Comprehensive genome-wide identification and ... - Springer Link

Plant Cell Tiss Organ Cult (2014) 118:279–292 DOI 10.1007/s11240-014-0480-x

ORIGINAL PAPER

Comprehensive genome-wide identification and expression profiling of foxtail millet [Setaria italica (L.)] miRNAs in response to abiotic stress and development of miRNA database Yusuf Khan • Amita Yadav • Venkata Suresh Bonthala Mehanathan Muthamilarasan • Chandra Bhan Yadav • Manoj Prasad

•

Received: 6 November 2013 / Accepted: 25 March 2014 / Published online: 1 April 2014 Ó Springer Science+Business Media Dordrecht 2014

Abstract MicroRNA (miRNA)-guided post-transcriptional regulation is an important mechanism of gene regulation during multiple biological processes including response to abiotic stresses. Foxtail millet is a model crop, which is genetically closely related to several bioenergy grasses and also known for its potential abiotic stress tolerance. Hence deciphering the role of miRNAs in regulating stress-responsive mechanism would enable imparting durable stress tolerance in both millets and bioenergy grasses. Considering this, a comprehensive genome-wide in silico analysis was performed in foxtail millet which identified 355 mature miRNAs along with their secondary structure as well as corresponding targets. Predicted miRNA targets were found to encode various DNA binding proteins, transcription factors or important functional enzymes, which could be the crucial regulators in plant abiotic stress responses. All the 355 miRNAs were physically mapped onto the foxtail millet genome and in silico tissue-specific expression for these miRNAs were studied. Comparative mapping of the 355 miRNAs between foxtail millet and other related grass species would assist miRNA studies in these genetically closelyrelated plants. Expression profiling was performed for eight candidate miRNAs under diverse abiotic stresses in foxtail Yusuf Khan, Amita Yadav and Venkata Suresh Bonthala have contributed equally to this work.

Electronic supplementary material The online version of this article (doi:10.1007/s11240-014-0480-x) contains supplementary material, which is available to authorized users. Y. Khan A. Yadav V. S. Bonthala M. Muthamilarasan C. B. Yadav M. Prasad (&) National Institute of Plant Genome Research (NIPGR), Aruna Asaf Ali Marg, New Delhi 110067, India e-mail: [email protected]

millet, which unravelled the putative involvement of these miRNAs in stress tolerance. With an aim of providing the generated miRNA marker information to the global scientific community, a foxtail millet MiRNA Database (FmMiRNADb: http://59.163.192.91/FmMiRNADb/index. html) has also been constructed. Overall, the present study provides novel insights onto the role of miRNAs in abiotic stress tolerance and would promisingly expedite research on post-transcriptional regulation of stress-related genes in millets and bioenergy grasses. Keywords Abiotic stress Comparative mapping Database Foxtail millet (Setaria italica) Genome-wide MicroRNA (miRNA) Physical map Synteny

Introduction Increasing world population along with prevalence of hunger and under-nutrition at alarming rates challenges the food security. On the other hand, growth and productivity of crop plants are adversely affected by biotic and abiotic stresses. Abiotic stresses are the major cause of crop damage, which impose an average yield loss of 50 % and above in crop plants (Bray et al. 2000). To tackle this, plants have developed sophisticated mechanisms involving complex molecular events and associated gene expressions which ultimately enable the plant to endure and surpass the hostile situations. Gene expression is regulated at three levels including transcriptional, post-transcriptional and post-translational level. In the post-transcriptional level of gene regulation, microRNAs (miRNAs) play a significant role as key regulators (Ambros 2004; Jones-Rhoades et al. 2006). They are vital components of post-transcriptional regulation of gene expression important for multiple

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biological processes including diverse developmental pathways (Mallory et al. 2004; Kim et al. 2005), signal transduction and protein degradation (Guo et al. 2005; Zhang et al. 2006a, b) and response to diverse stress factors (Sunkar and Zhu 2004; Phillips et al. 2007). miRNAs are *18–24-nucleotide (nt) non-coding RNAs that specifically binds to its target mRNAs and suppresses their translation by inducing cleavage of target (JonesRhoades et al. 2006; Sunkar et al. 2007). Biogenesis of miRNAs commences from single-stranded primary miRNA transcripts that form a hairpin structure by the activity of a dicer-like (DCL) enzyme. miRNA–miRNA* duplex from the hairpin structure is correctly cut out by the DCL1, hyponastic leaves 1 (HYL1) and serrate (SE) proteins. Following the addition of methyl groups by HUA enhancer 1 (HEN1) to the 30 end, the duplex is exported to the cytoplasm where it is loaded into an RNA-induced silencing complex (RISC) containing argonaute (AGO) protein and guides the cleavage of target gene (Muthamilarasan and Prasad 2013). Considering the importance of miRNAs in stress-regulated gene expression, numerous studies have been conducted on identification of stress-related miRNAs and its targets which would provide innovative tools for the genetic improvement of stress-tolerance behaviour in agronomically important crop plants (Paul et al. 2011; Sunkar et al. 2012). In support of this, a recent study had showed that the manipulation in miRNA-directed gene regulation can assist in generating plants with enhanced stress-tolerance (Sunkar et al. 2012). Noteworthy, the upor down-regulation of miRNA target genes appear to be dependent on their roles. Induced expression of miRNAs results in suppression of target gene expression, which may act as negative regulators of stress tolerance. Conversely, suppressed expression of miRNAs results in higher accumulation of targets. This ultimately results in better stress tolerance (Sunkar et al. 2007). The availability of reports on miRNA identification and expression analysis in many plant species (Sunkar et al. 2012) had encouraged the present study in foxtail millet (Setaria italica L.) which is a model crop for studying the genetics and genomics of several bioenergy grasses (Doust et al. 2009; Lata et al. 2013; Muthamilarasan et al. 2013). Availability of the genome sequence (Zhang et al. 2012; Bennetzen et al. 2012; Lata and Prasad 2013) of foxtail millet facilitated the genome-wide studies in this model crop (Pandey et al. 2013; Puranik et al. 2013; Mishra et al. 2014) and in view of this, we performed a genome-wide scanning in order to identify the miRNAs present in its genome. This was achieved by mapping the complete set of plant miRNAs on to the genomic as well as conserved domain sequences (CDS) of foxtail millet (Sunkar and Jagadeeswaran 2008). The identified homologous miRNAs

