Computational prediction, identification and expression profiling of microRNA against

drought stress in banana.

M.Muthusamy, S.Backiyarani, M.S. Saraswathi and S.Uma*

National Research Centre for Banana (ICAR),India

miRNAs - small endogenous, non-coding RNAs, usually consisting of ∼20–24 nucleotides.

Plays a critical role in a variety of cellular processes such as cell development and proliferation, apoptosis, and stress response etc.

miRNAs - multi-gene families - target Transcription Factors and

miRNAs - single gene - target non-TFs (dianeguo, CUHK,2007)

Banana genome coding for more number of Transcription factors

MiRNA Database- 25141 -mature miRNAs from 193 species

identified and deposited (miRNA blog)

RISC = RNA-induced Silencing Complex

Zhang et al.,2005

miRNAs induced and regulated by environmental biotic and abiotic stresses

b) Details of stress responsive miRNAs

a) miRNAs obtained from ESTs of various stress induced tissues

Expression of miRNA in various plant tissues

Cell Research 15(5):336-330

Zhang et al., 2005

miRNA biogenesis and regulation of miRNA gene regulation

microRNA Stress Crop Reference44 micro RNA includes 156,169,166,167,159 395,396 and 482

Drought Cow pea Figueroa et al.,2011

30 microRNA 16-downregulated (miR170, miR172, miR397, miR408, miR529, miR896, miR1030, miR1035, miR1050, miR1088, and miR1126) 14upegulatedmiR395, miR474, miR845, miR851, miR854, miR901, miR903, and miR1125)

Drought Rice Zhou et al.,2010

zma-miR156a/b/c/d/e/f/g/h/I, gma-miR319a/c,osa-miR319a,ppt-miR319a ,pta-miR319,ptc-miR319e,vvi-miR319b

Salt stress Maize roots

Ding et al.,2009

Up-regulated (miR162, miR168, miR172, miR319,miR396, miR397, miR398, miR408 and miR447)Downregulated (miR160, miR169, miR170 and miR391)

tomato leaf curlvirus

Tomato Naqvi et al.,2010

65 miRNA in pollen -----

Arabidopsis

Chambers et al.,2009

miR159,160,417 ABA mediated Reyes , and Chua.2007

miR398 Oxidative stress Lu et al.,2005

miR399 Phosphate deficeincy Pant et al.,2008

miR395 Sulphur deificency Takashashi et al.,1997

miR398 Copper deficiency Sunkar et al.,2005

miR156,162,164,408,475,480,481 Mechanical stress Lu et al.,2005

miRNA families regulated under various stresses

S.No miRNA family

miRNA seq Source Number of mismatches

Length of precursor

GC %

1 156c tgacagaagagagtgagcaca EST 0 98 56.12 156h tgacagaagagagagagcat EST 0 125 54.43 159 tttggattgaagggagctcta EST 0 218 48.24 160 tgcctggctccctgcatgcca EST 0 125 565 164 tggagaagcaggtcacgtgca Nt 1 91 58.26 166 tcggaccaggcttcattcccc EST 0 88 56.87 169 tagccaaggatgacttgcctg EST 0 129 54.38 396c ttccacagctttcttgaactt Nt 0 101 42.69 396f ctccacaggctttcttgaactg Nt 0 111 43.210 397 tcattgagtgcagcgttgatg EST 0 131 48.911 399 tgccaaaggagaattgccctg EST 0 92 54.312 444 ttgctgcctcaagcttgctgc EST 0 91 47.313 845 cggctctgataccaattgatg Nt 0 151 28.514 1310 ggcatcgggggcgcaacgccc EST 3 79 59.515 2083 ttcttgcactcctccatctct EST 1 106 59.416 2118 ttgccgattcctcccatcccta EST 2 137 59.917. 2914 catggtggtgacgggtgacggag EST 0 63 55.618 2916 tggggactcgaagacgatcatat EST 4 91 57.1

List of miRNAs identified from banana ESTs and Nt (before release of WGS)

(Selvarajan et al., unpublished )

Objectives

Computation based prediction, validation of miRNAs from drought stressed banana ESTs

Identification of suitable reference miRNA candidate/s for drought studies

Expression profiling of miRNA under drought stress in drought tolerant cv. Saba.

miRNA mining from drought stressed Musa ESTs

Sequence retrieval from

NCBI

Processing of ESTs

Vector clipping

PolyA removal

Repeat masking

Clustering and

assembling

Searching of homologue miRNA at

PMRD

Confirming the hair pin loop structure at

mfold

http://mobyle.pasteur.fr/cgi-bin/portal.py#forms::cap3

http://mfold.rna.albany.edu/?q=mfold/RNA-Folding-Form2.3

http://bioinformatics.cau.edu.cn/PMRD

miRNA family

miRNA seq Target gene

156c tgacagaagagagtgagcaca Squamosa promoter-binding-like protein

169 tagccaaggatgacttgcctg CCAAT-binding transcription factor

399 tgccaaaggagaattgccctg Electron transporter/ heat shock protein binding protein

