiRNA in Hevea, namely HbMIR169, HbMIR2911, HbMIR482, HbMIR472, HbMIR408 and HbMIRn3-10. Across the plant kingdom, lmiRNA were previously classed as heterochromatic small interfering RNAs (hc-siRNA) [28]. In Arabidopsis, the hc-siRNA derived from the miRNA-generating site is dependent on DCL3, RDR2 and PolIV associated with AGO4 and direct DNA methylation for some target sites [28]. As regards the higher number of reads for the 24 nucleotide compared with the 20-22 nucleotide miRNAs, the 14 miRNA families producing 20-22 nt and 23-27 nt miRNAs might mostly regulate their targets through direct DNA methylation in response to abiotic stress. This may confirm the results of a recent study showing the existence of site-specific abiotic stress-induced DNA methylation for five genes in Hevea [56].

Ancient and recently differentiated Hevea brasiliensismiRNAs provide additional information for evolutionary studies

The 48 Hevea brasiliensis conserved miRNA families were more or less similar to those of other species. In Hevea, five miRNA families (HbmiR319, HbmiR156/157 and HbmiR165/166) have been found in more than forty plant species [57], and nine correspond to ancient miRNA already present in the common ancestor of Embryophytes (HbmiR156, HbmiR159/319, HbmiR160, HbmiR165/166, HbmiR171, HbmiR408 and HbmiR395), two in the common ancestor of Tracheophytes (HbmiR397 and HbmiR398) and nine in the common ancestor of Spermaphytes (HbmiR162, HbmiR164, HbmiR168, HbmiR169, HbmiR172, HbmiR393, HbmiR394, HbmiR399 and HbmiR827) [58]. Interestingly, the so-called monocotyledon-specific HbmiR444 family was identified in this study [58]. This suggests that HbmiR444 appeared before the speciation between monocotyledons and dicotyledons. Three woody species-specific miRNA families were also found in Hevea brasiliensis: HbmiR473 (Citrus sinensis and Populus trichocarpa), HbmiR476 (Populus trichocarpa) and HbmiR479 (Populus trichocarpa, Vitis vinifera, Gossypium hirsutum and Citrus sinensis) and were probably recruited independently by woody species during their evolution. The 10 novel miRNA families identified in this work are likely to be more recent, after Hevea diversification, as they have not been identified up to now in other euphorbiaceous plants. Furthermore, this information complements previous studies on euphorbiaceous species [49] in which only 25 conserved miRNAs were identified.

Our data could be used to study the evolution of MIR genes. We showed that 70% of the putatively new MIR genes were able to produce lmiRNA of 23-27 nucleotides, compared to 29% for conserved MIR genes. This corresponds to what has been observed in Arabidopsis, where it was previously suggested that young MIR genes consistently produced siRNAs, while ancient MIR genes produced predominantly canonical miRNAs, showing that the evolution of MIR genes is associated with changes in the use of DCL, resulting in specific miRNA size classes [59]. In addition, several so-called plant species-specific miRNAs were also found in Hevea, such as HbmiR1310 initially found only in Pinus taeda [53], HbmiR2910, HbmiR2914, HbmiR2915 and HbmiR2916 found in Populus euphratica [60] and HbmiR476 in Populus trichocarpa [61]. Our data suggest that those miRNAs should no longer be considered as species-specific miRNA.

Our study also revealed some conserved and divergent mechanisms at post-transcriptional level. Indeed, four families of miRNA were subjected to RNA editing events at the sites previously observed in Arabidopsis. However, for the thirteen families left, RNA editing was not observed under our experimental conditions. Under our experimental conditions, the -1 + UU modification was not observed in Hevea for the nine families selected from Ebhardt et al. [50]. Once the Hevea genome sequence is available, it will be possible to conduct an exhaustive analysis of the discrepancies between members of a multigenic family and RNA editing events.

Predicted target/miRNA pairs conserved between plant species and putative new ones in Hevea brasiliensis

In comparison with other plants, several regulation mechanisms seem to be conserved in Hevea brasiliensis, such as the regulation of HD-ZIP III protein by HbmiR166 [62], Squamosa promoter binding protein by HbmiR156 [63], NAC domain protein by HbmiR164 [64], APETALA2-like protein by HbmiR172 [6567], CCAAT-binding transcription factor by HbmiR169 and cysteine proteinase by HbmiR396 [44]. These ancient miRNAs regulate ancestral transcription factors that coordinate highly conserved functions, such as organ polarity and separation, cell division, or hormonal control [68, 69]. We predicted potentially new miRNA/target pairs in Hevea. New target/miRNA pairs were found for transcription factors, such as, for example, the NAC domain protein by HbmiR165 and HbmiRn7, the GRAS domain protein by HbmiR170, HbmiR396 and HbmiRn5, WRKY by HbmiR396 and HbmiR444, and the APETALA2-like transcription factor by HbmiR156, HbmiR159, HbmiR393, HbmiR396 and HbmiR408. (Duan et al., submitted). In addition, we also detected new pairs involved in protein degradation (ubiquitin protein ligase by HbmiRn6 and HbmiRn7), in response to hormones (ABA-insensitive by HbmiR166, ABA responsive element binding protein by HbmiR2910, and HVA22-like protein by HbmiR398, CTR by HbmiRn5) and in the regulation of miRNA (cap binding protein by HbmiR167). Further analyses are needed to validate the inhibition of these targets at transcriptional or translational level and to unravel specific regulation in Hevea brasiliensis regarding the response to stress in latex cells, and latex production.