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Plant Cell Tiss Organ Cult (2014) 118:279–292

from foxtail millet were used for secondary structure prediction. After processing the miRNAs through stringent quality control procedures, targets were predicted and functional annotation of target sequences was performed. All the miRNAs were then physically mapped onto the nine chromosomes of foxtail millet and comparative genome mapping was conducted between foxtail millet, and sorghum, maize, rice and Brachypodium distachyon. Further, expression profiling of candidate miRNAs under abiotic stress conditions were performed using northern hybridization and quantitative stem-loop RT-PCR. Being a comprehensive report on genome-wide identification and expression profiling of miRNAs in the model plant foxtail millet, this report will serve as a solid base for functional genomic studies towards deciphering the stress-tolerance mechanism in foxtail millet.

Materials and methods In silico mining of microRNAs from foxtail millet genome The complete set of plant miRNA sequences was downloaded from miRBase release 20 (Kozomara and GriffithsJones 2011). The genome sequences and CDS of S. italica were retrieved from Phytozome (ftp://jgi-psf.org/pub/ compgen/phytozome/v9.0/Sitalica/; Goodstein et al. 2012). The plant miRNA sequences were then mapped onto the S. italica genomic and CDS sequences using the alignment tool Bowtie (Langmead and Salzberg 2012) by allowing three mismatches (Das and Mondal 2010). The pre-miRNAs were determined by retrieving the flanking region of 250-nt upstream and 250-nt downstream of each aligned miRNA loci in foxtail millet genome. RNAfold (Hofacker et al. 1994) was used to predict the secondary structure of pre-miRNAs, and the mature miRNAs were selected by performing a string of stringent analyses defined by Meyers et al. (2008), and Sunkar and Jagadeeswaran (2008). The criterions for screening mature miRNAs were (1) the predicted mature miRNAs should not have more than 3 nt mismatches as compared to known mature miRNAs, (2) the miRNA precursor sequence should tend to form a marked stem-loop hairpin secondary structure, (3) the mature miRNA sequence should be present in either of the arms of hairpin structure, (4) miRNAs ought to possess 1–4 mismatches with the opposite miRNA* sequence in the other arm, and (5) no loops or breaks appear in miRNA* sequences (Zhang et al. 2006c). Further filtering was performed by selecting the miRNAs with minimum free energy index (mfei) below -0.75 and BLAST searching the predicted miRNAs against known plant pre-miRNAs with e-value cut off of e-05, where minimum alignment


length should be half of known pre miRNAs. All the predicted miRNAs were manually checked to determine if they are matching to any transposons or retrotransposons. miRNA target prediction and GO analysis The targets of the miRNAs are predicted using web-based psRNA Target Server (http://biocomp5.noble.org/psRNA Target/; Dai and Zhao 2011) under default parameters. The functional annotation of target sequences and the analysis of annotation data were performed using Blast2GO (http:// www.blast2go.com). The nucleotide sequences of miRNA targets were imported into Blast2GO program and three steps were executed, viz. (1) BLASTx against the nonredundant protein database of NCBI, (2) mapping and retrieval of GO terms associated with the BLAST results, and (3) annotation of GO terms associated with each query to relate the sequences to known protein function. The program provides the output defining three categories of GO classification namely molecular functions, biological processes and cellular components. Physical mapping of predicted miRNAs on foxtail millet genome and comparative genome mapping between foxtail millet and other grass species The predicted miRNAs were searched by BLASTn against the whole genome sequence of S. italica using default parameters to identify the chromosomal position of the respective miRNAs. The physical map was finally visualized in MapChart software (Voorrips 2002). The homology relationship between predicted foxtail millet miRNA and miRNAs of sorghum, maize, rice and Brachypodium were visualized with visualization blocks using Circos 0.55 (http://circos.ca) (Krzywinski et al. 2009).

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Plant materials, stress treatments and total RNA isolation Seeds of two foxtail millet cultivars, namely IC403579 (stress tolerant) and IC480117 (stress sensitive) were surface sterilized in 3 % sodium hypochlorite for 20 min and rinsed 10–12 times (1 min each time) in distilled water (Lata et al. 2011). The seedlings were grown in a plant growth chamber containing two cabinets (PGC-6L; Percival Scientific Inc., USA) for 21 days after germination at 28 ± 1 °C day/23 ± 1 °C night/70 ± 5 % relative humidity with a photoperiod of 14 h and a photosynthetic photon flux density of 500 lmol m-2 s-1. The plants were watered daily with one-third strength Hoagland’s solution (Lata et al. 2011). Twenty-one day old seedlings were used for stress treatments. Dehydration stress was applied by transferring seedlings into the Hoagland’s solution containing 20 % polyethylene glycol (PEG-6000). Salinity stress was imposed by transferring seedlings into the Hoagland’s solution supplemented with 250 mM NaCl. For cold stress, the seedlings were kept in 4 °C. All the stress treatments were given for 6 h. The control plants were cultured in the same way as the stress treated plants but without the addition of PEG, NaCl and cold treatment. Whole seedlings were collected and used as such for RNA isolation. All experimental data are the means of at least three independent experiments and, for each experiment, *100 mg seedling samples were collected by random sampling. RNA was isolated using TRI Reagent (Sigma) following manufacturer’s instructions. The quality and purity of the preparations were determined at OD260:OD280 nm absorption ratio (1.8–2.0) using NanoDrop and the integrity of the preparations were confirmed by electrophoresis in a 1.2 % agarose gel containing formaldehyde. Small RNA enrichment and northern blot hybridizations

