Study of microRNA mediated gene regulation in Striga hermonthica through in-silico approach

Accepted Manuscript Study of microRNA mediated gene regulation in Striga hermonthica through in-silico approach

Swati Srivastava, Ashok Sharma PII: DOI: Reference:

S2352-2151(17)30020-X doi:10.1016/j.aggene.2017.09.004 AGGENE 55

To appear in: Received date: Revised date: Accepted date:

20 February 2017 12 July 2017 25 September 2017

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ACCEPTED MANUSCRIPT Study of microRNA mediated gene regulation in Striga hermonthica through in-silico approach Swati Srivastava and Ashok Sharma* Biotechnology Division, CSIR-Central Institute of Medicinal and Aromatic Plants, Post Office CIMAP, Lucknow, India *Corresponding Author: [email protected] Phone no.: 0522-2718517

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Fax no.: 0522-2342666

ACCEPTED MANUSCRIPT

Abstract Striga hermonthica is a parasitic plant that attacks mostly cereal crops for its growth and development. S. hermonthica parasitism affects two thirds of the arable land and over 100 milion people. It is also well known as a folk medicine due to presence of flavonoids, terpenes, saponins, cardiac glycosides, alkaloids, tannins and coumarins miRNAs are important gene regulatory elements involved in almost all biological processes during

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different biotic and abiotic stresses in plants. Mobile small RNA are reported to move bidrection between parasitic plant and host plants. The study was to investigate the miRNA

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of S. hermonthica and miRNA mediated regulation of host plant Oryza sativa genes. In-silico

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identification of miRNAs revealed 13 conserved miRNA families (miR1846c-3p, miR1848, miR1851, miR1857-5p, miR2102-3p, miR2864.1, miR417, miR437, miR444e, miR529a,

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miR810b.2, miR156e, miR5564b). Approximately, 185 genes playing diverse roles have been predicted as targets for the identified 12 miRNA families (miR1848, miR1851, miR1857-5p, miR2102-3p, miR2864.1, miR417, miR437, miR444e, miR529a, miR810b.2,

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miR156e, miR5564b). Manipulation in miRNAs or their targets may be utilized for

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developing better crop protection strategies.

Keywords: Striga hermonthica, In-silico analysis, miRNA prediction, Target genes, mobile

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small RNAs

Abbrevations: miRNA: MicroRNA, MFE: Minimal Folding Energy, MFEI: Minimal

Sequence

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Folding Index, GO: Gene Ontology, EST: Expressed Sequence Tag, GSS: Genome Survey

1. Introduction

Striga hermonthica belongs to family Orobanchaceae (Minorsky 2015; Spallek et al., 2013, Joel 2000). S. hermonthica is reported to attack cereal crops specially sorghum, pearl millet, rice, maize, tobacco and sugarcane in Africa, India, Asia, Australia and some part of the USA (Musselman 1980). S. hermonthica is also a well-known medicinal plant which has been

used

widely as traditional medicine

in Africa with a broad

spectrum of

pharmacological impacts against many physiological and infectious diseases in both animals and

humans (Hiremath et al., 2000; EL-Kamali 2009; Koua et al., 2011a,b; Okpako and

Ajaiyeoba, 2004; Khan et al., 1998; Choudhury et al., 2000; Baoua et al., 1980; Koua et al.,

ACCEPTED MANUSCRIPT 2011a,b). Communication from cell to cell occurs between organisms that have parasitic, pathogenic or symbiotic relationship. This type of

communication involves transportion of

regulatory molecules across the cellular boundaries between the host and its interacting pathogens, pests and parasitic or symbionts (Weiberg et al., 2015). Mobile small RNAs (such as siRNA

and miRNA) have been indicated to function in communiction between

hosts and their intracting

pathogens, pests and parasites (Brosnan and Voinnet, 2011;

Greenwood et al., 2016; Molnar et al., 2011). The small endogenous non-coding RNAs the regulation of expression of protein

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(ncRNAs) (21-25 nt) play an important role in

coding and noncoding genes in plants, animals and humans (Leucci et al., 2013). The

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function of ncRNAs have shown some differences between plants and animals in using

