Open Access

A computational-based update on microRNAs and their targets in barley (Hordeum vulgare L.)

  • Moreno Colaiacovo1Email author,
  • Annalisa Subacchi1,
  • Paolo Bagnaresi1,
  • Antonella Lamontanara1,
  • Luigi Cattivelli1 and
  • Primetta Faccioli1
BMC Genomics201011:595

DOI: 10.1186/1471-2164-11-595

Received: 30 April 2010

Accepted: 22 October 2010

Published: 22 October 2010

Abstract

Background

Many plant species have been investigated in the last years for the identification and characterization of the corresponding miRNAs, nevertheless extensive studies are not yet available on barley (at the time of this writing). To extend and to update information on miRNAs and their targets in barley and to identify candidate polymorphisms at miRNA target sites, the features of previously known plant miRNAs have been used to systematically search for barley miRNA homologues and targets in the publicly available ESTs database. Matching sequences have then been related to Unigene clusters on which most of this study was based.

Results

One hundred-fifty-six microRNA mature sequences belonging to 50 miRNA families have been found to significantly match at least one EST sequence in barley. As expected on the basis of phylogenetic relations, miRNAs putatively orthologous to those of Triticum are significantly over-represented inside the set of identified barley microRNA mature sequences. Many previously known and several putatively new miRNA/target pairs have been identified. When the predicted microRNA targets were grouped into functional categories, biological processes previously known to be regulated by miRNAs, such as development and response to biotic and abiotic stress, have been highlighted and most of the target molecular functions were related to transcription regulation. Candidate microRNA coding genes have been reported and genetic variation (SNPs/indels) both in functional regions of putative miRNAs (mature sequence) and at miRNA target sites has been found.

Conclusions

This study has provided an update of the information on barley miRNAs and their targets representing a foundation for future studies. Many of previously known plant microRNAs have homologues in barley with expected important roles during development, nutrient deprivation, biotic and abiotic stress response and other important physiological processes. Putative polymorphisms at miRNA target sites have been identified and they can represent an interesting source for the identification of functional genetic variability.

Background

MicroRNAs (miRNAs) are a class of non-coding small RNAs with fundamental roles in key plant biological processes such as development, signal transduction and environmental stress response [1]. miRNAs act on gene regulation at post-transcriptional level, a phenomenon known in plants as PTGS (Post Transcriptional Gene Silencing), through sequence-based interaction with target mRNAs.

Many plant species have been investigated during recent years for miRNAs identification and characterization. The current information available on barley refers to two papers [2, 3]. In particular, the paper of Dryanova et al. reports detailed information on both targets and miRNA coding sequences from Hordeum vulgare and for other members of Triticeae tribe, to which barley belongs [2]. However, extensive studies describing the organization of miRNA families, specifically in barley, are not yet available (at the time of this writing) and no miRNAs have been deposited in the publicly available miRNA database (miRBase, http://www.mirbase.org), this despite the economic importance of barley and its role as model species for Triticeae[4].

The conservation of miRNA sequences across species provides a powerful tool for the identification of novel miRNA genes based on homology with miRNAs previously described in other species. Search based on evolutionary conservation has allowed the identification of miRNA families in many plant species, including those where the complete genome sequence is not available, as it is currently the case of barley. Without genome sequence information a powerful alternative data source comes from ESTs (Expressed Sequence Tags): currently 501,616 ESTs are available in barley http://www.ncbi.nlm.nih.gov/dbEST/dbEST_summary.html[5].

The identification of target genes is a fundamental step for the determination of the biological function of microRNAs, besides being an indirect evidence for their existence. Evolutionary conserved targets have proven very helpful to test the effectiveness of miRNA target detection. The perfect or near perfect complementarity between a miRNA and its target mRNA, that is a peculiar feature of plant miRNAs, gives a powerful tool for the identification of target genes through BLAST analysis of miRNA mature sequences vs EST/genomic sequences. A large part of the "in silico" predicted targets have then been confirmed as bona fide targets by experimental approaches including Northern, 5'-RACE and, more recently, degradome analysis via NGS (Next Generation Sequencing) [6, 7].

The correct binding of miRNA to its cognate mRNA is critical for regulating the mRNA level and protein expression. This binding can be affected by single-nucleotide polymorphisms or indels in the miRNA target site leading to the suppression of existing binding sites or the generation of illegitimate ones. Therefore, small polymorphisms in miRNA targets can have a relevant effect on gene and protein expression and represent a type of genetic variability that can influence agronomical traits. As an example, overexpression of miR156b and miR156h in rice results in severe dwarfism, strongly reduced panicle size and delayed heading date [8].

To extend and to update information about miRNAs and their targets in barley and to identify candidate polymorphisms at microRNA target sites, barley EST sequences have been screened and related to Unigene clusters. UniGene is an experimental system for partitioning transcript sequences into a non-redundant set of gene-oriented clusters. Thus each UniGene cluster contains sequences that appear to come from the same transcription locus (gene or expressed pseudogene) http://www.ncbi.nlm.nih.gov/UniGene/index.html. Mining SNPs from ESTs allows the exploitation of genetic variability based on published sequences and the analysis of Unigene clusters can be very helpful for this purpose [9].

Results and Discussion

Barley miRNAs

Since only mature miRNA sequences rather than precursor sequences are conserved among plant species, mature miRNA sequences have been used as queries for BLAST search against Hordeum vulgare ESTs [10]. One hundred-fifty-six microRNA mature sequences belonging to 50 miRNA families have been found to significantly match at least one EST sequence in barley (the total number of matching ESTs was 855 - as reported in additional files 1 and 2) and could actually be related both to target or miRNA sequences, even if the probability is lower for the latter. Indeed the estimated frequency of pri-miRNAs in T. aestivum EST collection is as low as 0.003% [2].

The results illustrated above have been compared with those reported by Dryanova et al. where miRNAs and their targets have been searched in the Triticeae tribe [2]. Among the 33 miRNA families identified by Dryanova et al. in at least one species of the Triticeae tribe, 22 families were found in barley and 17 of them overlap with the present findings. Regarding barley, some miRNA families were found in just one of the two papers. Dryanova et al. found evidences for 5 additional miRNA families while the present work has found evidences in barley for miR390 and miR396 previously reported only in T. aestivum, and for additional 31 families not found by Dryanova et al. in anyone of the investigated species (i.e. miR442, miR529). The reasons for these discrepancies can be ascribed to the different miRBase release used (miRBase Release 8.0 for Dryanova et al., 2008 and miRBase Release 13.0 in the present work) and partially to differences in the BLAST settings adopted. Monocot-specific miRNAs (i.e. miR444) have also been found in both works [11].

Statistical analysis was employed to identify over and under-represented plant species from which the corresponding barley miRNA comes from. As reported in table 1 and 2, barley miRNA sequences putative orthologous to those of Triticum are significantly over-represented in our data also when very stringent p-value, e.g. 0.001, was used. Hordeum and Triticum genera are both members of the Poaceae family, Pooideae subfamily, Triticeae tribe. H. vulgare is often used as a model species for Triticeae, thanks to its diploid genome that could facilitate genome-wide searches of miRNAs.
Table 1

Statistical analysis for the identification of over and under-represented plant species.