2118 ttgccgattcctcccatcccta Disease resistance protein RPM1, NBS-LRR disease resistance protein

396c ttccacagctttcttgaactt Jasmonate O-methyltransferase

Details of the miRNAs used in this study and their target genes

Stem loop RT Primer and miRNA specific amplification

Identification of reference miRNAs

RNA isolation from different tissues/cultivars/drought exposed samples

cDNA synthesis

miRNA amplification

Mixture of miRNA (multiplexing) stem loop RT primer

miRNA specific forward and Universal Reverse

Analysis of mean cP values and analysis with GeNorm

Real time PCR

Reference gene selection (least variation)

Designing of Stem loop RT primer for miRNA real time PCR assay

Universal Reverse : GTGCAGGGTCCGAGGT miR156cRT Primer:TCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACGTGCTCForward : GCGGCGGTGACAGAAGAGAGT

miR169RT Primer:TCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACCAGGCAForward : GCGGCGGTAGCCAAGGATGA

miR396cRT Primer: TCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACAAGTTCForward : GCGGCGGTTCCACAAGCTTTC

miR399RT Primer: CGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACCAGGGCForward : TGCCAAAGGAGAATTGCCCTG

miR2118RT Primer: CGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTAGGGAForward : GCGGCTTGCCGATTCCTCC

M 1 2 3 4 5 6

40bp

60bp

100bp42bp

Amplification of miRNA

40bp

60bp

Lane M-10bp DNA RulerLane1 miR156Lane2 miR169Lane3 miR2118Lane4 miR 396cLane5 miR399Lane 6 Negative control

miRNA detection and profiling for selection of reference gene

Real time PCR assay

Identification of suitable reference miRNA candidates with traditional control

Tool used : GenEX to find out gene useful for normalizationTotal number of genes : Five miRNAs with standard Musa reference gene for biotic and abiotic stresses -25srRNA

Result : Best gene is 25srRNA with least variability (M-Value) of 1.034 and the next best is miR399,which almost constant throughout the different tissues and even under drought stressed conditions, next only to 25s rRNA

RNA isolation from drought imposed samples

cDNA synthesis

miRNA amplification

Mixture of miRNA stem loop RT primer

Universal reverse and miRNA specific forward

Expression profiling studies (miR169, 156&2118)

Real time PCR

Comparison with drought responsive genes

Dehydrin and aquaporin gene

Expression profiling of miRNAs in drought imposed condition in tolerant cultivar

Amplification curves of reference miRNA-399 and target miRNA-169 in Real time PCR assay (Roche LC 480)

miR169 expression found significantly high, when the drought condition is progressedOther up-regulated miRNAs are miR156 and miR2118

Expression trend : miR169 α 1/Aquaporin

Expression trend : miR169 α 1/dehydrin

ConclusionFive miRNAs namely 156c, 169, 399, 2118 and 396c were

identified in banana.

miR399 is the best candidate for reference gene selection among the tested miRNAs. Their expression under drought stress is not much influenced .Hence it can used for normalization (internal control) in drought associated miRNA expression studies.

miR169,156 and 2118 were up-regulated during drought stress in tolerant cultivar Saba. Hence their up regulation may be responsible for drought tolerance.

Comparison of miRNA expression with expression of Dehydrin and Aquaporin suggested that miR169 would directly or indirectly regulates both genes under drought stress.

Drought stress induced expression of miR169g in rice (Zhao et al., 2007) is clear evident of miR169’s association with drought response mechanism .

This miRNA may be regulated by DRE present in upstream sequencesduring drought stress. The present study and Zhou et al., 2010 further support the drought induced significant up-regulation in banana and rice respectively. The up-regulation/over expression of miR169 cause drought tolerant in crop plants (Zhang et al., 2010).

However the regulatory pathway, due to its dynamic and regulation of multiple genes and the impact of drought induced up or down regulation for the particular tissues or species has to be worked out individually.

Reports also states claim that down regulation of miRNA, enhance the drought tolerance (Zhou et al., 2010) capacity in plants. The expression analysis of predicted target failed to provide any correspondence with down-regulation of miRNA under drought in rice. Thus it may involve in post transcriptional regulation viz., repressing the translation of target genes.

In many cases, miRNAs predicted to have same target genes indeed have different functions during development (Sieber et al., 2007).

However, the drought induced up-regulation of miR169 under drought stress in banana responsible for tolerant mechanism.