Conserved and divergent regulation of redox homeostasis in Hevea brasiliensis

Several enzymes involved in ROS production or detoxification were found in our analysis. Two ROS-producing enzymes, Rboh, are likely to be targeted by HbmiR2914 and HbmiR476. They are key regulators in the ROS signalling network as they integrate different signal transduction pathways, such as calcium, protein phosphorylation and lipid signalling with ROS production [6]. Careful attention was also paid to targets classified in the GO term "antioxidant activity". For conserved miRNAs, two CuZnSOD chloroplastic and peroxisomal isoforms were predicted to be targeted by HbmiR398 and HbmiR159 respectively. Surprisingly, the CuZnSOD cytosolic isoform was not predicted by the LeARN pipeline and, even manually, no correct alignment was possible with HbmiR398. So far, validation by 5' RACE PCR has failed under our experimental conditions. By contrast, previous studies in Arabidopsis showed that HbmiR398 targeted both chloroplastic and cytosolic isoforms [34].

Some miRNA families have been reported to be H2O2-responsive in rice, such as OsmiR169, OsmiR397, OsmiR528, OsmiR827, OsmiR319 and OsmiR408 [70]. In rice, OsmiR397 targets laccase genes involved in lignin biosynthesis and those targets were also found for HbmiR397 in Hevea.

Conclusion

A library of small RNAs was deeply sequenced to identify conserved and novel miRNAs in Hevea brasiliensis. Combined with the Hevea transcript database, this led to the identification of 48 miRNA families and 10 novel miRNAs. Our study revealed new insights for evolutionary studies on woody plants and the potentially specific regulation of redox homeostasis in Hevea. All the putative targets of interest for Hevea stress biology and latex production will be experimentally validated and the target genes and their corresponding miRNAs will undergo co-expression analysis.

Methods

Production of in vitro plantlets

Embryogenic friable callus from line CI05538 of clone PB 260 was maintained on MM medium in the culture room at 27°C, with 60% humidity in the dark [71]. In vitro plants were regenerated according to the procedure described in [72]. Briefly, somatic embryogenesis was initiated by sub-culturing 1 gram of callus in 250 mL flasks containing 50 mL of a semi-solid Induction Medium. Pro-embryos were then developed in a temporary immersion system (RITA®, CIRAD, France) with 1 min of immersion per day in the liquid development media DEV1 and DEV2. For plant regeneration, only well-shaped mature embryos were collected after 8 weeks of culturing [73]. They were transferred to glass tubes on a semi-solid DEV3 medium for one month under a light intensity of 60 μmol m-2 s-1 and a 12 h light/dark photoperiod for embryo conversion into plants. Plantlets were then acclimatized in the greenhouse at 28°C with 60% relative humidity.

Plant material cultivation and treatments

Budded plants and two-year-old in vitro plants of clone PB260 were grown in the greenhouse at 27°C, with 50% humidity under natural light conditions. Budded plants and two-year-old in vitro plants of clone PB260 were exposed to cold at 4°C for 8 h, light (PAR = 1000-1500 μmol/m2/s for 4 h), drought (4 weeks for young budded plants and 10 days for two-year-old in vitro plants without watering), flooding (covered with water for 4 days), salinity (300 mM NaCl for 4 days) and wounding for 4 h (Table 2). Hormonal treatments were applied in hermetically sealed boxes, either with 5 ppm of ethylene for 4 h or 0.3 μM of methyl jasmonate for 4 h as described in [74, 75].

Isolation of small RNAs

For all treatments, leaves, bark and roots were sampled in liquid nitrogen and then frozen at -80°C pending small RNA isolation. Small RNAs were purified with the mirPremier microRNA Isolation Kit (Sigma-Aldrich, St. Louis, MO, USA), following the manufacturer's instructions for plant tissues. Briefly, 100 mg of the plant tissue powder was mixed with a lysis mix that released small RNA and, at the same time, inactivated ribonucleases and interfering secondary metabolites that might exist in plant tissues. Large RNA and genomic DNA remained insoluble and were removed from the lysate along with other cellular debris by brief centrifugation. Small RNA was then captured on a silica binding column in the presence of alcohol. Residual impurities were removed by wash solutions, and purified small RNA was eluted in RNase-free water.

Deep sequencing of small RNAs

Small RNA samples from callus and from control and treated plants were pooled. A Hevea small RNA library was constructed by the SKULTECH Company (France). Briefly, small RNAs were separated on a denaturing 15% polyacrylamide gel. The gel slices containing RNA with a size of about 15-50 nucleotides were excised and the RNA was eluted. Small RNAs were ligated to 5' and 3' adapters using T4 RNA ligase. Products from the second ligation were gel purified then reverse transcribed and amplified using PCR to produce cDNA. The PCR was performed with two primers that annealed to the ends of the adapters. PCR products were separated on 15% polyacrylamide gel with ethidium bromide staining. The gel slices containing DNA with a size of about 92 nucleotides were excised and the DNA was eluted. The resulting cDNA was sequenced using Solexa/Illumina technology (Illumina Inc., San Diego, CA, USA). Raw reads were trimmed using Trim_Adaptor application (non Open source application) with the minimum clip length fixed at 6 nucleotides.

Bioinformatic analyses

This work was supported by the high performance cluster of the SouthGreen Bioinformatics platform (southgreen.cirad.fr - UMR AGAP - CIRAD) comprising 26 compute nodes (208 Nehalem cores) and a 16 to storage capacity connected through a low latency Infiniband network. Sun Grid Engine was used as the workload manager to schedule jobs on the different compute nodes.