 

Initial dataset

H.vulgare

 

n° of mature sequences (redundant set)

%

n° of mature sequences (redundant set) matching at least one barley EST

%

p-value

Arabidopsis thaliana

207

10.7

43

8.7

0.019

Oryza sativa

415

21.5

102

20.5

0.038

Glycine max

79

4.1

15

3.0

0.046

Pinus taeda

38

2.0

6

1.2

0.068

Triticum aestivum

32

1.7

20

4.0

2.0 × 10-4

Physcomitrella patens

281

14.6

39

7.8

1.7 × 10-6

Populus trichocarpa

237

12.3

71

14.3

0.021

Chlamydomonas reinhardtii

84

4.4

2

0.4

6.3 × 10-8

Selaginella moellendorffii

64

3.3

12

2.4

0.058

Vitis vinifera

140

7.3

47

9.5

0.012

Brassica napus

44

2.3

19

3.8

0.010

Gossypium hirsutum

13

0.7

4

0.8

0.185

Medicago truncatula

46

2.4

8

1.6

0.068

Solanum lycopersicum

30

1.6

11

2.2

0.065

Sorghum bicolor

72

3.7

27

5.4

0.014

Zea mays

98

5.1

48

9.7

1.1 × 10-5

Brassica oleracea

7

0.4

2

0.4

0.268

Brassica rapa

19

1.0

8

1.6

0.061

Saccharum officinarum

16

0.8

11

2.2

2.3 × 10-3

Gossypium herbecium

1

0.1

0

0.0

0.773

Carica papaya

1

0.1

0

0.0

0.773

Vigna unguiculata

1

0.1

0

0.0

0.773

Lotus japonicus

2

0.1

0

0.0

0.597

Gossypium rammindii

2

0.1

2

0.4

0.079

Total

1929

 

497

  

For each species, the table shows the number of mature sequences from the redundant set of 1929 sequences stored in the miRBase and number of mature sequences matching at least one barley EST. It also shows the p-value calculated with a binomial distribution.

Table 2

Over and under-represented plant species within barley miRNAs identified with respect to the stringency chosen for the p-value.

Threshold

Over-represented plant species

Under-represented plant species

p-value ≤0.05

Triticum aestivum

Arabidopsis thaliana

 

Populus trichocarpa

Oryza sativa

 

Vitis vinifera

Glycine max

 

Brassica napus

Physcomitrella patens

 

Sorghum bicolor

Chlamydomonas reinhardtii

 

Zea mays

 
 

Saccharum officinarum

 

p-value ≤ 0.01

Triticum aestivum

Physcomitrella patens

 

Zea mays

Chlamydomonas reinhardtii

 

Saccharum officinarum

 

p-value ≤ 0.005

Triticum aestivum

Physcomitrella patens

 

Zea mays

Chlamydomonas reinhardtii

 

Saccharum officinarum

 

p-value ≤ 0.001

Triticum aestivum

Physcomitrella patens

 

Zea mays

Chlamydomonas reinhardtii

Zea mays is also closely related to barley being part of monocot group and Poaceae family. Oryza sativa although is part of Poaceae family is under-represented, when a low stringent p-value (0.05) was used.

Some ESTs have matched to more than one miRNAs belonging either to the same family or to different families (additional file 3). The first case can be due to the high level of similarity among mature sequences from different members of the same family, while ESTs matching to different miRNA families could represent examples of multi-microRNA based control.

Transcripts targeted by more than one miRNA have also been found also in other plant species such as rice [12]. These findings are common in animals where many different miRNAs recognize the same target mRNA, usually at the 3'UTR [13].

To identify and annotate potential microRNA-regulated genes in barley, the 855 matching ESTs were related to Unigene clusters. Clusters annotated as protein-coding sequences were then selected for subsequent analysis and listed in tables 3 and 4. A total of 121 different Unigene clusters putatively representing the targets for 37 miRNA families has been found. Similar results (e.g. on average more than 1 putative target/miRNA family) were reported by Zhang et al. in maize (115 target for 26 miRNA families) [14]. Sometimes different targets for a specific miRNA are members of the same gene family (e.g. miR156-SBP family), while in other cases there is no evident relationship among the putative targets of a given miRNA (e.g. miR1121). Previous studies report six targets or fewer for most Arabidopsis miRNAs, a number significantly lower than in animals, for example, in Drosophila each miRNA has on average over 50 predicted targets [13, 15].
Table 3

miRNA target genes identified in barley and confirmed by previous studies

miRNA family

miRNA name

Unigene

Unigene annotation

Literature reported target for this miRNA (citation number in brackets)

156

miR156

Hv.29207

protein coding (SBP domain)

Arabidopsis thaliana[24]

 

miR156

Hv.5875

protein coding (SBP domain)

Oryza sativa[8]

 

miR156

Hv.28351

protein coding (SBP domain)

Hordeum vulgare[2]

 

miR156

Hv.21387

SPL2 (SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 2)

Triticum aestivum[2]

 

miR156

Hv.28414

SPL5 (SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 5)

 
    

Arabidopsis thaliana[24]

159

miR159

Hv.12

MYB family transcription factor

Oryza sativa[39]

    

Hordeum vulgare[2]

    

Triticum aestivum[2]

    

Arabidopsis thaliana[24]

160

miR160

Hv.5089

ARF16 (AUXIN RESPONSE FACTOR 16)

Oryza sativa[39]

    

Hordeum vulgare[2]

    

Triticum aestivum[2]

164

miR164

Hv.877

NAC domain containing protein

Arabidopsis thaliana[24]

 

miR164

Hv.28795

NAC domain containing protein

Zea mays[14]

 

miR164

Hv.25370

NAM superfamily

Hordeum vulgare[2]

 

miR164

Hv.21779

NAC domain containing protein

Triticum aestivum[2]

168

miR168

Hv.26206

AGO1 (ARGONAUTE 1)

Arabidopsis thaliana[24]

 

miR168

Hv.19452

AGO1 (ARGONAUTE 1)

Hordeum vulgare[2]

    

Triticum aestivum[2]

169

miR169

Hv.13681

CCAAT-binding transcription factor (CBF-B/NF-YA) family protein

Aquilegia coerulea[40]

 

miR169

Hv.406

CCAAT-binding transcription factor (CBF-B/NF-YA) family protein

Hordeum vulgare[2]

 

miR169

Hv.9532

CCAAT-binding transcription factor (CBF-B/NF-YA) family protein

Triticum aestivum[2]

    

Arabidopsis thaliana[24]

171

miR171

Hv.9855

GRAS family transcription factor

Brachypodium distachyon[41]

    

Hordeum vulgare[2]

    

Triticum aestivum[2]

    

Arabidopsis[42]

172

miR172

Hv.6575

RAP2.7/TOE1 (TARGET OF EAT1 1), AP2 superfamily

Hordeum vulgare[2]

    

Triticum aestivum[2]

393

miR393

Hv.29376

AFB2 (AUXIN SIGNALING F-BOX 2), auxin binding/ubiquitin-protein ligase

Aquilegia coerulea[40]

 

miR393

Hv.2498

TIR1 (TRANSPORT INHIBITOR RESPONSE 1), ubiquitin-protein ligase

Hordeum vulgare[2]

    

Triticum aestivum[2]

    

Aquilegia coerulea[40]

394

miR394

Hv.8877

F-box family protein

Hordeum vulgare[2]

    

Triticum aestivum[2]

    

Aquilegia coerulea[40]

395

miR395

Hv.12870

ATPS1

Hordeum vulgare[2]

    

Triticum aestivum[2]

396

miR396

Hv.28722

WRC, QLQ

 
 

miR396

Hv.22031

growth-regulating factor

Arabidopsis thaliana[39]

 

miR396

Hv.19321

WRC, QLQ

Oryza sativa[39]

 

miR396

Hv.9742

WRC, QLQ

Triticum aestivum[2]

399

miR399

Hv.5443

ATUBC24/PHO2/UBC24 (PHOSPHATE 2), ubiquitin-protein ligase

Arabidopsis thaliana[39]

    

Oryza sativa[39]

408

miR408

Hv.10831

ARPN (PLANTACYANIN), copper ion binding (Cu-bind-like superfamily)

Medicago truncatula[43]

    

Populus trichocarpa[44]

 

miR408

Hv.24052

Plastocyanin-like domain-containing protein (Cu-bind-like superfamily)

Oryza sativa[45]