In silico expression analysis To elucidate the tissue-specific expression profile of miRNA genes and its targets, the S. italica Illumina RNAHiSeq reads from four tissues namely spica, stem, leaf and root were retrieved from European Nucleotide Archive [SRX128226 (spica); SRX128225 (stem); SRX128224 (leaf); SRX128223 (root)]. The RNA-seq data was then mapped onto the gene sequences of S. italica by CLC Genomics Workbench v.4.7.1 (http://www.clcbio.com/ genomics). The number of reads mapped was normalized by RPKM (reads per kilobase per million) method. The heat map showing tissue specific expression was generated on the RPKM value for each gene in all the tissue samples using TIGR MultiExperiment Viewer (MeV4) software package (Saeed et al. 2003, 2006).

Small RNA enrichment was preformed according to Lu et al. (2007). Precisely, high molecular weight RNAs were precipitated by adding 50 % PEG (MW = 8,000) to a final concentration of 5 % and 5 M NaCl to a final concentration of 0.5 M. Supernatant having small RNA was then precipitated by ethanol. Enriched small RNA (8–10 lg) was resolved in 15 % denaturing polyacrylamide/19 TBE/8M urea gels (100 V) and blotted on Amersham Hybond N? nylon membrane (GE Healthcare). RNA on membrane was fixed by UV-crosslinking. Antisense of mature miRNAs was end-labeled with [c-32P] ATP by using the T4 polynucleotide kinase (NEB) as per the manufacture’s protocol. Hybridization was carried out by overnight incubation of membrane at 42 °C in hybridization buffer (7 % SDS, 0.5 M sodium phosphate, 1 mM EDTA) and signals were

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detected by autoradiography and images were scanned using a phosphor-imager (Typhoon 9210, GE Healthcare) and quantified using Bio-Rad Quantity One software (USA).

wise). A ‘Tutorial’ has also been provided to increase the usability of the database.

Results and discussion Expression analysis of predicted miRNAs by quantitative stem-loop real time PCR

Identification of potential miRNAs and structure prediction

The DNA contamination in total RNA was removed using TURBO DNA-freeTM Kit (Ambion, USA). The stem loop primers for cDNA synthesis, were designed according to Chen et al. (2005). miRNA-specific SL-RT reactions were performed using superscript cDNA synthesis kit (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s protocol. The forward miRNA primers were designed from the mature miRNA sequences and the reverse was the universal reverse primer for miRNA (Chen et al. 2005). The stem-loop RT-qPCR was performed according to Kumar et al. (2013) in biological triplicate. The amount of transcripts, accumulated for particular miRNA, was normalized to the internal control U6 small nuclear RNA and analyzed using the 2DDCt method (Kumar et al. 2013). Database construction To facilitate wider usage of these developed miRNAs, Foxtail millet MicroRNA Database (FmMiRNADb) has been constructed using open source softwares (Apache, PHP and MySQL). The user-friendly web-interface allows easy access of the miRNA information including their chromosomal location, length, MFE, AMFE, sequences of pre-miRNA and mature miRNA, secondary structure and target gene information. Further, the CMap feature (Youens-Clark et al. 2009) has also been integrated in the database, which enables the user to visualize the physical map of the miRNAs (either chromosome-wise or miRNA-

About 9,032 known plant miRNA sequences were aligned to foxtail millet genome and 372,695 loci were identified. It produced about 3,614 secondary structures in compliance to the criteria defined by Meyers et al. (2008) and Sunkar and Jagadeeswaran (2008). Further filtering based on mfei and BLAST search against known plant pre-miRNAs produced 355 S. italica miRNAs (sit-miR) belonging to 53 families (Supplementary Table 1). The secondary structure of all the precursors of 355 miRNAs revealed the presence of representative stem-loop structure, with the mature miRNA either on the 50 or 30 ends (Supplementary Fig. 1). Of these 355 miRNAs, eight (Sit-miR162a, Sit-miR397a, Sit-miR393, Sit-miR167b, Sit-miR156c, Sit-miR171b, SitmiR160d, Sit-miR6248a) were chosen for expression profiling under different abiotic stresses by Northern hybridization. The characteristics and secondary structure of these candidate miRNAs are given in Table 1 and Fig. 1, respectively. Comparing 53 miRNA families identified in this study with the 48 foxtail millet miRNA families earlier reported by Bennetzen et al. (2012) showed 20 families in common. This difference is plausibly due to the difference in the strategy followed for miRNA identification. In the analysis performed by Bennetzen et al. (2012), the mature miRNA sequences of two tissues were aligned on S. italica genome and reported only the miRNAs which showed perfect alignment, whereas the present study used a pipeline of

Table 1 Characteristics of eight candidate miRNAs chosen for expression profiling miRNAs

Mature sequence

Matching family

LM (nt)

LP (nt)

Strand

A?U (%)

MFE

AMFE

MFEI

Chromosome

Sit-miR162a

GGGCGCAGTGGTTTATCGATC

162

21

141

?

46.1

-60.1

-42.6

-0.8

1

Sit-miR397a Sit-miR393

TCATTGAGTGCAGCGTTGATGA TCCAAAGGGATCGCATTGATCT

397 393

22 22

92 142

? ?

37.0 39.4

-62.6 -80.5

-68.0 -56.7

-1.1 -0.9

1 3

Sit-miR167b

TGAAGCTGCCAGCATGATCTG

167

21

141

?

53.9

-65.2

-46.2

-1.0

3

Sit-miR156c

TGACAGAAGAGAGTGAGCAC

156

20

100

?

42.0

-65.5

-65.5

-1.1

2

Sit-miR171b

TGAGCCGAACCAATATCACTC

171/479

21

111

?