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different ribonuclease, genomic arrangement of miRNA genes, target recognition and mode of evolutionary emergence (Axtell et al., 2011). The mechanism of its interaction with

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lncRNA and siRNA in targeting DNA methylation is also different in plants and animals. miRNA regulates the gene expression by targeting complementary site at 3’ untranslated regions (UTRs) of specific mRNAs for post-transcriptional repression or degradation (He

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and Hannon, 2004; Sala-Cirtog et al., 2015, Reinhart et al., 2002). These miRNAs have multiple functions, including regulation of mRNA expression and siRNA biogenesis as well

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as various stress responses. Several methods have been reported for the identification of miRNAs such as genetic screening, direct cloning after isolation of small RNAs, computational approach and expressed sequence tags (ESTs) analysis (Zhang et al., 2006).

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In this study, we have used computational approach for the identification of miRNAs families expressed in S. hermonthica and predicted their putative targets (one of reported host plants). The available EST and GSS data of S. hermonthica provide an opportunity for conserved miRNA and their putative target transcripts. In miRBase

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the prediction of

database, 7385 mature plant miRNA are reported from 72 plant species including many medicinally as well as economically important crop species (Sala-Cirtog et al., 2015). However, no miRNAs are available for S. hermonthica in miRBase database. In S. hermonthica, 67,955 and 4356 sequences of ESTs and GSS data, respectively, were available in the NCBI database. In our study, we used EST and GSS data for in-silico identification and

characterization of conserved miRNAs and their predicted target

transcripts. The identification of miRNA and corresponding targets is necessary for the understanding of their role in controlling different biological functions (Pio et al., 2014; Qu et al., 2011).

ACCEPTED MANUSCRIPT 2. Material and methods

2.1 Computational analysis of sequence data EST

and

GSS

data

of

S.

hermonthica

were

retrieved

from

NCBI

(http://www.ncbi.nlm.nih.gov/nucest). To remove the redundancy among 72,311 (67,955 EST and 4,356 GSS) sequences, CAP3 tool (Huang and Madan, 1999) was used which resulted into 8226 contigs. BLASTX (e-value<=1e-5) was performed against protein UniprotKB/Swissprot

(release

2010_12)

and

UniProtKB/TrEMBL

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databases

(release

2011_01) to remove the protein-coding sequences. The non-coding contigs were further used

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for the identification of miRNAs. miRNAs and their corresponding targets were identified by

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C-mii (version 1.11) (Numnark et al., 2012). The putative miRNA candidates were scanned against the available miRNAs in Oryza sativa (660), Sorghum bicolor (172), Zea mays (172)

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from miRBase using BLASTN (e-value cut-off = 10). The RNA database, Rfam 10 (Griffiths-Jones et al., 2003) was used to predict primary and precursor structure of identified miRNAs. UNAFold parameters as applied were maximum base pair distance 3000,

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maximum bulge/interior loop size 30 and single tread run 37o C temperature.

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2.2 Prediction of potential S. hermonthica miRNAs and their target transcripts For a homology search based miRNA identification, miRNAs of O. sativa, S. bicolor, Z. mays were selected as a reference dataset. Following criteria were considered for a miRNA

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candidate: 1) The length of the predicted mature miRNAs should be 19–25 nucleotides, 2) maximum four mismatches were allowed for the predicted mature miRNAs against reference miRNA, 3) localization of the mature miRNA within stem–loop structure should be one arm,

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4) not more than 5 mismatches were allowed between miRNA sequence and the guide miRNA sequence, 5) AU content should be high and 6) minimal folding free energy (MFE) and high MFE index (MFEI) value of the secondary structure should be highly negative. The results were again validated at sequence and structure level through miRNA-dis (Liu et al., 2015). The identified miRNA candidates were used for the target identification against the same transcripts which were used for miRNA identification through psRNATarget (Dai and Zhao, 2011). Following criteria were set for the prediction of miRNA target genes: 1) not more than four mismatches were allowed between predicted mRNAs and target gene, 2) no mismatches were allowed for 10th and 11th positions of complementary site as it is considered as a cleavage site and 3) maximum 4 GU pair was allowed in the complimentary alignment.