    

Hordeum vulgare[2]

    

Triticum aestivum[2]

 

miR408

Hv.20945

ARGONAUTE like superfamily

Hordeum vulgare[2]

529

miR529

Hv.29207

protein coding (SBP domain)

Aquilegia coerulea[40]

 

miR529

Hv.28351

protein coding (SBP domain)

Zea Mays[46]

    

Arabidopsis thaliana[47]

827

miR827

HV. 10218

SPX superfamily, MFS superfamily

Oryza sativa[48]

Table 4

Novel miRNA target genes identified

miRNA family

miRNA name

Unigene

Unigene annotation

Functional annotation

390

miR390

Hv.15993

protease inhibitor, seed storage, lipid transfer protein (LTP) family protein

lipid transport

441

miR1126

Hv.10635

beta-adaptin

protein transport

 

miR1126

Hv.25101

ankyrin protein kinase, serine/threonine protein kinase

regulation in signal transduction

 

miR1126

Hv.18172

protein coding

unknown function

 

miR1126

Hv.5267

SRT2, DNA binding

vernalization, auxin signalling

818

miR818+1436

Hv.11323

protein coding

unknown function

 

miR818+1436

Hv.9623

NLI interacting factor (NIF) family protein

phosphatase activity

 

miR1436

Hv.8609

Coproporphyrinogen III oxidase

chlorophyll biosynthesis

 

miR1436

Hv.16854

P-loop NTPase superfamily

unknown function

 

miR1436

Hv.8351

protein coding

unknown function

 

miR1436

Hv.28025

protein coding

unknown function

 

miR1436

Hv.27779

Vps51 superfamily

vescicular transport

 

miR1436

Hv.19811

ILL3 (IAA-amino acid hydrolase ILR1-like 3), metallopeptidase

stress and hormone response

 

miR1436

Hv.18734

MAP kinase

signal transduction, stress signalling

 

miR1436

Hv.15543

protein coding

unknown function

 

miR1436

Hv.12920

PKc-like superfamily

abiotic stress resistance

 

miR1436

Hv.11057

Integral membrane family protein

endomembrane system

 

miR1436

Hv.3476

protein coding

unknown function

 

miR1439

Hv.19109

PKc-like superfamily

unknown function

 

miR1439

Hv.23816

exo-endo-phos superfamily

unknown function

 

miR1439

Hv.11224

tatD-related deoxyribonuclease family protein

deoxyribonuclease activity

821

miR821

Hv.3660

GDH1 (Glutamate dehydrogenase)

nitrogen metabolism

1030

miR1030

Hv.12064

AS1/ATMYB91/ATPHAN/MYB91 (ASYMMETRIC LEAVES 1, MYB DOMAIN PROTEIN)

transcription factor

 

miR1030

Hv.7960

protein coding

unknown function

 

miR1030

Hv.14867

RNA recognition motif (RRM)-containing protein

post-transcriptional gene expression processes

1119

miR1119

Hv.29225

protein coding

unknown function

 

miR1119

Hv.29210

protein coding

unknown function

 

miR1119

Hv.27666

protein coding

unknown function

 

miR1119

Hv.23883

ADF2 (ACTIN DEPOLYMERIZING FACTOR 2), actin binding

actin turnover, stress response, plant defense signalling pathway

 

miR1119

Hv.23689

RRM superfamily, RNA binding

involved in post-transcriptional gene expression processes

 

miR1119

Hv.23343

molybdenum cofactor sulfurase family protein, superfamily

stress response

1120

miR1120

Hv.21827

protein coding

unknown function

 

miR1121

Hv.464

serine/threonine kinase

response to salt stress

 

miR1121

Hv.20180

Kelch repeat-containing protein

unknown function

 

miR1121

Hv.2132

protein coding

unknown function

 

miR1121

Hv.26959

POK (POKY POLLEN TUBE)

pollen tube growth

 

miR1121

Hv.20763

SRG1 (SENESCENCE-RELATED GENE 1), oxidoreductase

flavonoid biosyntetic processes and senescence

 

miR1121

Hv.20600

serine/threonine protein kinase, PKc-like superfamily

abiotic stress resistance

 

miR1121

Hv.12124

ATPase family AAA domain-containing protein

unknown function

 

miR1121

Hv.10391

protein coding

unknown function

 

miR1121

Hv.9294

protein coding

unknown function

 

miR1121

Hv.6581

protein coding

unknown function

 

miR1121

Hv.6532

ATPase-Plipid, haloacid dehalogenase-like hydrolase family protein

ATPase activity

 

miR1121

Hv.4756

FAR1 superfamily, MULE transposon domain

light control of development

 

miR1121

Hv.3142

CRS1-YhbY (CRM domain) superfamily

RNA binding/intron splicing

1122

miR1122

Hv.12219

serine/threonine protein kinase, PKc-like superfamily

abiotic stress resistance

 

miR1128+1133

Hv.23560

indole-3-glycerol phosphate synthase, TIM-phosphate binding superfamily

aminoacid biosynthesis

 

miR1128+1133

Hv.679

UBIQUITIN CARRIER PROTEIN, ubiquitin-protein ligase

ubiquitination

 

miR1128+ 1133+1136

Hv.26146

AIM1 (ABNORMAL INFLORESCENCE MERISTEM), enoyl-CoA hydratase

auxin metabolism

 

miR1128+1135

Hv.23257

integral membrane HPP family protein

unknown function

 

miR1128+1135

Hv.21122

SOS5 (SALT OVERLY SENSITIVE 5)

salt signalling/osmo-stress

 

miR1128

Hv.17314

protein coding

unknown function

 

miR1128

Hv.14876

ARF-GAP DOMAIN, C2 superfamily

vescicle traffic/development

 

miR1128+1133

Hv.12752

ATP-dependent peptidase, ATPase, metallopeptidase

peptidase activity

 

miR1128+ 1133+1136

Hv.6454

oligopeptide transporter

oligopeptide transporter

 

miR1128+1133

Hv.3596

Cysteine hydrolases, catalytic/nicotinamidase

response to abscisic acid stimulus

 

miR1133

Hv.14592

pathogenesis related protein-1

plant defense

 

miR1133

Hv.12091

oxidoreductase, zinc-binding dehydrogenase family protein

stress response

 

miR1133

Hv.28954

HLH superfamily

transcription factor

 

miR1133

Hv.28555

serine/threonine protein kinase, PKc-like superfamily

abiotic stress resistance

 

miR1133

Hv.4244

CTP synthase

CTP synthase activity

 

miR1135

Hv.5272

Epidermal growth factor receptor-like protein

vacuolar transport

 

miR1135

Hv.223

Limit dextrinase

carbohydrate metabolic process

 

miR1135

Hv.18515

ubiquitin family protein

ubiquitination

 

miR1135

Hv.16976

HEAT repeat-containing protein

unknown function

 

miR1135

Hv.16897

ATTPS6 (A. thaliana trehalose phosphatase/synthase 6), transferase, transferring glycosyl groups, trehalose-phosphatase

development

1130

miR1130

Hv.12920

PKc-like superfamily

abiotic stress response

1134

miR1134

Hv.29810

WRKY transcription factor

transcription factor

 

miR1134

Hv.29222

ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit

carbon fixation

 

miR1134

Hv.22973

octopine synthase binding factor1, ATBZIP53 (BASIC REGION/LEUCINE ZIPPER MOTIF 53), DNA binding/protein heterodimerization/sequence-specific DNA binding/transcription factor

stress response

 

miR1134

Hv.22600

fumarylacetoacetate hydrolase family protein

tyrosine catabolism

 

miR1134

Hv.9579

L-asparaginase, putative/L-asparagine amidohydrolase, putative

nitrogen metabolism

 

miR1134

Hv.239

AWPM-19-like membrane family protein

freezing tolerance

 