Identification of the conserved heveamiRNAs

Adapters and low-quality sequences were removed from the raw reads obtained from Illumina sequencing using cross-match software http://www.phrap.org/phredphrpconsed.html; the non-redundant dataset was produced using WU nrdb software. The unique small RNA sequences were then compared with BlastN [76] against the Arabidopsis genome (Phytozome V6) and the PMRD database [38]http://bioinformatics.cau.edu.cn/PMRD in order to classify the small RNAs and identify mature miRNAs in Hevea already known in several species.

Identification of HeveamiRNA precursors

Conserved miRNAs and putative novel miRNA precursors were identified in the PB260 transcriptome http://bassigny/cgi-bin/esttik_dev/quick_search.cgi using the LeARN pipeline http://symbiose.toulouse.inra.fr/LeARN/release_1;5.0[50], a platform for detecting, clustering and annotating non-coding RNAs. Novel miRNAs were separated into 4 classes according to their position on the stem-loop structure. Class 1 precursors only produced small RNAs corresponding to the predicted miR and miR* (i.e., small RNAs in a 25-bp region around the miR:miR* predicted region to be considered with two nucleotide overhangs linked to DCL action); class 2 comprised hairpins containing both predicted miR and miR* and additional small RNAs, of lower abundance, outside this region; class 3 involved precursors with reads on the defining small RNA region only (3a, at least two reads; 3b, only one read); class 4 consisted of precursors containing the defining small RNA plus reads of lower abundance outside the predicted miR:miR* duplex. Finally, class 5 contained hairpins that generated small RNAs of greater abundance than the defining RNA outside the predicted miR:miR* region [77]. Each miRNA precursor was checked manually following the rules described in [45]. Specifically, we considered dominant, mature sequences residing in the stem region of the stem-loop structure and ranging between 20-22 nt with a maximum free-folding energy of -25 kcal mol-1. A maximum of six unpaired nucleotides between the miRNA and miRNA* was allowed. The distance between the miRNA and miRNA* was fixed between 5 and 240 nucleotides.

Identification of putative HeveamiRNA targets

Computational identification of miRNA targets was performed with the Miranda toolbox included in the pipeline with the default parameters (gap_value = 2, mm_value = 1, gu_value = 0.5, score_threshold = 3, min_length_alignment = 20 and no_mismatch_positions = 10;11) for conserved miRNA and secondly checked with psRNAtarget http://plantgrn.noble.org/psRNATarget/(length_alignment = 18), [78]. For the putative novel miRNAs, psRNAtarget server (length_alignment = 18) and Miranda (gap_value = 2, mm_value = 1, gu_value = 0.5, score_threshold = 3, min_length_alignment = 18 and no_mismatch_positions = 10; 11) were used to scan the Hevea EST databases (Duan et al., to be submitted, http://bassigny/cgi-bin/esttik_dev/quick_search.cgi). A first assembly set was generated from reads of leaves, bark, latex, embryogenic tissues, and roots separately to create tissue-specific transcript databases with sequence names CLxxcontigxx. Then, reads from all tissues were used to generate one transcript sequence database for the Hevea clone PB260 with the sequence name hevea_454_xxx.

Gene Ontology analyses were performed with the Blast2GO program [79, 80]. In detail, sequence analyses, such as alignments and BLAST, were performed with Geneious software [81].

Total RNA extraction

Total RNAs from leaves, bark and roots subjected to abiotic stress treatments were extracted using the caesium chloride cushion method adapted from Sambrook [82] by Duan et al. [75]. One gram of fresh matter was ground and transferred to a tube containing 30 ml of extraction buffer consisting of 4 M guanidium isothiocyanate, 1% sarcosine, 1% polyvinylpyrrolidone and 1% ß-mercapto-ethanol. After homogenization, the tubes were kept on ice and then centrifuged at 10,000 g at 4°C for 30 minutes. The supernatant was transferred to a new tube containing 8 ml of 5.7 M CsCl. Ultracentrifugation in a swinging bucket was carried out at 89,705 g at 20°C for 20 hours. The supernatant and caesium cushion were discarded, whilst the RNA pellet was washed with 70% ethanol. After 30 minutes of air drying, the pellet was dissolved in 200 μl of sterile water. RNAs were conserved at -80°C.

Experimental validation of miRNA targets

The GeneRacer kit (Invitrogen, Carlsbad, CA, USA) was used to ligate a 5'RNA adapter to pooled RNAs prior to reverse transcription. The resulting cDNAs were used as a template for PCR amplification using one Gene-Specific Primer (GSP) designed downstream of the predicted miRNA:target binding site and one GeneRacer 5' forward primer. The primers used were HbCuZnSODchl R: GGGTAACCAGCAAATGCAAGCAGC, HbARF R: CTTGTGTTGAGTTGCAGCGCG and HbSquamosa R: TGGAGCCTAATTGGCTTCCTTCTGC. The first PCR product was then used for nest amplification performed with a nested GSP primer and a nested GeneRacer 5' forward primer. The nested primers used were HbCuZnSODchl nested R: GCAGGGAACAATGGCTGCC, HbARF nested R: ATGTTCAGCTCAGTGGACACGG, and HbSquamosa nested R: TTGTTTGGCACCACGCTTGTGGAAG. The PCR products were gel purified, cloned into PCR2.1 TOPO TA vector (Invitrogen, Carlsbad, CA, USA) and sequenced.