Moreover, miRNA regulation peaks when the drought progress.

miR156miR156 regulates the genes coding for the most conserved plant protein SQUAMOSA PROMOTER BINDING PROTEIN-LIKE during drought/salt stress and their expression was found to be high during drought stress (Shen et al.,2010)

The other role of miR156 are regulation of phase change and flowering and other developmental stages of plants.

However, the observation of low expression of miR156 in drought stressed rice plants makes to think us about their multi-functionality, which depends the current physiological conditions (Gou et al., 2011) or abiotic stresses.

miR2118

The miRNA family-miR2118 was up-regulated in drought conditions well documented in Medicago truncatula (Wang et al., 2011) and its expression also found to be high during other abiotic stresses like cold, salinity and ABA.

The target being TIR-NBS-LRR domain protein, their association with plant defense mechanism is undoubtedly one of the important events in plant response.

(1) Mature miRNAs have sequences with no more than three nucleotide mismatches compared with all previously known mature plant miRNAs;

(2) Sequences of miRNA precursors can fold into an appropriate hairpin secondary structure that contains the 22 nt mature miRNA sequence within one arm of the hairpin structure;

(3) The secondary structures of miRNA precursors have higher negative minimal free energies (MFEs) and minimal free energy indices (MFEIs) than other types of RNAs;

(4) miRNA has an A+U content of 30–70%;

(5) miRNA has fewer than six mismatches with the opposite miRNA sequence in the other arm; (6) no loop or break in miRNA sequence is allowed.

The discovery of microRNAs (miRNAs) as gene regulators has led to a paradigm shift in the understanding of post-transcriptional gene regulation in plants and animals.

miRNAs have emerged as master regulators of plant growth and development. Evidence suggesting that miRNAs play a role in plant stress responses arises from the discovery that miR398 targets genes with known roles in stress tolerance.

In addition, the expression profiles of most miRNAs that are implicated in plant growth and development are significantly altered during stress.

These later findings imply that attenuated plant growth and development under stress may be under the control of stress-responsive miRNAs.

(Sunkar R, Li YF, Jagadeeswaran G. 2012 Functions of microRNAs in plant stress responses. Trends Plant Sci 17(4):196-203. [abstract])

Plant miRNA and their role

Plant microRNA annotation standardized (2008) Meyers BC et al. 2008 Criteria for Annotation of Plant microRNAs. Plant Cell 20(12):3186-90.

Near-perfect complementarity - The computational identification of miRNA targets in plants is relatively more straightforward than in animals,

In plants, most miRNAs have perfect or near perfect complementarity to their mRNA targets. Upon binding to their mRNA targets, the miRNA-containing RISCs function as endonucleases, cleaving the mRNA

Plant microRNA database goes online at China Agricultural University (2010)integrates available plant miRNA data deposited in public databases, gleaned from the recent literature, and data generated in-house includes predicted sequences http://bioinformatics.cau.edu.cn/PMRD/Zhang Z et al. 2010 PMRD: plant microRNA database. NAR 38 (Database issue): D806-D813.

MicroRNAs responsive to biotic and abiotic stresses in diverse plant species

Three miRNAs namely miR156, miR399 and miR169 were identified from these ESTs. Apart from these, two miRNAs (miR2118 and miR396), already identified in other plants against stress (Wang et al., 2011) were also used for studying the expression profiling of banana under drought stress condition.

The secondary structure and stability of the potential miRNA sequences, predicted by the bioinformatics approach, were analyzed by online software Mfold 3.2 (http://mfold.bioinfo.rpi.edu/cgi-bin/rna-form1.cgi), and and further miRNA prediction criteria proposed by Ambros et al. (2003) were followed

miRNA reference gene selection

Tool used : GeNorm

OutputPrior to quantitative expression profiling of miRNA in drought stressed samples, selection of suitable reference miRNA gene is much required.

Hence as a first step to identify the suitable reference miRNA gene, the mean cp (cycle crossing point) values of all five miRNAs gathered from different tissue as well as drought stressed samples and applied to GeNorm software.

Based on the least variable values , the genes were ranked for the reference gene selection

Normalization

ProblemAny variation in gene expression levels is composed of both true biological and experimentally induced (technical) variation.

AdvantageIt is to reduce the technical variation within a dataset, enabling a better appreciation of the biological variation.

A geNorm analysis determines the expression stability value for each gene (M value) and it calculates normalization factor V values. Both values are subsequently used to determine the optimal number and set of reference RNAs to be used in further studies.

Specific miRNAs that demonstrate the least variability across experimental conditions under Drought and across a wide variety of tissues ,we have identified the following candidate control genes as showing the least variability: miR399 for drought