miR1134

Hv.26138

AWPM-19-like membrane family protein

freezing tolerance

 

miR1134

Hv.24001

dehydrin family protein

stress response

 

miR1134

Hv.23108

B3-hordein fragment

seed storage protein

 

miR1134

Hv.23080

ATNUDT17 (A. thaliana Nudix hydrolase homolog 17)

hydrolase activity

 

miR1134

Hv.16060

Sulfotransferase domain

sulfotransferase activity

1438

miR1438

Hv.26216

RAP2.2, AP2 superfamily

transcription factor

1533

miR1533

Hv.29041

aldehyde dehydrogenase

stress response

1846

miR1846

Hv.19467

UDP-GLUCOSYL TRANSFERASE

stress response

1848

miR1848

Hv.6944

Pollen_Ole_e_I super family

unknown function

1862

miR1862

Hv.26602

protein coding

unknown function

1867

miR1867

Hv.18578

FLAVODOXIN-LIKE QUINONE REDUCTASE 1

auxin response gene

 

miR1867

Hv.1368

ATPase, coupled to transmembrane movement of substances

ATPase activity

1871

miR1871

Hv.28885

protein coding

unknown function

2091

miR2091

Hv.6058

FKBP superfamily

regulation of photosyntetic process/stress response/plant hormone pathways

2094

miR2094

Hv.699

RNA binding

RNA binding

2102

miR2102

Hv.22799

RNA binding

stress response

Although several of the candidate miRNA/target pairs here identified have the same functional annotation reported in previously studied species (table 3) and specifically in barley some putative novel microRNA/target pairs have been discovered (table 4) [2]. Actually, some of these novel targets were reported by literature as regulated by a different microRNA. Most of the novel miRNA/target pairs refer to miRNAs recently discovered and thus probably less studied (i.e. miR1120, miR1122, miR1134). The Argonaute-like protein found as a novel target for miR408 in H.vulgare by Dryanova et al. has been confirmed also in the present work.

Transcription factor families comprise most of the highly conserved miRNA targets (see table 3) such as SBP family for miRNA 156, AP2 family for miR172, GRAS family for miR171, myb family for miR159, GRF family for miR396 and ARF family for miR160. These results confirmed what previously observed in Triticeae and in other species [2]. In rice about 70% of conserved miRNA targets are transcription factors, while in wheat one-third of the predicted targets was found to encode for transcription factors [11, 12]. Conserved miRNAs also target genes involved in their own biogenesis and function: as an example miR168 targets AGO1 which is part of the RISC complex responsible for the miRNA-mediated mRNA cleavage [15]. miRNA regulate gene expression also by targeting enzymes of the ubiquitination pathway (ubiquitin conjugating enzyme E2 and TIR1/ubiquitin ligase): barley miR393, miR399, miR1128, miR1133, miR1135 can be considered putative regulators of gene expression at protein level.

The number of target genes identified as different Unigene clusters (tables 3-4) is very different among the miRNA families. In rice Zhou et al. have found a high number of targets for miR156 and miR396 and a low number for miR162, miR167, miR395, miR398 and miR399 [12]. This finding could indicate that the former miRNAs are nodes in gene regulation networks, while the latter could act on specialized pathways.

The predicted targets have been grouped into functional categories and reported in figures 1 and 2 where the target annotations based on GO terms are shown. Biological processes known to be regulated by miRNAs, such as development and response to biotic and abiotic stress, have been highlighted both in known (figure 1a) and in novel targets (figure 2a). Moreover, most of the molecular functions are related to transcriptional regulation and DNA/nucleotide binding in both groups (figures 1b-2b). These findings suggest that the predicted target genes can be considered a reliable dataset to be used in subsequent analysis.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-11-595/MediaObjects/12864_2010_Article_3292_Fig1_HTML.jpg
Figure 1

Functional enrichment for the miRNA targets identified. For each GO term it is shown the number of targets annotated with that term with respect to the total number of targets (%). Figure 1a refers to the biological process, while figure 1b refers to the molecular function.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-11-595/MediaObjects/12864_2010_Article_3292_Fig2_HTML.jpg
Figure 2

Functional enrichment for the novel miRNA targets identified. For each GO term it is shown the number of targets annotated with that term with respect to the total number of novel targets (%). Figure 2a refers to the biological process, while figure 2b refers to the molecular function.

For some Unigene clusters the annotation was related to transcribed genes rather than protein coding sequences. These Unigenes could represent miRNA-coding genes as shown by other authors [16, 17]. Table 5 reports the Unigene clusters candidate to encode miRNA coding genes on the basis of the precursor sequence secondary structure (MFEI >0.85, see Materials and Methods) and of the presence of the miRNA* (miRNA passenger sequence). It cannot be excluded that the clusters unable to fold with a miRNA-like structure (e.g. Hv.8579, Hv.11623) are false negatives for several reasons, such as truncated precursor sequences in EST database. Putative microRNA sequences have also been BLASTed against previously known precursors available from mirBASE: the analysis found similarities with 6 different miRNA families. The secondary structures of the putative microRNA precursors are reported in the additional file 4. Linking together sequences containing miRNA precursors from Dryanova et al. and from the present work, information on several microRNA putative secondary structures, belonging to 10 miRNA families are now available [2]. The mature miRNAs predicted from these data are 18 to 24 nt long, with a higher frequency for 20 and 21 nt.
Table 5

Unigene clusters candidate to encode for miRNAs

 

Features of the precursors identified

Best scoring alignment with miRBase precursors

Unigene cluster

ΔG (kcal/mol)

MFEI

NM

ML

PL

Arm

Accession number

miRNA

Score

e-value

Hv.1306

-72.8

0.89

2

21

167

3'

MI0006178

tae-MIR444

736

1 × 10-54

Hv.5064

-22.0

1.00

2

18

68

3'

MI0006199

tae-MIR1137

168

4 × 10-8

Hv.7117

-74.9

0.96

3

21

115

5'

MI0011565

bdi-MIR397

196

5 × 10-10

Hv.8158

-60.7

0.88

3

21

92

5'

MI0001763

sof-MIR168a

393

1 × 10-26

Hv.14657

-31.5

1.85

2

21

69

3'

MI0006183

tae-MIR1121

241

3 × 10-14

Hv.15131

-51.1

0.90

2

21

129

3'

MI0006976

osa-MIR444d

431

2 × 10-29

Hv.16635

-91.7

0.92

3

21

200

3'

MI0006170

tae-MIR159a

805

2 × 10-60

Hv.22601

-34.0

0.92

4

22

97

3'

MI0006192

tae-MIR1130

179

9 × 10-9

Hv.28058

-63.8

1.60

2

24

129

3'

MI0006182

tae-MIR1120

147

7 × 10-6

Hv.29065

-53.6

1.07

4

22

131

5'

MI0006199

tae-MIR1137

182

8 × 10-9

Hv.29519

-42.9

1.02

2

21

96

3'

MI0006192

tae-MIR1130

144

8 × 10-6

Hv.30469

-39.0

0.91

3

21

117

5'

MI0006199

tae-MIR1137

141

2 × 10-5

For each cluster, the table shows details about the putative precursors: the free energy ΔG, the minimal folding free energy index (MFEI), the number of mismatches in miRNA/miRNA* duplex (NM), the mature length (ML), the precursor length (PL) and the location of mature miRNA (3' or 5'). Moreover, it is also reported the more similar known precursor in miRBase, with the alignment score and p-value.

Genetic variation at miRNA target sites

A single nucleotide change in the sequence of a target site can affect miRNA regulation: as a consequence naturally occurring SNPs in target sites are candidates for relevant functional variations. Nair et al. established a perfect association between a SNP at the miR172 targeting site and cleistogamy in barley [18]. Overall few papers have been published to date describing variations among plant genotypes at miRNAs and their target sites, while plenty of information is available for humans [1923]. Genome-wide studies in humans have shown that the levels of polymorphism at miRNA and miRNA target sites are lower than at coding or neutral regions, however beneficial miRNA-target site polymorphisms also exist [19].