Complementary DNA (cDNA) synthesis and stem-loop RT-PCR

We checked for the absence of contaminating genomic DNA prior to cDNA synthesis. If genomic DNA was detected, a DNAse treatment was performed using TurboDNAse (Ambion, Texas, USA) following the manufacturer's instructions. Four micrograms of DNA-free RNAs was used for cDNA in a 40 μL reaction mixture using a RevertAid™ M-MuLV reverse transcriptase following the manufacturer's instructions (MBI, Fermentas, Canada). The primers used for the stem-loop amplification are presented in Table 9. RT-PCR cycling conditions comprised one denaturation cycle at 95°C for 2 min, followed by 35 amplification cycles (95°C for 20 s, 58°C for 30 s, and 72°C for 30 s). All primers were also validated by real-time PCR. The real-time PCR reaction mixtures consisted of 2 μL of RT product cDNA, 1 μL of 5 μM of each primer, and 3 μL 2 × SYBR green PCR master mix (LightCycler® 480 SYBR Green I Master, Roche Applied Sciences) in a 6 μL volume. PCR cycling conditions comprised one denaturation cycle at 95°C for 10 min, followed by 45 amplification cycles (95°C for 5 s and 60°C for 20 s). Melting curves were analysed to check the specificity of the PCR amplification (Table 9). The standard curve was generated using a two-fold dilution series of 10 points in triplicate from a mixed cDNA sample. This standard curve allowed the calculation of primer efficiencies (Table 9).
Table 9

Primer list for the stem-loop RT-PCR and the real-time PCR and their efficiencies

Precursor name

Forward primer

Reverse primer

Unique melting pick

PCR efficiency

Hbpre-miR156

TGGTGATGTTGTTGACAGAAGATAGAGAGC

GCACAAAGGAGTGAGATGCAGAGTCC

Yes

1,790

Hbpre-mir159a

GGTTAAGAAGTGGAGCTCCTTGAAGTC

GCTCCCTTCAATCCAAACAAGGATC

Yes

1,958

Hbpre-mir159b

GTGGAGCTCCTTGAAGTCCAATAGAGG

AGAGCTCCCTTCAATCCAAACAAGG

Yes

1,881

Hbpre-mir166b

GGGGAATGTTGTCTGGTTCGATG

TCAAATCAAACCCTGTTGGGGG

Yes

1,738

Hbpre-mir396

TGACCCTCTTCGTATTCTTCCACAGC

CCCACAGCTTTATTGAACCGCAAC

Yes

1,782

Hbpre-mir408b

GACATACAAAGACTGGGAACAGGCAG

GCCACAAGCCAGGGAAGAGGC

Yes

1,792

Hbpre-mir476

GCCTTGTATGTTTCATTTAGTAATCCTTCT

GATAATCCTTCTATGCAAAGTCTTTTATGC

Yes

1,732

Hbpre-mir2910

CAGGTCCAGACATAGTAAGGATTGACAG

CGTTAACGGATTAACCAGACAAATCGC

Yes

1,728

Hbpre-miRn1

ACCAGGAACTGGTATCAACCCAGC

TGCTACCAATGAATCGGACCCACC

Yes

1,837

Hbpre-miRn2

CAGTAAATAGCAGTATCGTGGATAGGG

GTCCAATCATTGATCCTGAAAATTTCTAC

Yes

1,828

Hbpre-miRn3

TGGATTGGAGCCCAATACTGTGAC

CTGCTCCATTGATTTTACCATCTATGC

Yes

1,873

Hbpre-miRn4

TGCGTGGGTAGATTGAGCTGC

GCTCCTGAGATATGCAAGCCACAAG

Yes

1,863

Hbpre-miRn6

ACCTAGGATGTAGAAGAGCATAAC

ACTACATGAGTGGATATATAGGAATCC

Yes

1,787

Hbpre-miRn7

ATCTGCTACTCGGATAACCGTAG

GCAGCAAATCTTCCATAGCATCC

Yes

1,925

Hbpre-miRn8

TTTCTTGATTCTAGTGGGTGGTG

GGAATTAACCAGACAAATCGCTC

Yes

1,905

Hbpre-miRn9

AGTCGGGGGCATTCGTATTTC

GCAAATACTTTCGCAGTTGTTCG

Yes

1,956

Hbpre-miRn10

TGGATTTATGAAAGACGAACAACTGCG

TTCGAGCCCCCCAATTTCG

Yes

1,712

Abbreviations

miRNAs: 

microRNAs

TPD: 

Tapping Panel Dryness

ROS: 

Reactive Oxygen Species

PMRD: 

Plant microRNA database

DCL1: 

RNAse III-like DICER-like I

HASTY: 

plant ortholog to exportin 5

RISC: 

RNA-induced silencing complex

AGO1: 

Argonaute 1

EST: 

Expressed Sequence Tag

Declarations

Acknowledgements

This work was supported by the Institut Français du Caoutchouc. The authors thank Gérald Oliver and Maryannick Rio for technical assistance. We are grateful to Dr Kuswanhadi and Tetty Chaidamsari for RNA isolation from the latex and bark of trees grown at the Sembawa Research Centre (Indonesia). The authors thank Peter Biggins for his revision of the English.

Notes: All precursors and mature miRNA sequences will be submitted to the MiRbase registry after acceptance of the manuscript for publication

Authors’ Affiliations

(1)
CIRAD, UMR AGAP
(2)
King Mongkut's University of Technology
(3)
CATAS, RRI, Danzhou