46.8

-53.3

-48.0

-0.9

1

Sit-miR160d

TGCCTGGCTCCCTGAATGCC

160

20

100

?

37.0

-58.4

-58.4

-0.9

6

Sit-miR6248a

TATTTAAGAATGGAGGCAGT

6248

20

121

?

70.2

-37.5

-31.0

-1.0

5

LM length of mature miRNAs, LP length of precursor, MFEs minimal folding free energies, AMFE adjusted minimal folding free energy, MFEIs minimal folding free energy indexes.

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Plant Cell Tiss Organ Cult (2014) 118:279–292 Fig. 1 Secondary structures of eight sit-miRNA candidates. The red coloured sequences represent the mature miRNA. (Color figure online)

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Sit-miR156-c

Sit-miR160-d

Sit-miR162-a

Sit-miR167-b

Sit-miR171-b

Sit-miR393

Sit-miR397-a

Sit-miR6248-a

stringent criteria followed by Sunkar and Jagadeeswaran (2008) to identify potential miRNAs. Targets of predicted miRNAs and GO annotation Target prediction from psRNA target web server identified about 1,057 putative targets for 355 miRNAs of foxtail millet and it was evidenced that a single miRNA may regulate several target genes and vice versa (Supplementary Table 2). These results conforms other previous reports which suggested that one miRNA may target several genes (Zhou et al. 2010; Reinhart et al. 2002). Putative

targets consist of transcription factors and gene families regulating signal transduction, hormone responses, metabolic process, cell growth and development. Of these, transcription factors were the maximum targets of sitmiRNAs are transcription factors and this is in consistent with previous report in maize (Jiao et al. 2011). However, functions of some targets genes are largely unknown. Similar to the previous reports (Sunkar et al. 2005; Lu et al. 2008a, b), the predominant mode of gene regulation by foxtail millet miRNAs has been appeared through cleavage as compared to translational inhibition (Supplementary Table 2). The GO analysis conducted through Blast2Go

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Fig. 2 Gene ontology (GO) distributions for the miRNA targets. The Blast2Go program defines the gene ontology under three categories, molecular functions, biological processes, and cellular component

showed the putative participation of miRNA targets in diverse molecular and biological processes (Fig. 2). Cellular localization prediction showed that predominant target proteins are localized in the nucleus, and some in other cellular components (Fig. 2). The potential targets identified in the present study and its GO data would assist in investigating the functional importance of predicted miRNAs in physiological and stress related signalling mechanism. Physical mapping of miRNAs The determination of genomic distribution of 355 miRNAs on the foxtail millet genome revealed its physical localization on the nine chromosomes of foxtail millet with an average density of 0.9 miRNAs/Mb (Fig. 3; Supplementary Table 3). The average miRNA density was maximum (1.4/Mb) in the chromosome 1. Analysing the chromosome-wise distribution and frequency of these physically mapped miRNAs showed higher frequency of miRNAs mapped on chromosome 1 (58 miRNAs, 16.3 %) and minimum on chromosome 4 (34, 9.6 %) (Fig. 3; Supplementary Table 3). For the first time, this report shows the chromosomal location of 355 miRNAs on foxtail millet genome and this information would promisingly expedite

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the functional genomics studies for deciphering the role of miRNAs. In silico comparative genome mapping between foxtail millet and other grass species The physically mapped 355 miRNAs on the nine chromosomes of foxtail millet were compared with their physical location on the chromosomes of other related grass genomes of sorghum, maize, rice and Brachypodium (Fig. 4; Supplementary Tables 4, 5, 6, 7). The comparative genome mapping showed considerably significant proportion of sequence-based orthology and syntenic relationship of miRNA distributed over nine foxtail millet chromosomes with sorghum (696), rice (127), maize (64) and Brachypodium (41) chromosomes (Fig. 4; Supplementary Tables 4, 5, 6, 7). A complex syntenic relationship was revealed between the genomes of foxtail millet and sorghum. Each miRNA gene of foxtail millet chromosome showed synteny towards almost all the chromosomes of sorghum, resulting in 696 genomic regions on ten chromosomes of sorghum (Fig. 4; Supplementary Table 4). Between foxtail millet and maize genomes, miRNAs distributed over nine chromosomes of foxtail millet showed significant matches with 64 genomic regions of ten

Plant Cell Tiss Organ Cult (2014) 118:279–292 Chr2

Chr1 0.7 2.0 2.1 2.3 2.6 4.0 5.5 5.9 6.6 7.0 7.2 7.3 8.6 10.0 10.2 10.6 12.4 12.8 13.1 13.3 14.7 14.8 16.0 22.2 23.9 24.3 24.7 25.4 25.5 26.5 27.0 27.2 27.8 30.2 30.5 30.7 31.4 32.4 32.8 34.0 34.2 34.9 35.7 39.7 39.8 40.9 41.3 41.5 41.6 41.7

Sit-miR5568.1 Sit-miR5568.2 Sit-miR399a Sit-miR399g Sit-miR399b Sit-miR5568.3 Sit-miR1127h Sit-miR5568.4 Sit-miR5568.5 Sit-miR171a Sit-miR5568.6 Sit-miR156a Sit-miR5568.7 Sit-miR160a Sit-miR397a Sit-miR397b Sit-miR5568.8 Sit-miR5568.9 Sit-miR5568.160 Sit-miR5045 Sit-miR781 Sit-miR5568.10 Sit-miR1848l Sit-miR5568.11 Sit-miR5568.12 Sit-miR5568.13 Sit-miR5568.14 Sit-miR5568.15 Sit-miR5568.16 Sit-miR437a Sit-miR5568.17 Sit-miR169h Sit-miR6220c Sit-miR5568.18 Sit-miR5568.19 Sit-miR5568.20 Sit-miR5568.21 Sit-miR5568.22 Sit-miR5568.23 Sit-miR162a Sit-miR162b Sit-miR827 Sit-miR156b Sit-miR1128d Sit-miR5169e Sit-miR5205m Sit-miR394 Sit-miR171b Sit-miR5568.24 Sit-miR5568.25 Sit-miR2905 Sit-miR164c Sit-miR5568.26 Sit-miR1436 Sit-miR172 Sit-miR5568.27 Sit-miR5568.28 Sit-miR5169d