In this study, publicly available SNP data have been analyzed in context with miRNAs and their target sites. EST-derived SNPs can provide a rich source of biologically useful genetic variation due to the redundancy of gene sequence, the diversity of genotypes present in the databases and the fact that each putative polymorphism is associated with an expressed gene. Variations both in functional regions of putative miRNAs (mature sequence) and at miRNA target sites have been found. Previous works in human have highlighted a relatively low level of variation in functional microRNA regions and an appreciable level of variation at target sites [21].

Hv.5064, the candidate for miR1137 coding sequence, has been tested for modifications of pre-miRNA structure due to a base substitution in position 13 (C/G, table 6, figure 3). To evaluate the possible impact of this SNP on pre-miRNA secondary structure, Gibbs free energy (ΔG) and MFEI from each version of pre-miRNA were calculated using mfold program. Data in figure 3 show the structural variation obtained when moving from "C variant" to "G variant" with a higher MFEI for the second one and thus a greater stability of the molecule (miRNA-miRNA* pairing enhanced in the G variant). Difference in ΔG moving from C to G and vice versa were calculated according to Ehrenreich and Purugganan [19]. ΔΔG was +1.3 for the former change and -1.3 for the latter suggesting that some SNPs can stabilize/destabilize pre-miRNA structure. No target gene has been reported in literature for miR1137.
Table 6

Putative polymorphisms identified at miRNA target sites and inside miRNA mature sequences

miRNA family

miRNA name

Unigene

Unigene cluster annotation

Putative Polymorphisms at miRNA target site (5'-3')

Barley miRNA mature sequence (5'-3')

156

miR156

Hv.5875

protein coding (SBP domain)

#GTGCTCTCT(C)CTCTTCTGTCA

UGACAGAAGAGA GAGAGCAC (12)

 

miR156

Hv.21387

SPL2 (SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 2)

#ATGCTCTC(G)TC(T)TC(G)TTCTGTCA

UGACAGAAG AG AG AGAGCAU (9-11-13)

164

miR164

Hv.28795

NAC domain containing protein

#AGCAAGTGCCC(A)TGCTTCTCCA

UGGAGAAGCAG GGCACUUGCU (11)

169

miR169

Hv.13681

CCAAT-binding transcription factor (CBF-B/NF-YA) family protein

#CAGGCAACTCATCCTTGGCT(C)T

AA GCCAAGGAUGAGUUGCCUG (2)

 

miR169

Hv.9532

CCAAT-binding transcription factor (CBF-B/NF-YA) family protein

#GGCAATTCATCCTTGGC(T)TT

AAG CCAAGGAUGAAUUGCC (3)

393

miR393

Hv.2498

TIR1 (TRANSPORT INHIBITOR RESPONSE 1), ubiquitin-protein ligase

#G(C)ACAATGCG(T)ATCCC(+CT)TTTGGA

UCCAAA() GGGAUC GCAUUGUC (6-12-20)

396

miR396

Hv.9742

WRC, QLQ

#GTTCAAG(A)AAAGCCTGTGGA

UCCACAGGCUUUC UUGAAC(13)

408

miR408

Hv.20945

ARGONAUTE like superfamily

#CAGGGCAG(T)AGGCAGTGCAG

CUGCACUGCCUC UGCCCUG (12)

 

miR408

AutoSNP contig 2094

Plastocyanin

#CAGGGAAGAGGCA(C) GTGCGG

CCGCACU(G) GCCUCUUCCCUG (7)

444

miR444

Hv.16297

/

 

*GCAGUUGCU(C) GCCUCAAGCUU (9)

818

miR818

Hv.11323

protein coding

#CCGTCCCATAA(CC)TATAAGGG

CCCUUAUAU UAUGGGACGG (9)

 

miR1436

Hv.8351

protein coding

#ACTCCCTCC(T) GTCCCATAAT

AUUAUGGGACG GAGGGAGU (11)

 

miR1436

Hv.11323

protein coding

#ACTCCCTCCGTCCCATAA--(CC)T

A-- UUAUGGGACGGAGGGAGU (2)

 

miR1439

Hv.23816

exo-endo-phos superfamily

# AATACTCACTCCGTCCCAAAA(G)

U UUUGGGACGGAGUGAGUAUU (1)

 

miR1439

Hv.11224

tatD-related deoxyribonuclease family protein

#TACTCACTCCGTTCCA(T) AAA

UUUU GGAACGGAGUGAGUA (4)

821

miR821

Hv.3660

GDH1 (Glutamate dehydrogenase)

#TCAA(C)CAAAAAAGTTGAAT

AUUCAACUUUUUUGU UGA (15)

1030

miR1030

Hv.14867

RNA recognition motif (RRM)-containing protein

#TGGT(G)GCAGGTGCAGGTGCAGG

CCUGCACCUGCACCUGCA CCA (18)

1119

miR1119

Hv.29226

/

 

*UGGC(-)A(C)CGGCGCGAUGCUCAGUCA(-)G(C) (4-5-23-24)

 

miR1119

Hv.29225

protein coding

#CTGAC(A)TGAGCATCGCGCCGTGCCA

UGGCACGGCGCGAUGCUCAG UCAG (20)

 

miR1119

Hv.27666

protein coding

#C(T)TGAC(T/A)TG(A)A(G)GCA(T)TCGCGCCGTGCC

GGCACGGCGCGAU GCUC AG UCAG (13-16-17-19-23)

 

miR1119

Hv.23343

molybdenum cofactor sulfurase family protein, superfamily

#CT(G)G(T)A(G) CT(C) GAGCATCGCGCCGTGCC

GGCACGGCGCGAUGCUCA GUCA G (18-20-21-22)

 

miR1119

Hv.29210

protein coding

#T(G)GGCACG(A)GC(T)GCGAT(A)GCTCAG(A)TCAG(A)

C UGAC UGAGCA UCGCG CC GUGCCA (1-5-11-16-18-24)

1120

miR1121

Hv.464

serine/threonine kinase

#A(G)A(G)GAGCGTTTAGATCACTA

UAGUGAUCUAAACGCUCUU (18-19)

 

miR1121

Hv.6581

protein coding

#TAAGAGCGTTTAGATCACT(C) A

UA GUGAUCUAAACGCUCUUA (2)

 

miR1121

Hv.6532

ATPase-Plipid, haloacid dehalogenase-like hydrolase family protein

#TAAG(A)AGTGTTTAGATCACTACT

AGUAGUGAUCUAAACACUC UUA (19)

 

miR1121

Hv.5064

/

 

*UAGUACAAAGUUG(C)AGUCA (13)

1122

miR1128

Hv.14876

ARF-GAP DOMAIN, C2 superfamily

#TTTG(T)GG(A)ACGGA(G)GGGAGTAGTA

UACUACUCCCU CCGUC CC AAA (11-16-18)

 

miR1133

Hv.12091

oxidoreductase, zinc-binding dehydrogenase family protein

#TTTGGG(A)ACGGAGGGAGTAC(-)TAT

AUAG UACUCCCUCCGUC CCAAA (3-16)

 

miR1133

Hv.28555

serine/threonine protein kinase, PKc-like superfamily

#TTTCGGACAGAGGG(T)AGTATAT

AUAUACUC CCUCUGUCCGAAA (8)

 

miR1135

Hv.18515

ubiquitin family protein

#TTC(G)GGAATTACTTGTCGCA

UGCGACAAGUAAUUCCG AA (17)

1134

miR1134

Hv.22973

octopine synthase binding factor1, ATBZIP53 (BASIC REGION/LEUCINE ZIPPER MOTIF 53), DNA binding/protein heterodimerization/sequence-specific DNA binding/transcription factor