References

  1. De Faÿ E, Jacob JL, D'Auzac J, Jacob JL: Anatomical organization of the laticiferous system in the bark. Physiology of rubber tree latex. Edited by: Chrestin H. Boca Raton (FL): CRC Press; 1989:4-14.Google Scholar
  2. Lacote R, Gabla O, Obouayeba S, Eschbach JM, Rivano F, Dian K, Gohet E: Long-term effect of ethylene stimulation on the yield of rubber trees is linked to latex cell biochemistry. Field Crop Res. 2010, 115: 94-98. 10.1016/j.fcr.2009.10.007.View ArticleGoogle Scholar
  3. Venkatachalam P, Thulaseedharan A, Raghothama K: Molecular identification and characterization of a gene associated with the onset of tapping panel dryness (TPD) syndrome in rubber tree (Hevea brasiliensis Muell.) by mRNA differential display. Mol Biotechnol. 2009, 41 (1): 42-52. 10.1007/s12033-008-9095-y.PubMedView ArticleGoogle Scholar
  4. Chrestin H: Biochemical aspects of bark dryness induced by overstimulation of rubber trees with Ethrel. Physiology of rubber tree latex. Edited by: D'Auzac-J JJL, Chrestin H. 1989, Boca Raton (FL): CRC Press, 432-439.Google Scholar
  5. Chrestin H, Bangrantz J, D'Auzac J, Jacob JL: Role of the lutoidic tonoplast in the senescence and degeneration of the laticifer of Hevea brasiliensis. Zeitschrift-für-Pflanzenphysiologie. 1984, 114 (3): 261-268.View ArticleGoogle Scholar
  6. Suzuki N, Miller G, Morales J, Shulaev V, Torres MA, Mittler R: Respiratory burst oxidases: the engines of ROS signaling. Curr Opin Plant Biol. 2011, 14 (6): 691-699. 10.1016/j.pbi.2011.07.014.PubMedView ArticleGoogle Scholar
  7. Suzuki N, Koussevitzky S, Mittler R, Miller G: ROS and redox signalling in the response of plants to abiotic stress. Plant Cell Environ. 2012, 35 (2): 259-270. 10.1111/j.1365-3040.2011.02336.x.PubMedView ArticleGoogle Scholar
  8. Mittler R, Vanderauwera S, Suzuki N, Miller G, Tognetti VB, Vandepoele K, Gollery M, Shulaev V, Van Breusegem F: ROS signaling: the new wave?. Trends Plant Sci. 2011, 16 (6): 300-309. 10.1016/j.tplants.2011.03.007.PubMedView ArticleGoogle Scholar
  9. Jaspers P, Kangasjarvi J: Reactive oxygen species in abiotic stress signaling. Physiol Plant. 2010, 138 (4): 405-413. 10.1111/j.1399-3054.2009.01321.x.PubMedView ArticleGoogle Scholar
  10. Phillips JR, Dalmay T, Bartels D: The role of small RNAs in abiotic stress. FEBS Lett. 2007, 581 (19): 3592-3597. 10.1016/j.febslet.2007.04.007.PubMedView ArticleGoogle Scholar
  11. Sunkar R, Chinnusamy V, Zhu J, Zhu JK: Small RNAs as big players in plant abiotic stress responses and nutrient deprivation. Trends Plant Sci. 2007, 12 (7): 301-309. 10.1016/j.tplants.2007.05.001.PubMedView ArticleGoogle Scholar
  12. 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.PubMedView ArticleGoogle Scholar
  13. Shukla LI, Chinnusamy V, Sunkar R: The role of microRNAs and other endogenous small RNAs in plant stress responses. Biochim Biophys Acta. 2008, 1779 (11): 743-748.PubMedView ArticleGoogle Scholar
  14. Nonogaki H: MicroRNA gene regulation cascades during early stages of plant development. Plant Cell Physiol. 2010, 51 (11): 1840-1846. 10.1093/pcp/pcq154.PubMedView ArticleGoogle Scholar
  15. De-Kang L, Bai X, Li Y, Ding XD, Ge Y, Cai H, Ji W, Wu N, Zhu YM: Profiling of cold-stress-responsive miRNAs in rice by microarrays. Gene. 2010, 459 (1-2): 39-47. 10.1016/j.gene.2010.03.011.View ArticleGoogle Scholar
  16. 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.PubMedView ArticleGoogle Scholar
  17. Llave C, Kasschau KD, Rector MA, Carrington JC: Endogenous and silencing-associated small RNAs in plants. Plant Cell. 2002, 14 (7): 1605-1619. 10.1105/tpc.003210.PubMedPubMed CentralView ArticleGoogle Scholar
  18. Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004, 116 (2): 281-297. 10.1016/S0092-8674(04)00045-5.PubMedView ArticleGoogle Scholar
  19. Carrington JC, Ambros V: Role of microRNAs in plant and animal development. Science. 2003, 301 (5631): 336-338. 10.1126/science.1085242.PubMedView ArticleGoogle Scholar
  20. Lanet E, Delannoy E, Sormani R, Floris M, Brodersen P, Crete P, Voinnet O, Robaglia C: Biochemical evidence for translational repression by Arabidopsis microRNAs. Plant Cell. 2009, 21 (6): 1762-1768. 10.1105/tpc.108.063412.PubMedPubMed CentralView ArticleGoogle Scholar
  21. Zhang B, Pan X, Cobb GP, Anderson TA: Plant microRNA: a small regulatory molecule with big impact. Dev Biol. 2006, 289 (1): 3-16. 10.1016/j.ydbio.2005.10.036.PubMedView ArticleGoogle Scholar
  22. Chen X: MicroRNA biogenesis and function in plants. FEBS Lett. 2005, 579 (26): 5923-5931. 10.1016/j.febslet.2005.07.071.PubMedView ArticleGoogle Scholar