1.3 2.4 4.0 4.3 5.8 6.3 7.3 7.6 8.3 9.9 10.1 11.8

Sit-miR2645 Sit-miR160b Sit-miR5568.29 Sit-miR5205k Sit-miR5568.30 Sit-miR5568.31 Sit-miR1848r Sit-miR5568.32 Sit-miR1848q Sit-miR1848p Sit-miR5568.33 Sit-miR5568.34

13.9

Sit-miR1128b

16.1 16.5 17.4

Sit-miR1127g Sit-miR1131 Sit-miR5205j

Chr3

5.4 6.2 7.7

Sit-miR5205f Sit-miR393 Sit-miR5568.41 Sit-miR5568.42 Sit-miR5568.43 Sit-miR5568.44 Sit-miR5568.45 Sit-miR5568.46 Sit-miR5568.47 Sit-miR5568.48 Sit-miR395a Sit-miR5568.49 Sit-miR169b Sit-miR5568.50

11.2

Sit-miR5568.51

0.3 0.5 1.1 1.3 2.3 2.9 3.7 3.9 4.8 4.9

15.0 15.6 16.2 16.4 18.0

20.6

24.4 25.1 26.4

Sit-miR5568.35

27.9 30.1 30.5

Sit-miR1848o Sit-miR1128a Sit-miR5205i Sit-miR156c Sit-miR156d Sit-miR5169c Sit-miR1848y Sit-miR166a Sit-miR156e Sit-miR1117i Sit-miR5568.36 Sit-miR5568.37 Sit-miR169a

30.8 31.4 32.4 32.8 34.7 34.9 35.0 36.8 36.9 40.9 41.4

Sit-miR5568.38 Sit-miR5568.39 Sit-miR5205h

43.7 44.2 44.3 45.4

Sit-miR5568.40 Sit-miR1318 Sit-miR5205g Sit-miR6253

48.1

Sit-miR164b

285

26.1 28.5

Sit-miR5205e Sit-miR5568.52 Sit-miR1848n Sit-miR5381c Sit-miR5568.53

1.3 2.0 2.1 3.6 3.7 3.9 5.6 6.9 7.6 7.7 8.0 9.0 9.9 10.0 10.1 10.5 12.5 14.7 15.9 16.4

Sit-miR159b Sit-miR6220a Sit-miR5568.84 Sit-miR1127c Sit-miR169c Sit-miR5568.85 Sit-miR5568.86 Sit-miR159a Sit-miR5568.87 Sit-miR3879a

10.3

Sit-miR5568.88

11.5

Sit-miR156g

Sit-miR5568.54 Sit-miR1117h Sit-miR5169b Sit-miR5568.55 Sit-miR1439a

23.8

Sit-miR1127f Sit-miR5568.56

39.1 39.5 40.6 40.8 42.4 42.5 45.2 46.2 46.5 46.6 46.8 47.0

Sit-miR5568.57 Sit-miR1268 Sit-miR6248c Sit-miR5568.58 Sit-miR1435a Sit-miR1848m Sit-miR5568.59 Sit-miR1127e Sit-miR166b Sit-miR5568.60 Sit-miR5568.61 Sit-miR1127d Sit-miR167a Sit-miR167b Sit-miR5568.62 Sit-miR437b

3.5 3.9 4.5 5.3 5.8 5.9 6.6 7.3 8.3 8.4 9.0 9.4 10.2 12.6

Sit-miR5568.78

33.4 34.4

Sit-miR5568.79 Sit-miR399c Sit-miR399d Sit-miR5568.80 Sit-miR5568.81 Sit-miR156f Sit-miR6220b Sit-miR437d

38.1 38.3 38.4 38.5

Sit-miR160c Sit-miR5568.82 Sit-miR5381b Sit-miR5568.83

28.7

Sit-miR1848e Sit-miR1117b Sit-miR160d Sit-miR2275d Sit-miR2275c Sit-miR5568.98 Sit-miR5568.99 Sit-miR5384a Sit-miR1848d Sit-miR5568.100 Sit-miR160e Sit-miR2275b Sit-miR2275a Sit-miR2981 Sit-miR5381a Sit-miR1848c Sit-miR1848b Sit-miR5205c Sit-miR5568.101

Chr7

Sit-miR1127a

3.3

Sit-miR5568.110

13.1 13.2 14.3 15.7 15.8 16.9

Sit-miR1117d Sit-miR1117c

Sit-miR5568.102 Sit-miR5568.103 Sit-miR5205b Sit-miR1117a Sit-miR1848a Sit-miR5568.104 Sit-miR5568.105 Sit-miR5568.106

23.3 23.8

Sit-miR5568.89 Sit-miR1848k

27.4

Sit-miR5568.90

27.5 28.5

Sit-miR3885a Sit-miR5568.107

30.8 31.1 31.4 32.1

Sit-miR5568.91 Sit-miR5568.92 Sit-miR5568.93 Sit-miR156h

30.4 30.8 31.7 32.8

Sit-miR5568.108 Sit-miR5568.109 Sit-miR160f Sit-miR5205a

34.3 35.3 36.7 37.0 37.8 39.5

34.0

Sit-miR169d

40.3

Sit-miR3809 Sit-miR5568.94 Sit-miR5568.95 Sit-miR1127b Sit-miR5568.96 Sit-miR1848i Sit-miR5049 Sit-miR6248b Sit-miR1848h