#TCTTCTTCTTCTTCTTG(C)TTC(---) TTG

CAAGAAC AAGAAGAAGAAGAAGA (4-5-6-7)

 

miR1134

Hv.9579

L-asparaginase, putative/L-asparagine amidohydrolase, putative

#TC(G)T(C) TCTTCTTCTTGGTGTTGGTG

CACCAACACCAAGAAGAAGAAG A (21-22)

 

miR1134

Hv.26138

AWPM-19-like membrane family protein

#TCTTCTTC(G)TTCTT(A)GTCGTTGTTG

CAACAACGACA AGAAG AAGAAGA (11-16)

 

miR1134

Hv.24001

dehydrin family protein

#TTCTTCTTCTTGTTGTTTTT(-) G

CA AAAACAACAAGAAGAAGAA (2)

 

miR1134

Hv.8025

/

 

*UCUUCUUCUUUUGUUGUUGU(C)UG (20)

 

miR1134

Hv.5763

/

 

*CUUC(G) UUCCUCUUGUUGUUGUUG (4)

1871

miR1871

Hv.28885

protein coding

# C(T) AACATGATATCAGAGCCA

UGGCUCUGAUAUCAUGUUG (19)

Letters in bold refer to SNPs represented by at least two independent sequences, while the numbers in brackets refer to the position of the SNP in the sequence. Plus means insertion, minus means deletion.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-11-595/MediaObjects/12864_2010_Article_3292_Fig3_HTML.jpg
Figure 3

Predicted secondary structures of the two variants of the miR1137 precursor identified in the Unigene cluster Hv.5064. The variants with a C and a G in the 13th position are respectively reported in the left and in the right side of the figure. The table shows for each variant: the free energy ΔG, the length of the precursor, the GC content, the MFEI (Minimal Folding Energy Index), the number of mismatches between the mature sequence and the paired miRNA* passenger and the arm of the hairpin where the mature sequence is located.

In plants most of the miRNA-based regulation relies on the cleavage of target mRNAs that normally occurs at the tenth nucleotide of the complementary region and numerous studies on miRNA-target interaction have highlighted the importance of positions 2 to 12, more frequently 10 and 11 [24]. Although most of the putative polymorphisms highlighted in this work are outside those critical positions, several examples of putative functionally relevant polymorphisms have been detected. Table 6 reports the putative polymorphisms detected after comparison among EST sequences inside Unigene clusters, without any selection against false positives. Some of these nucleotide variation could be due to sequencing errors or related to very similar genes belonging to a specific family, nevertheless when the SNPs/indels rely on two or more copies of independent sequences it can be considered a good candidate for a true positive polymorphic target site [25]. For example, a polymorphism in miRNA 408 target site detected by AutoSNP in contig 2094 (coding for a plastocyanin) is based on sequences from two different cultivars reporting the same allelic variant as part of a haplotype where a SSR (Simple Sequence Repeat) polymorphism is located upstream the target sequence (figure 4). Some polymorphisms also showed an evolutionary conserved position, the nucleotide variation identified in Hv.2498 (targeted by miR393) has also been found in the orthologous gene of Arabidopsis in the same position by Ehrenreich and Purugganan [19].
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-11-595/MediaObjects/12864_2010_Article_3292_Fig4_HTML.jpg
Figure 4

SNP identified in contig2094 within the target site for miRNA 408. In this multiple alignment performed with AutoSNP, two cultivars (Optic and Morex) report the same allelic variant as part of a haplotype including a SSR polymorphism located upstream the target sequence.

The Squamosa-promoter Binding Protein (SBP) is a known target family for miR156. Many plant transcription factors involved in the regulation of the transition from the vegetative to the reproductive phase belong to this family and it has been shown that overexpressing SBP genes can lead to increased leaf initiation, decreased apical dominance and delayed flowering time [15]. The increase of the activity of some miRNAs (among which miR156) is part of the infection strategy performed by the Turnip mosaic virus in Arabidopsis [26, 27]. miR156 performs a critical function in mediating developmental processes and it is also related to the response to biotic stress. The screening of barley databases has identified two SBP genes targeted by miR156 for which two nucleotide variations occur in critical positions (11-12). If these SNPs will be experimentally confirmed, they could have the effect of destabilizing the interaction between the miRNA and the mRNA, which could consequently avoids cleavage and lead to phenotypical variations in developmental features or in the resistance to viral infection.

A SNP also occurs in a crucial point of the experimentally confirmed NAC1 target for miR164. NAC1 is a transcription factor involved in shoot apical meristem formation and auxin-mediated lateral root formation. Guo et al. showed that the overexpression of miR164 leads to reduced lateral rooting; conversely the disruption of the regulation mediated by this miRNA increases the number of lateral roots [28]. The authors have reported that miR164 directs cleavage in vivo at a position complementary to the 10th nucleotide from the 5' end of the mature sequence [28]. The SNP found in barley is in the 11th position, therefore it is likely to prevent the cleavage and produce phenotypic effects on root development.

SNPs have been identified also in other two conserved miRNA targets, TIR1 and AGO4, targeted respectively by miR393 and miR408. TIR1 is an auxin-receptor negatively regulated by miRNAs in response to bacterial flagellin, as a defence mechanism against Pseudomonas syringae[29]. AGO4 is a protein involved in the siRNA mediated gene silencing, and it is required for the resistance to the same pathogen [30]. Therefore, miR393 and miR408 are likely to work in a coupled manner during P. syringae infection. The two SNPs identified are in the 12th position and could potentially alter the levels of pathogens resistance.

SNPs were also found in previously not reported miRNA targets, such as the AWPM-19-like protein matching to the miRNA 1134. AWPM-19 accumulates in wheat plasma membrane during cold acclimation in response to abscisic acid [31]. If this miRNA really controls the synthesis of this protein, a deleterious SNP in the 11th position could then change resistance to cold stress.

Conclusions

This study has thus provided an update of the information on barley miRNAs and their targets representing a foundation for future studies.

Novel putative target genes have been identified and most of them are involved in stress and hormone response. Indeed, the role of plant miRNAs in abiotic and biotic stress response as well as in auxin signalling is well known [32, 33]. In particular, protein kinases such as protein kinase C and serine/threonine kinase, known to be important regulator on abiotic stress resistance, are largely present in novel microRNA/target pairs identified.

The results have also shown that microRNA target sites can be an interesting source for the identification of functional genetic variability, representing an interesting source of candidate molecular markers for application in barley breeding. Putative polymorphisms have now to be verified by amplification and sequencing of the target sequences on a larger set of genotypes.

Sequence analysis based on known miRNAs can obviously give insights only on conserved mRNAs and related targets. Future work will thus be based on the construction of a degradome library for parallel analysis of RNA end (PARE), a powerful approach for high-throughput identification/validation of conserved and non conserved targets.

Methods

miRNA reference dataset

The initial miRNA dataset has been obtained by extracting the mature sequences (1929 entries) of the Viridiplantae group from the miRBase release 13 http://www.mirbase.org[34]. By removing identical mature sequences, the size of this dataset has been subsequently reduced to 1014 non-redundant sequences related to 468 miRNA families.

Searching for mature miRNAs matching sequences in barley

The full collection of non-redundant mature miRNA sequences was used in a BLASTn search against dbEST http://www.ncbi.nlm.nih.gov, accepting a number of mismatch lower than 4.

The set of miRNA mature sequences (including the identical sequences removed at the first step of the work) with at least one matching EST have been classified on the basis of the species of origin. The binomial distribution was used to assess the statistical significance for the represented plant species; this allowed identifying those species chosen from the initial dataset more or less frequently than random. Four different thresholds for the p-values were applied (0.05, 0.01, 0.005, 0.001).