  23. Bonnet E, Van de Peer Y, Rouze P: The small RNA world of plants. New Phytol. 2006, 171 (3): 451-468. 10.1111/j.1469-8137.2006.01806.x.PubMedView ArticleGoogle Scholar
  24. Kim VN: Small RNAs: classification, biogenesis, and function. Mol Cells. 2005, 19 (1): 1-15. 10.1016/j.molcel.2005.05.026.PubMedView ArticleGoogle Scholar
  25. Xie Z, Kasschau KD, Carrington JC: Negative feedback regulation of Dicer-Like1 in Arabidopsis by microRNA-guided mRNA degradation. Curr Biol. 2003, 13 (9): 784-789. 10.1016/S0960-9822(03)00281-1.PubMedView ArticleGoogle Scholar
  26. Schauer SE, Jacobsen SE, Meinke DW, Ray A: DICER-LIKE1: blind men and elephants in Arabidopsis development. Trends Plant Sci. 2002, 7 (11): 487-491. 10.1016/S1360-1385(02)02355-5.PubMedView ArticleGoogle Scholar
  27. Bollman KM, Aukerman MJ, Park MY, Hunter C, Berardini TZ, Poethig RS: HASTY, the Arabidopsis ortholog of exportin 5/MSN5, regulates phase change and morphogenesis. Development. 2003, 130 (8): 1493-1504. 10.1242/dev.00362.PubMedView ArticleGoogle Scholar
  28. Chellappan P, Xia J, Zhou X, Gao S, Zhang X, Coutino G, Vazquez F, Zhang W, Jin H: siRNAs from miRNA sites mediate DNA methylation of target genes. Nucleic Acids Res. 2010, 38 (20): 6883-6894. 10.1093/nar/gkq590.PubMedPubMed CentralView ArticleGoogle Scholar
  29. Wu L, Zhou H, Zhang Q, Zhang J, Ni F, Liu C, Qi Y: DNA Methylation Mediated by a MicroRNA Pathway. Mol Cell. 2010, 38 (3): 465-475. 10.1016/j.molcel.2010.03.008.PubMedView ArticleGoogle Scholar
  30. Ebhardt HA, Tsang HH, Dai DC, Liu Y, Bostan B, Fahlman RP: Meta-analysis of small RNA-sequencing errors reveals ubiquitous post-transcriptional RNA modifications. Nucleic Acids Res. 2009, 37 (8): 2461-2470. 10.1093/nar/gkp093.PubMedPubMed CentralView ArticleGoogle Scholar
  31. Newman MA, Mani V, Hammond SM: Deep sequencing of microRNA precursors reveals extensive 3' end modification. RNA. 2011, 17 (10): 1795-1803. 10.1261/rna.2713611.PubMedPubMed CentralView ArticleGoogle Scholar
  32. Sunkar R, Zhu JK: Novel and stress-regulated microRNAs and other small RNAs from Arabidopsis. Plant Cell. 2004, 16 (8): 2001-2019. 10.1105/tpc.104.022830.PubMedPubMed CentralView ArticleGoogle Scholar
  33. Khraiwesh B, Zhu JK, Zhu J: Role of miRNAs and siRNAs in biotic and abiotic stress responses of plants. Biochim Biophys Acta. 2012, 1819 (2): 137-138.PubMedPubMed CentralView ArticleGoogle Scholar
  34. Sunkar R, Kapoor A, Zhu JK: Posttranscriptional induction of two Cu/Zn superoxide dismutase genes in Arabidopsis is mediated by downregulation of miR398 and important for oxidative stress tolerance. Plant Cell. 2006, 18 (8): 2051-2065. 10.1105/tpc.106.041673.PubMedPubMed CentralView ArticleGoogle Scholar
  35. Jagadeeswaran G, Saini A, Sunkar R: Biotic and abiotic stress down-regulate miR398 expression in Arabidopsis. Planta. 2009, 229 (4): 1009-1014. 10.1007/s00425-009-0889-3.PubMedView ArticleGoogle Scholar
  36. Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ: miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 2006, 34: 140-144. 10.1093/nar/gkj430.View ArticleGoogle Scholar
  37. Griffiths-Jones S, Saini HK, Van Dongen S, Enright AJ: miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008, 36: 154-158. 10.1093/nar/gkn221.View ArticleGoogle Scholar
  38. Zhang Z, Yu J, Li D, Liu F, Zhou X, Wang T, Ling Y, Su Z: PMRD: plant microRNA database. Nucleic Acids Res. 2006, 38: 806-813.View ArticleGoogle Scholar
  39. Archak S, Nagaraju J: Computational prediction of rice (Oryza sativa) miRNA targets. Genomics Proteomics Bioinformatics. 2007, 5 (3-4): 196-206. 10.1016/S1672-0229(08)60007-8.PubMedView ArticleGoogle Scholar
  40. Sunkar R, Girke T, Jain PK, Zhu JK: Cloning and characterization of microRNAs from rice. Plant Cell. 2005, 17 (5): 1397-1411. 10.1105/tpc.105.031682.PubMedPubMed CentralView ArticleGoogle Scholar
  41. Barakat A, Wall PK, Diloreto S, Depamphilis CW, Carlson JE: Conservation and divergence of microRNAs in Populus. BMC Genomics. 2007, 8: 481. 10.1186/1471-2164-8-481.PubMedPubMed CentralView ArticleGoogle Scholar
  42. Arazi T: MicroRNAs in the moss Physcomitrella patens. Plant Mol Biol. DOI: 10.1007/s11103-011-9761-5
  43. Mica E, Piccolo V, Delledonne M, Ferrarini A, Pezzotti M, Casati C, Del Fabbro C, Valle G, Policriti A, Morgante M, et al: High throughput approaches reveal splicing of primary microRNA transcripts and tissue specific expression of mature microRNAs in Vitis vinifera. BMC Genomics. 2009, 10: 558. 10.1186/1471-2164-10-558.PubMedPubMed CentralView ArticleGoogle Scholar