42.4

Sit-miR6248a

44.6 44.8 46.0 46.8

Sit-miR1848g Sit-miR1848f Sit-miR5205d Sit-miR5568.97

22.0 22.7

39.7

Chr8 0.0

1.5

18.6 17.1 17.8

25.2

31.4 34.5 34.9

0.4 2.0 3.2

21.1 21.3 21.5

30.9

50.0 50.3

2.0 2.1 2.3 4.4 5.0 5.3 6.7 7.0 7.4 7.8

Sit-miR2118b Sit-miR2118a

28.9

48.3

Sit-miR399f Sit-miR5568.63 Sit-miR1117g Sit-miR5568.64 Sit-miR437c Sit-miR1117f Sit-miR5568.65 Sit-miR5568.66 Sit-miR5568.67 Sit-miR5568.68 Sit-miR5568.69 Sit-miR5568.70 Sit-miR5568.71 Sit-miR5568.72 Sit-miR5568.73 Sit-miR5169a Sit-miR5568.74 Sit-miR5568.75 Sit-miR5568.76 Sit-miR5568.77 Sit-miR1117e

Chr6

Chr5

Chr4

20.5

22.4 22.6 22.7 23.4 24.0 27.8 29.4 30.2 31.2 32.1 32.2 32.7 33.1 34.2 34.6

Sit-miR1117o Sit-miR5568.111 Sit-miR5568.112 Sit-miR5568.113 Sit-miR5568.114 Sit-miR5568.115 Sit-miR5205s Sit-miR5568.116 Sit-miR2118j Sit-miR2118i Sit-miR2118h Sit-miR2118g Sit-miR2118f Sit-miR2118e Sit-miR2118d Sit-miR2118c Sit-miR482b Sit-miR482a Sit-miR5568.117 Sit-miR5205r Sit-miR5568.118 Sit-miR5568.119 Sit-miR5568.120 Sit-miR399e Sit-miR169e Sit-miR5568.121 Sit-miR395b Sit-miR6197 Sit-miR5568.122 Sit-miR1439b Sit-miR5205q Sit-miR1848x Sit-miR5568.123

Sit-miR5565

6.2 6.7 8.1 8.9 9.6 10.2 11.2 11.7 12.1

Sit-miR1117n Sit-miR5169h Sit-miR5169g Sit-miR5568.124 Sit-miR5568.125 Sit-miR156i Sit-miR5568.126 Sit-miR3897 Sit-miR169f

16.1

Sit-miR5568.127

Chr9 0.5 2.1 2.4 2.5 3.3

Sit-miR169g Sit-miR390 Sit-miR5568.138 Sit-miR5568.139 Sit-miR167c

5.4 5.8

Sit-miR5205o Sit-miR167d

8.6 9.4

Sit-miR5568.140 Sit-miR5205n

10.6

Sit-miR5568.141

12.5 12.6 13.6

Sit-miR171c Sit-miR5568.142 Sit-miR166c

16.6

Sit-miR437f

19.1

Sit-miR5568.143 Sit-miR5568.144 Sit-miR1128e

25.8

Sit-miR5568.145

33.8 35.1 35.5 37.0 37.2 37.9 38.6

Sit-miR5568.146 Sit-miR5568.147 Sit-miR5568.148 Sit-miR5568.149 Sit-miR5568.150 Sit-miR5568.151 Sit-miR1128c

40.3

Sit-miR5568.152

18.5

21.6 23.0 23.5 24.2 24.7 26.7 27.1 27.6 29.1 29.4 29.6 31.0 31.4 32.8 33.8 33.9 34.0 35.1 38.8 39.4 39.9 40.0

Sit-miR535 Sit-miR5568.128 Sit-miR5568.129 Sit-miR1117m Sit-miR3885b Sit-miR1848w Sit-miR5568.130 Sit-miR1117l Sit-miR5169f Sit-miR5205p Sit-miR437e Sit-miR3879b Sit-miR5568.131 Sit-miR5568.132 Sit-miR1435b Sit-miR5568.133 Sit-miR1117k Sit-miR1117j Sit-miR1848u Sit-miR5568.134 Sit-miR5568.135 Sit-miR5568.136 Sit-miR5568.137 Sit-miR1848t

41.8

Sit-miR1848s

43.7

47.1 48.2 48.9 49.8

Sit-miR437g Sit-miR5568.153 Sit-miR5568.154 Sit-miR5568.155 Sit-miR437h Sit-miR5057 Sit-miR5568.156 Sit-miR5568.157 Sit-miR164a Sit-miR5568.158

53.9

Sit-miR5384b

55.5 56.1

Sit-miR166d Sit-miR5568.159

58.6

Sit-miR5205l

45.2 46.7 46.8

Fig. 3 Physical map of the foxtail millet showing the chromosomal location of 355 miRNAs. The numbers in the left indicates the chromosomal location (in Mbp) and the respective miRNA IDs are

provided in the right. The eight candidate miRNAs chosen for expression profiling were shown in bold and underlined

maize chromosomes (Fig. 4; Supplementary Table 5). All the nine foxtail millet chromosomes showed considerable and higher average frequency of miRNA gene-based syntenic relationship with specific maize chromosomes. The comparative mapping between foxtail millet and rice genomes revealed syntenic relationship of miRNAs distributed over nine chromosomes of foxtail millet with 127 genomic regions on 12 chromosomes of rice (Fig. 4; Supplementary Table 6). In foxtail millet—Brachypodium synteny, miRNAs of foxtail millet showed significant matches with 41 genomic regions of Brachypodium chromosomes (Fig. 4; Supplementary Table 7). As expected, maximum syntenic relationships of foxtail millet chromosomes with sorghum chromosomes based on miRNA were evident. The miRNA based comparative mapping would serve as a platform for identifying potential miRNAs in the investigated species as well as in other millets and bioenergy crop plants.

both miRNAs and its corresponding targets. Of the 355 sitmiRNAs, expression profiling could be investigated for 127 miRNAs due to the difficulty of MeV4 in detecting low level transcripts. The heat map displayed higher expression pattern for miRNAs sit-miR156c, sit-miR156g, sit-miR156h, sit-miR166c and sit-miR166d in all the three tissues namely leaf, flower and root (Fig. 5). The heat map generated for the miRNA targets showed a differential expression pattern with lower/or no expression levels for 64 targets (*13 %) and 44 targets (*9 %) showed higher expression in all the four tissues namely root, leaf, stem and spica (Supplementary Fig. 2).