Matching ESTs have then been related to Unigene clusters and the corresponding annotations were recorded (if available). The GO slimmer tool available on the Gene Ontology website http://www.geneontology.org has been used to identify the GO slim terms more represented in the set of potential targets on the basis of the Unigene cluster annotations. For this analysis the Plant GO Slim subset has been used.

Identification of putative miRNA precursors

True miRNA precursors should have both a mature sequence on one arm of the hairpin and a paired passenger sequence (called miRNA*) on the opposite arm. To assess these features the precursor sequences were extracted from the consensus sequences, obtained by the Sequencer Software (Gene Codes) on Unigene cluster assemblies, by cutting 13 nt before the 5' hit and 13 nt after the 3' hit, since this region (called the pri-extension region of the hairpin) was recently shown to have this average length in plants [35]. In order to predict the secondary structure of the precursors, the software mfold 3.2, free available at http://mfold.bioinfo.rpi.edu/cgi-bin/rna-form1.cgi, was used [36]. The minimal folding free energy index (MFEI) and the GC content were calculated for each sequence.

All the sequences with a MFEI greater than 0.85 were considered potential miRNA precursors [37]; besides, only 4 mismatches were allowed between the mature sequence and the passenger sequence, and only few and small asymmetric bulges were accepted [38].

Identification of SNPs/indels at miRNA target sites

Polymorphisms in target genes have been searched through a comparison of the ESTs belonging to the same Unigene cluster. Each cluster has been assembled by Sequencer Software (Gene Codes) and polymorphisms have been searched on miRNA complementary sequence sites.

AutoSNP database http://autosnpdb.qfab.org.au was also screened using target gene annotations as contig-searching keywords.

Declarations

Acknowledgements

This work has been supported by "MIRNA" project and "Mappa 5A" project (Italian Ministry for Agriculture). The authors thank Mr. Renzo Alberici and Miss Alice Martini for the technical assistance provided.