  44. Zhang B, Pan X, Stellwag EJ: Identification of soybean microRNAs and their targets. Planta. 2008, 229 (1): 161-182. 10.1007/s00425-008-0818-x.PubMedView ArticleGoogle Scholar
  45. Zhao CZ, Xia H, Frazier TP, Yao YY, Bi YP, Li AQ, Li MJ, Li CS, Zhang BH, Wang XJ: Deep sequencing identifies novel and conserved microRNAs in peanuts (Arachis hypogaea L.). BMC Plant Biol. 2010, 10: 3. 10.1186/1471-2229-10-3.PubMedPubMed CentralView ArticleGoogle Scholar
  46. Zhang J, Zeng R, Chen J, Liu X, Liao Q: Identification of conserved microRNAs and their targets from Solanum lycopersicum Mill. Gene. 2008, 423 (1): 1-7. 10.1016/j.gene.2008.05.023.PubMedView ArticleGoogle Scholar
  47. Xie FL, Huang SQ, Guo K, Xiang AL, Zhu YY, Nie L, Yang ZM: Computational identification of novel microRNAs and targets in Brassica napus. FEBS Lett. 2007, 581 (7): 1464-1474. 10.1016/j.febslet.2007.02.074.PubMedView ArticleGoogle Scholar
  48. Arenas-Huertero C, Perez B, Rabanal F, Blanco-Melo D, De la Rosa C, Estrada-Navarrete G, Sanchez F, Covarrubias AA, Reyes JL: Conserved and novel miRNAs in the legume Phaseolus vulgaris in response to stress. Plant Mol Biol. 2009, 70 (4): 385-401. 10.1007/s11103-009-9480-3.PubMedView ArticleGoogle Scholar
  49. Zeng C, Wang W, Zheng Y, Chen X, Bo W, Song S, Zhang W, Peng M: Conservation and divergence of microRNAs and their functions in Euphorbiaceous plants. Nucleic Acids Res. 2010, 38 (3): 981-995. 10.1093/nar/gkp1035.PubMedPubMed CentralView ArticleGoogle Scholar
  50. Ebhardt HA, Fedynak A, Fahlman RP: Naturally occurring variations in sequence length creates microRNA isoforms that differ in argonaute effector complex specificity. Silence. 2009, 1 (1): 12.View ArticleGoogle Scholar
  51. Noirot C, Gaspin C, Schiex T, Gouzy J: LeARN: a platform for detecting, clustering and annotating non-coding RNAs. BMC Bioinformatics. 2008, 9: 21. 10.1186/1471-2105-9-21.PubMedPubMed CentralView ArticleGoogle Scholar
  52. Rajagopalan R, Vaucheret H, Trejo J, Bartel DP: A diverse and evolutionarily fluid set of microRNAs in Arabidopsis thaliana. Genes Dev. 2006, 20 (24): 3407-3425. 10.1101/gad.1476406.PubMedPubMed CentralView ArticleGoogle Scholar
  53. Morin RD, Aksay G, Dolgosheina E, Ebhardt HA, Magrini V, Mardis ER, Sahinalp SC, Unrau PJ: Comparative analysis of the small RNA transcriptomes of Pinus contorta and Oryza sativa. Genome Res. 2008, 18 (4): 571-584. 10.1101/gr.6897308.PubMedPubMed CentralView ArticleGoogle Scholar
  54. Qiu D, Pan X, Wilson IW, Li F, Liu M, Teng W, Zhang B: High throughput sequencing technology reveals that the taxoid elicitor methyl jasmonate regulates microRNA expression in Chinese yew (Taxus chinensis). Gene. 2009, 436 (1-2): 37-44. 10.1016/j.gene.2009.01.006.PubMedView ArticleGoogle Scholar
  55. Zhang J, Xu Y, Huan Q, Chong K: Deep sequencing of Brachypodium small RNAs at the global genome level identifies microRNAs involved in cold stress response. BMC Genomics. 2009, 10: 449. 10.1186/1471-2164-10-449.PubMedPubMed CentralView ArticleGoogle Scholar
  56. Uthup TK, Ravindran M, Bini K, Thakurdas S: Divergent DNA Methylation Patterns Associated with Abiotic Stress in Hevea brasiliensis. Mol Plant. 2011, DOI: 10.1093/mp/ssr039Google Scholar
  57. Sunkar R, Jagadeeswaran G: In silico identification of conserved microRNAs in large number of diverse plant species. BMC Plant Biol. 2008, 8: 37. 10.1186/1471-2229-8-37.PubMedPubMed CentralView ArticleGoogle Scholar
  58. Cuperus JT, Fahlgren N, Carrington JC: Evolution and functional diversification of MIRNA genes. Plant Cell. 2011, 23 (2): 431-442. 10.1105/tpc.110.082784.PubMedPubMed CentralView ArticleGoogle Scholar
  59. Vazquez F, Blevins T, Ailhas J, Boller T, Meins F: Evolution of Arabidopsis MIR genes generates novel microRNA classes. Nucleic Acids Res. 2008, 36 (20): 6429-6438. 10.1093/nar/gkn670.PubMedPubMed CentralView ArticleGoogle Scholar
  60. Li B, Yin W, Xia X: Identification of microRNAs and their targets from Populus euphratica. Biochem Biophys Res Commun. 2009, 388 (2): 272-7. 10.1016/j.bbrc.2009.07.161.PubMedView ArticleGoogle Scholar
  61. 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.PubMedPubMed CentralView ArticleGoogle Scholar
  62. Yao X, Wang H, Li H, Yuan Z, Li F, Yang L, Huang H: Two types of cis-acting elements control the abaxial epidermis-specific transcription of the MIR165a and MIR166a genes. FEBS Lett. 2009, 583 (22): 3711-3717. 10.1016/j.febslet.2009.10.076.PubMedView ArticleGoogle Scholar