In silico tissue-specific expression profiling of sitmiRNAs Heat map generated for examining the tissue-specific expression showed a differential transcript abundance of of

Northern blot analysis and stem-loop RT-qPCR In order to gain additional support on the expression of predicted miRNAs, northern blot hybridizations and quantitative stem-loop real time PCR were carried out using RNA samples from different abiotic stress treated seedlings of foxtail millet. A similar kind of expression pattern was observed in northern blot hybridizations and stem-loop RT-qPCR. The differential accumulation of eight randomly selected miRNAs (Sit-miR162a, SitmiR397a, Sit-miR393, Sit-miR167b, Sit-miR156c, Sit-

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Fig. 4 Comparative mapping between 355 physically mapped miRNAs on foxtail millet chromosomes with sorghum, maize, rice and Brachypodium. sit S. italica; osa O. sativa, sbi S. bicolor, zma Z. mays, bdi B. distachyon

Fig. 5 Heat-map showing the expression pattern of Sit-miRNA RNA-Seq, based on the fold-change values in the treated sample when compared with its unstressed control sample. The color scale for fold-change values is shown at the right

miR171b, Sit-miR160d, Sit-miR6248a) was determined in two cultivars having contrasting stress tolerance behaviour. The hybridization probes for northern analysis and primers for cDNA synthesis and stem-loop RT-qPCR are described in Supplementary Table 8 and the results were shown in Figs. 6 and 7. Functional annotation of predicted targets of the candidate miRNAs was summarized in Table 2.

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Sit-miR156c showed homology with miR156 family members. The members of miR156 family possess conserved functions across plant species and their role in regulating the squamosa promoter binding protein (SBP)like genes during developmental processes was well studied (Bonnet et al. 2006; Gandikota et al. 2007). Although the role of miR156 in stress-associated regulatory mechanisms is still unclear, studies showed up-regulation of


IC403579

287

IC480117

Sit-miR156c Sit-miR162a

Sit-miR167b

Sit-miR397a

Sit-miR393

Sit-miR160d

Sit-miR171b Sit-miR6248a

Fig. 6 RNA gel blot analysis showing the regulation of miRNA expression by drought, salt and cold treatment in stress tolerant and susceptible cultivar of foxtail millet. The 25S rRNA bands were visualized by ethidium bromide staining of polyacrylamide gels and

are used as loading controls. Marker represents 17 and 24 nt. IC403579 tolerant, IC480117 sensitive. The marker lane and sRNA blots were hybridized separately for the samples sit-miR156c, sitmiR162a, sit-miR167b, sit-miR160d, sit-miR171b

miR156 in Arabidopsis subjected to salt stress (Liu et al. 2008) and down-regulation in Populus subjected to cold stress (Lu et al. 2008a, b). In the present study, a slight down-regulation of Sit-miR156c during cold stress in both the cultivars was observed, which is in accordance to the previous reports (Lu et al. 2008a, b). Sit-miR162a is homologous to miR162 family and is reported to negatively regulate the miRNA biogenesis pathway by inhibiting DCL1 transcripts (Xie et al. 2003). Previous report showed the up-regulation of miR162 family members in maize during salinity stress (Khraiwesh et al. 2011). However, the present study has not observed any significant difference in expression of Sit-miR162a in tolerant and sensitive cultivars. In this study, Sit-miR167b (homologous to miR167) did not show any significant difference in expression during abiotic stresses (dehydration, salt and cold). However, miR167 was reported to get up-regulated during drought as well as salt stress in Arabidopsis (Liu et al. 2008). Kinaselike domain containing proteins were predicted as target instead of ARF8, as reported by Bonnet et al. (2006). This

indicates the involvement of Sit-miR167b in gene regulatory mechanisms. Sit-miR397a (homologous to miR397) was observed to be up-regulated during drought and cold stress in sensitive cv. IC480117 and down-regulated in tolerant cv. IC403579. This is in agreement with the previous studies conducted in Arabidopsis (Sunkar and Zhu 2004; Sunkar et al. 2012; Lu et al. 2008a, b). In addition to its role in drought and cold stress, miR397 family members are also reported to play a prime role in copper homeostasis (Sunkar 2010). Target prediction shows multi-copper oxidase protein as the target for Sit-miR397a whose role in regulation of gene expression during stress remains elusive. In the present study, Sit-miR160d, a homologue of miR160 family was found to be expressed in sensitive cultivar with reduced expression during salt treatment. Target prediction shows auxin response factor (ARF) as sitmiR160d target. Several miRNAs including miR160 and miR393 play a key role in auxin-signalling pathway and target different genes such as ARFs and transport inhibitor response1 (TIR1), respectively (Bonnet et al. 2006).