Authors’ Affiliations

(1)
CRA-Genomics Research Centre

References

  1. Bartel DP: MicroRNAs: genomics, biogenesis, mechanism and function. Cell. 2004, 116: 281-297. 10.1016/S0092-8674(04)00045-5.PubMedView ArticleGoogle Scholar
  2. Dryanova A, Zakharov A, Gulick PJ: Data mining for miRNAs and their targets in the Triticeae. Genome. 2008, 51: 433-443. 10.1139/G08-025.PubMedView ArticleGoogle Scholar
  3. 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.PubMed CentralPubMedView ArticleGoogle Scholar
  4. Schreiber AW, Sutton T, Caldo RA, Kalashyan E, Lovell B, Mayo G, Muehlbauer GJ, Druka A, Waugh R, Wise RP, Langridge P, Baumann U: Comparative transcriptomics in the Triticeae. BMC Genomics. 2009, 10: 285-10.1186/1471-2164-10-285.PubMed CentralPubMedView ArticleGoogle Scholar
  5. Zhang BH, Pan XP, Wang QL, Cobb GP, Anderson TA: Identification and characterization of new plant microRNAs using EST analysis. Cell Res. 2005, 15 (5): 336-360. 10.1038/sj.cr.7290302.PubMedView ArticleGoogle Scholar
  6. German MA, Pillay M, Jeong D, Hetawal A, Luo S, Janardhanan P, Kannan V, Rymarquis LA, Nobuta K, German R, De Paoli E, Lu C, Schroth G, Meyers BC, Green PJ: Global identification of microRNA-target RNA pairs by parallel analysis of RNA ends. Nat Biotechnol. 2008, 26 (8): 941-946. 10.1038/nbt1417.PubMedView ArticleGoogle Scholar
  7. 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. 2009, 38 (3): 981-995. 10.1093/nar/gkp1035.PubMed CentralPubMedView ArticleGoogle Scholar
  8. Xie K, Wu C, Xiong L: Genomic organization, differential expression and interaction of SQUAMOSA promoter-binding-like transcription factors and microRNA156 in rice. Plant Physiol. 2006, 142: 280-293. 10.1104/pp.106.084475.PubMed CentralPubMedView ArticleGoogle Scholar
  9. Picoult-Newberg L, Ideker TE, Pohl MG, Taylor SL, Donaldson MA, Nickerson DA, Boyce-Jacino M: Mining SNPs from EST databases. Genome Res. 1999, 9: 167-174.PubMed CentralPubMedGoogle Scholar
  10. Zhang B, Pan X, Cannon CH, Cobb GP, Anderson TA: Conservation and divergence of plant microRNA genes. Plant J. 2006, 46: 243-259. 10.1111/j.1365-313X.2006.02697.x.PubMedView ArticleGoogle Scholar
  11. Yao Y, Guo G, Ni Z, Sunkar R, Du J, Zhu JK, Sun Q: Cloning and characterization of microRNAs from wheat (Triticum aestivum L.). Genome Biol. 2007, 8: R96-10.1186/gb-2007-8-6-r96.PubMed CentralPubMedView ArticleGoogle Scholar
  12. Zhou M, Gu L, Li P, Song X, Wei L, Chen Z, Cao X: Degradome sequencing reveals endogenous small RNA targets in rice (Oryza sativa L., ssp. Indica). Front Biol. 2010, 5 (1): 67-90.View ArticleGoogle Scholar
  13. Grun D, Wang YL, Langenberger D, Gunsalus KC, Rajewsky N: microRNA target predictions across seven Drosophila species and comparison to mammalian targets. PLoS Comput Biol. 2005, 1: e13-10.1371/journal.pcbi.0010013.PubMed CentralPubMedView ArticleGoogle Scholar
  14. Zhang B, Pan X, Anderson TA: Identification of 188 conserved maize microRNAs and their targets. FEBS Lett. 2006, 580: 3753-3762. 10.1016/j.febslet.2006.05.063.PubMedView ArticleGoogle Scholar
  15. Jones-Rhoades MW, Bartel DP, Bartel B: MicroRNAs and their regulatory roles in plants. Annu Rev Plant Biol. 2006, 57: 19-53. 10.1146/annurev.arplant.57.032905.105218.PubMedView ArticleGoogle Scholar
  16. Guo Q, Xiang A, Yang Q, Yang Z: Bioinformatic identification of microRNAs and their target genes from Solanum tuberosum expressed sequence tags. Chinese Sci Bull. 2007, 52 (17): 2380-2389. 10.1007/s11434-007-0359-x.View ArticleGoogle Scholar
  17. 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: 1464-1474. 10.1016/j.febslet.2007.02.074.PubMedView ArticleGoogle Scholar
  18. Nair SK, Wang N, Turuspekov Y, Pourkheirandish M, Sinsuwongwat S, Chen G, Sameri M, Tagiri A, Honda I, Watanabe Y, Kanamori H, Wicker T, Stein N, Nagamura Y, Matsumoto T, Komatsuda T: Cleistogamous flowering in barley arises from the suppression of microRNA-guided HvAP2 mRNA cleavage. PNAS. 2010, 107: 480-496.Google Scholar
  19. Ehrenreich IM, Purugganan MD: Sequence variation of microRNAs and their binding sites in Arabidopsis. Plant Physiol. 2008, 146: 1974-1982. 10.1104/pp.108.116582.PubMed CentralPubMedView ArticleGoogle Scholar
  20. Guo X, Gui Y, Wang Y, Zhu Q, Helliwell C, Fan L: Selection and mutation on microRNA target sequences during rice evolution. BMC Genomics. 2008, 9: 454-10.1186/1471-2164-9-454.PubMed CentralPubMedView ArticleGoogle Scholar
  21. Saunders MA, Liang H, Li WH: Human polymorphisms at microRNAs and microRNA target sites. PNAS. 2007, 104: 3300-3305. 10.1073/pnas.0611347104.PubMed CentralPubMedView ArticleGoogle Scholar
  22. Sethupathy P, Collins FS: MicroRNA target site polymorphisms and human disease. Trends Genet. 2008, 24: 489-497. 10.1016/j.tig.2008.07.004.PubMedView ArticleGoogle Scholar
  23. Gardner PP, Vinther J: Mutation of miRNA target sequences during human evolution. Trends Genet. 2008, 24: 262-265. 10.1016/j.tig.2008.03.009.PubMedView ArticleGoogle Scholar
  24. Rhoades MW, Reinhart BJ, Lim LP, Burge CB, Bartel B, Bartel DP: Prediction of plant microRNA targets. Cell. 2002, 110: 513-520. 10.1016/S0092-8674(02)00863-2.PubMedView ArticleGoogle Scholar
  25. Batley J, Barker G, O'Sullivan H, Edwards KJ, Edwards D: Mining for single nucleotide polymorphisms and insertions/deletions in maize expressed sequence tag data. Plant Physiol. 2003, 132: 84-91. 10.1104/pp.102.019422.PubMed CentralPubMedView ArticleGoogle Scholar
  26. Kasschau KD, Xie Z, Allen E, Llave C, Chapman EJ, Krizan KA, Carrington JC: P1/HC-Pro, a viral suppressor of RNA silencing, interferes with Arabidposis development and miRNA function. Dev Cell. 2003, 4: 205-217. 10.1016/S1534-5807(03)00025-X.PubMedView ArticleGoogle Scholar
  27. Chen J, Li WX, Xie D, Peng JR, Ding SW: Viral virulence protein suppresses RNA silencing-mediated defense but upregulates the role of microRNA in host gene expression. Plant Cell. 2004, 16: 1302-1313. 10.1105/tpc.018986.PubMed CentralPubMedView ArticleGoogle Scholar
  28. 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: 1376-1386. 10.1105/tpc.105.030841.PubMed CentralPubMedView ArticleGoogle Scholar
  29. Navarro L, Dunoyer P, Jay F, Arnold B, Dharmasiri N, Estelle M, Voinnet O, Jones JDG: A plant miRNA contributes to antibacterial resistance by repressing auxin signalling. Science. 2006, 312: 436-439. 10.1126/science.1126088.PubMedView ArticleGoogle Scholar
  30. Agorio A, Vera P: ARGONAUTE4 is required for resistance to Pseudomonas syringae in Arabidopsis. Plant Cell. 2007, 19: 3778-3790. 10.1105/tpc.107.054494.PubMed CentralPubMedView ArticleGoogle Scholar
  31. Koike M, Takezawa D, Arakawa K, Yoshida S: Accumulation of 19-kDa plasma membrane polypeptide during induction of freezing tolerance in wheat suspension-cultured cells by abscisic acid. Plant Cell Physiol. 1997, 38: 707-716.PubMedView ArticleGoogle Scholar
  32. Martin RC, Liu PP, Goloviznina NA, Nonogaki H: microRNA, seeds, and Darwin?: diverse function of microRNA in seed biology and plant response to stress. J Exp Bot. 2010, 69: 2229-2234. 10.1093/jxb/erq063.View ArticleGoogle Scholar
  33. Teotia PS, Mukherjee SK, Mishra NS: Fine tuning of auxin signalling by miRNAs. Physiol Mol Biol Plants. 2008, 14: 81-90. 10.1007/s12298-008-0007-1.PubMed CentralPubMedView ArticleGoogle Scholar
  34. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ: miRBase: tools for microRNA genomics. NAR. 2008, D154-D158. 36 Database
  35. Kadri S, Hinman V, Benos PV: HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models. Bioinformatics. 2009, 10 (Suppl I): S35-PubMed CentralPubMedGoogle Scholar
  36. Zuker M: mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 2003, 31 (13): 3406-15. 10.1093/nar/gkg595.PubMed CentralPubMedView ArticleGoogle Scholar
  37. Zhang BH, Pan XP, Cox SB, Cobb GP, Anderson TA: Evidence that miRNAs are different from other RNAs. Cell Mol Life Sci. 2006, 63: 246-254. 10.1007/s00018-005-5467-7.PubMedView ArticleGoogle Scholar
  38. Meyers BC, Axtell MJ, Bartel B, Bartel DP, Baulcombe D, Bowman JL, Cao X, Carrington JC, Chen X, Green PJ, Griffiths-Jones S, Jacobsen SE, Mallory AC, Martienssen RA, Poethig RS, Qi Y, Vaucheret H, Vonnet O, Watanabe Y, Weigel D, Zhu JK: Criteria for annotation of plant microRNAs. Plant Cell. 2008, 20: 3186-3190. 10.1105/tpc.108.064311.PubMed CentralPubMedView ArticleGoogle Scholar
  39. Jones-Rhoades MW, Bartel DP: Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Molecular Cell. 2004, 14: 787-799. 10.1016/j.molcel.2004.05.027.PubMedView ArticleGoogle Scholar
  40. Puzey JR, Kramer EM: Identification of conserved Aquilegia coerulea microRNAs and their targets. Gene. 2009, 448 (1): 46-56. 10.1016/j.gene.2009.08.005.PubMedView ArticleGoogle Scholar
  41. Unver T, Budak H: Conserved microRNAs and their targets in model grass species Brachypodium distachyon. Planta. 2009, 230 (4): 659-69. 10.1007/s00425-009-0974-7.PubMedView ArticleGoogle Scholar
  42. Park W, Li J, Song R, Messing J, Chen X: CARPEL FACTORY, a Dicer homolog, and HEN1, a novel protein, act in microRNA metabolism in Arabidopsis thaliana. Current Biology. 2002, 12 (17): 1484-1495. 10.1016/S0960-9822(02)01017-5.PubMedView ArticleGoogle Scholar
  43. Trindade I, Capitão 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.PubMedView ArticleGoogle Scholar
  44. Lu S, Sun YH, Shi R, Clark C, Li LG, Chiang VL: Novel and mechanical stress-responsive microRNAs in Populus trichocarpa that are absent from Arabidopsis. Plant Cell. 2005, 17: 2186-2203. 10.1105/tpc.105.033456.PubMed CentralPubMedView ArticleGoogle Scholar
  45. Li YF, Zheng Y, Addo-Quaye C, Zhang L, Saini A, Jagadeeswaran G, Axtell MJ, Zhang WX, Sunkar R: Transcriptome-wide identification of microRNA targets in rice. Plant J. 2010, 62 (5): 742-759. 10.1111/j.1365-313X.2010.04187.x.PubMedView ArticleGoogle Scholar
  46. Zhang LF, Chia JM, Kumari S, Stein JC, Liu ZJ, Narechania A, Maher CA, Guill K, McMullen MD, Ware D: A genome-wide characterization of microRNA genes in maize. PLoS Genet. 2009, 5 (11): e1000716-10.1371/journal.pgen.1000716.PubMed CentralPubMedView ArticleGoogle Scholar
  47. Rajagopalan R, Vaucheret H, Trejo J, Bartel DP: A diverse and evolutionarily fluid set of microRNAs in Arabidopsis thaliana. Genes and Development. 2006, 20 (24): 3407-3425. 10.1101/gad.1476406.PubMed CentralPubMedView ArticleGoogle Scholar
  48. Lacombe S, Nagasaki H, Santi C, Duval D, Piégu B, Bangratz M, Breitler JC, Guiderdoni E, Brugidou C, Hirsch J, Cao XF, Brice C, Panaud O, Karlowski WM, Sato Y, Echeverria M: Identification of precursor transcripts for 6 novel miRNAs expands the diversity on the genomic organisation and expression of miRNA genes in rice. BMC Plant Biol. 2008, 8: 123-10.1186/1471-2229-8-123.PubMed CentralPubMedView ArticleGoogle Scholar

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