  63. Chen X, Zhang Z, Liu D, Zhang K, Li A, Mao L: SQUAMOSA promoter-binding protein-like transcription factors: star players for plant growth and development. J Integr Plant Biol. 2010, 52 (11): 946-951. 10.1111/j.1744-7909.2010.00987.x.PubMedView ArticleGoogle Scholar
  64. 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.PubMedPubMed CentralView ArticleGoogle Scholar
  65. Chen X: A microRNA as a translational repressor of APETALA2 in Arabidopsis flower development. Science. 2004, 303 (5666): 2022-2025. 10.1126/science.1088060.PubMedView ArticleGoogle Scholar
  66. Wollmann H, Mica E, Todesco M, Long JA, Weigel D: On reconciling the interactions between APETALA2, miR172 and AGAMOUS with the ABC model of flower development. Development. 2010, 137 (21): 3633-3642. 10.1242/dev.036673.PubMedPubMed CentralView ArticleGoogle Scholar
  67. Zhu QH, Helliwell CA: Regulation of flowering time and floral patterning by miR172. J Exp Bot. 2011, 62 (2): 487-495. 10.1093/jxb/erq295.PubMedView ArticleGoogle Scholar
  68. Allen E, Xie Z, Gustafson AM, Sung GH, Spatafora JW, Carrington JC: Evolution of microRNA genes by inverted duplication of target gene sequences in Arabidopsis thaliana. Nat Genet. 2004, 36 (12): 1282-1290. 10.1038/ng1478.PubMedView ArticleGoogle Scholar
  69. Axtell MJ: Evolution of microRNAs and their targets: are all microRNAs biologically relevant?. Biochim Biophys Acta. 2008, 1779 (11): 725-734.PubMedView ArticleGoogle Scholar
  70. Li T, Li H, Zhang YX, Liu JY: Identification and analysis of seven H2O2-responsive miRNAs and 32 new miRNAs in the seedlings of rice (Oryza sativa L. ssp. indica). Nucleic Acids Res. 2011, 39 (7): 2821-2833. 10.1093/nar/gkq1047.PubMedPubMed CentralView ArticleGoogle Scholar
  71. Carron M-P, Etienne H, Lardet L, Campagna S, Perrin Y, Leconte A, Chaine C: Somatic embryogenesis in rubber (Hevea brasiliensis Müll. Arg.). Somatic embryogenesis in woody plants. Edited by: Jain SGP, Newton R. Dordrecht: Kluwer Academic Publishers; 1995:117-136.View ArticleGoogle Scholar
  72. Lardet L, Martin F, Dessailly F, Carron MP, Montoro P: Effect of exogenous calcium on post-thaw growth recovery and subsequent plant regeneration of cryopreserved embryogenic calli of Hevea brasiliensis (Mull. Arg.). Plant Cell Rep. 2007, 26 (5): 559-569. 10.1007/s00299-006-0278-3.PubMedView ArticleGoogle Scholar
  73. Lardet L, Piombo G, Oriol F, Dechamp E, Carron M: Relation between biochemical characteristics and conversion ability in Hevea brasiliensis zygotic and somatic embryos. Can J Bot. 1999, 77: 1168-1177.Google Scholar
  74. Kuswanhadi , Leclercq J, Rio M, Tregear J, Ducamp-Collin MN, Montoro P: Isolation of three members of the multigene family encoding ACC oxidases in Hevea brasiliensis and investigation of their responses to ethylene stimulation and wounding. Journal of rubber research. 2010, 13 (3): 185-205.Google Scholar
  75. Duan C, Rio M, Leclercq J, Bonnot F, Oliver G, Montoro P: Gene expression pattern in response to wounding, methyl jasmonate and ethylene in the bark of Hevea brasiliensis. Tree Physiol. 2010, 30 (10): 1349-1359. 10.1093/treephys/tpq066.PubMedView ArticleGoogle Scholar
  76. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol. 1990, 215 (3): 403-410.PubMedView ArticleGoogle Scholar
  77. Lelandais-Briere C, Naya L, Sallet E, Calenge F, Frugier F, Hartmann C, Gouzy J, Crespi M: Genome-wide Medicago truncatula small RNA analysis revealed novel microRNAs and isoforms differentially regulated in roots and nodules. Plant Cell. 2009, 21 (9): 2780-2796. 10.1105/tpc.109.068130.PubMedPubMed CentralView ArticleGoogle Scholar
  78. Dai X, Zhao PX: psRNATarget: a plant small RNA target analysis server. Nucleic Acids Res. 2011, 39 (2): W155-W159. 10.1093/nar/gkr829.PubMedPubMed CentralView ArticleGoogle Scholar
  79. Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M: Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005, 21 (18): 3674-3676. 10.1093/bioinformatics/bti610.PubMedView ArticleGoogle Scholar
  80. Götz S, Garcia-Gomez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ, Robles M, Talon M, Dopazo J, Conesa A: High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 2008, 36 (10): 3420-3435. 10.1093/nar/gkn176.PubMedPubMed CentralView ArticleGoogle Scholar
  81. Drummond AJ, Ashton B, Buxton S, Cheung M, Cooper A, Heled J, Kearse M, Moir R, Stones-Havas S, Sturrock S, et al: Geneious v5.3. 2010, Available from http://www.geneious.com/Google Scholar
  82. Sambrook J, Fritsch EF, Maniatis T: Molecular cloning. A laboratory manual. Edited by: CS H. New York: Cold Spring Harbor Laboratory Press; 1989.Google Scholar

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