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288


Fig. 7 Analysis of miRNA expression by quantitative stem-loop real time PCR under drought, salt and cold treatment in stress tolerant and susceptible cultivar of foxtail millet. U6 small nuclear RNA was used

as endogenous control. The error bars representing standard deviation were calculated based on biological triplicates. IC403579 tolerant, IC480117 sensitive

Table 2 Summary of genes regulated by the eight candidate sit-miRNAs miRNAs

Phytozome ID of target(s)

Target description

Sit-miR156c

Si006472m, Si006471m, Si013870m, Si001804m, Si030195m, Si017749m, Si030892m, Si026656m

SBP-box gene family member

Sit-miR162a

Si014055m, Si014054m, Si014056m, Si001226m

mTERF domain containing protein

Sit-miR167b

Si005334m, Si031789m

No annotation

Si021183m, Si021184m

Pumilio-family RNA binding protein

Si021081m

Receptor-like protein kinase 2 precursor

Si003916m

Serine/threonine protein kinase

Sit-miR397a Sit-miR393

Si001625m, Si026172m, Si021577m, Si034916m, Si000806m Si009703m, Si021562m

Laccase precursor protein, putative F-box domain and LRR containing protein

Sit-miR160d

Si034525m

Auxin response factor 18, putative

Sit-miR171b

Si000466m, Si000464m, Si000415m, Si000340m

Auxin response factor

Si016508m

Scarecrow

Si016432m, Si016571m

Ring finger protein RNF10-related

Si040801m, Si033700m

No annotation

Sit-miR6248a

123


289

Fig. 8 Screenshots of FmMiRNADb. a The complete information displayed for a given miRNA Sit-miR156c. The display panel is hyperlinked to physical map, secondary structure and to other useful

databases such as Phytozome, Gramene and Uniprot. b The CMap interface displaying the physical position of the miRNA Sit-miR156c

Further, miR160 has important roles in seed germination, post-germination and in various developmental pathways where they contribute to negative regulation of ARF10 and ARF17 (Liu et al. 2007; Mallory et al. 2005). In Populus tremula miR160 was confirmed to be UV-B-responsive and in Arabidopsis, it was ABA-responsive (Xie et al. 2003; Liu et al. 2007). Sit-miR393 (homologue of miR393) was found to be up-regulated in sensitive cv. IC480117, while down-regulated in tolerant cv. IC403579. Similar results on upregulation in tolerant cultivars in response to dehydration and cold stress was reported in Arabidopsis (Sunkar and Zhu 2004; Zhou et al. 2008), rice (Arenas-Huertero et al. 2009) and common bean (Zhao et al. 2007). F-box proteins are among the predicted targets for miR393. Previous report suggested that a class of F-box protein, TIR1 is involved in the ubiquitination of the TIR1/AUX/IAA complex and thus enhancing the auxin-regulated transcription (Bonnet et al. 2006). This suggests the

combinatorial role of stress-regulated miRNAs and auxin signalling machinery. Sit-miR171b, a homologue of miR171 family was found to be down-regulated during salt stress in sensitive cultivar, but opposite expression behaviour was observed during cold stress. Target prediction analysis showed gibberellicacid insensitive (GAI), repressor of GAI (RGA) and scarecrow (SCR) (GRAS) transcription factor, as its targets which indicate the functional conservation of miRNA and targets. Further, miR171 is perfectly complementary to the scarecrow-like family of transcription factors which are involved in the control of a wide range of developmental processes including radial patterning in roots and hormone signalling (Bonnet et al. 2006). The contrasting expression pattern of Sit-miR171b indicates its involvement in both salt and cold stress. After dehydration treatment, another miRNA, SitmiR6248a (homolog of miR6248) showed the contrasting expression behaviour in both of the cultivars. However, in

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salt and cold stresses this showed the up-regulated expression in sensitive cultivar only. Till date miR6248 has been studied only in rice after arsenic treatment (Liu 2012). This is the first report mentioning the role of miR6248 in abiotic stresses. Ring finger protein has been predicted as the target gene for Sit-miR6248a and further characterization of target will help in elucidating its role in abiotic stress.

Plant Cell Tiss Organ Cult (2014) 118:279–292 Ms. Amita Yadav and Mr. Mehanathan Muthamilarasan, respectively. Authors work in this area is supported by the core grant of National Institute of Plant Genome Research, New Delhi, India. Assistance of Dr. Debasis Chattopadhyay, Scientist, NIPGR for providing server facility is greatly appreciated. Computational resources under BTISNET program (DISC facility) are thankfully acknowledged.

References Online web-resource implementation and user interface To facilitate wider use of these novel miRNAs identified in foxtail millet, an online database has been constructed [Foxtail millet miRNA database (FmMiRNADb); http://59. 163.192.91/FmMiRNADb/index.html] (Fig. 8). The FmMiRNADb is a web-based, searchable and downloadable database where the user can freely access the complete miRNA information. In addition, CMap interface has been integrated in the database to facilitate the viewing of interactive physical map. The database allows the browsing of miRNA information chromosome-wise. The complete data of all the miRNAs such as its chromosomal location, length, MFE, AMFE, sequences of pre-miRNA and mature miRNA, secondary structure and target gene information are provided in the database. Further, hyperlinks to other public databases including Phytozome (http://www.phyto zome.net/), Gramene (http://ensembl.gramene.org/), and UniProt (http://www.uniprot.org/) for each target gene will assist the user to conduct downstream analyses swiftly. For the ease of access, a tutorial has also been provided in the database (Supplementary Fig. 3). In summary, the present investigation reports 355 mature miRNAs in the model plant foxtail millet. The miRNA targets were found to be transcription factors and gene families regulating signal transduction, hormone responses, metabolic processes, cell growth and development. The unknown miRNA target genes identified in this study may be unique to foxtail millet. The online database would increase the usability of the developed information among the global research community. Being a drought tolerant crop, understanding the basic regulatory network is necessary, which governs the stress tolerance behaviour of foxtail millet. Hence studying the role of miRNAs in the stress tolerant and sensitive cultivars would provide insights in understanding the complex stress-associated regulatory mechanisms. This knowledge would assist the plant science community to conduct research and generate elite cultivars tolerant to environmental stresses and thus evading food insecurity. Acknowledgments Grateful thanks are due to Council of Scientific and Industrial Research and University Grants Commission, Government of India for providing CSIR-SRF and UGC-JRF to

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