ACCEPTED MANUSCRIPT 2.3 Interaction study of predicted miRNA and their target transcripts For understanding the interaction of predicted miRNA and their target transcripts, biological networks were constructed through Cytoscape 3.2 (Shannon et al., 2003).

2.4 Gene ontology and pathway for predicting target transcripts To understand the function of identified miRNAs and their regulating targets, the gene ontology analysis of the identified target transcript was performed by Blast2GO (Conesa and

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Götz, 2008). Furthermore to analyze the regulation of identified miRNA pathway analysis

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was performed by using KASS server (Moriya et al., 2007).

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3.1 miRNA identification and characterization

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3. Results

Twenty-three miRNA families were predicted from 8226 contigs through C-mii tool. These predicted miRNAs were considered further for reducing the duplicacy and improving the

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accuracy. The results were again validated through miRNA-dis. Out of 23 miRNA families,

Table1

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only 13 miRNA families were validated and considered for further study (Table1).

Homolog of S. hermonthica miRNA, predicted miRNA. Homolog

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Contigs

Contig8091 Contig7944 Contig2922 Contig832 Contig2694 Contig6749 Contig4047 Contig3652 Contig388 Contig267 Contig2681 Contig4481 Contig6798

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Pre dicte d miRNA family miR1846c-3p miR1848 miR1851 miR1857-5p omiR2102-3p miR2864.1 miR417 miR437 miR444e miR529a miR810b.2 miR156e miR5564b

miRNA

osa-miR1846c-3p osa-miR1848 osa-miR1851 osa-miR1857-5p osa-miR2102-3p osa-miR2864.1 osa-miR417 osa-miR437 osa-miR444e osa-miR529a osa-miR810b.2 sbi-miR156e sbi-miR5564b

Predicted miRNA sequence 5': GGCCCCCGGGCUGCUCGCUGG :3' 5': CCGUGCCCGCGCGCGCGUGCU :3' 5': CCUUUGGGAUGGCAUCUUGGA :3' 5': UGGCAUUUUUGGAGCGGGAGG :3' 5': CUCGGUGCCGGUUACGGCGGCG :3' 5': GUUUGCUGCCCUUGGUUCGGA :3' 5': UCAGGUUCAUCAACAUCGUCC :3' 5': GGUUGAAGUGAAUUUGUUUCA :3' 5': AAAGCAAUUGAAGUUUGACUU :3' 5': UUUGGAGAUGUUAUUUUGGUAA :3' 5': CUGUACCCUCUCUGGUUAUC :3' 5':AAGUGAUUUAAUUUUACCUUU:3' 5': CUGUACCCUCUCUGGUUAUC :3'

3.2 Analysis of length variation of miRNAs Variation in pre-miRNA

range of 43 to 1105 nt with an average length of 171 nt was

observed. On the other hand, the variation from 20 to 22 nt was observed in mature miRNA sequences as earlier reported (Fig. 1).

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3.3 AU and GC content study

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Fig. 1. pre-miRNA and mature miRNA sequence lengths.

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Higher AU content is an important feature to measure the difference between miRNA with other non-coding RNAs. In our results, we have also observed that predicted miRNAs

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showed AU content and GC content in range of 18.71 to 75.71 and 24.29 to 81.29 with an

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average value of 48.90 and 51.09, respectively (Fig. 2).

Fig. 2. Show predicted mature-miRNA nucleotide content, AU and GC content.

3.4 MFE and MFEI calculations

ACCEPTED MANUSCRIPT MFE (Minimum Folding Energy) is an important parameter for determining the secondary structure of pre-miRNA. The highly negative MFE indicates thermodynamically stable secondary structure of the corresponding sequences. In our analysis, the range of MFE was between 16 to 447.1 (-kcal/mol) with an average 68.32 (-kcal/mol).

MFEI (Minimum

Folding Free Enrgy Index) is considered to distinguish pre-miRNA from other coding and non coding RNA and RNA fragments. MFEI value ranged from 0.609 to 1.069 (-kcal/mol)

3.5 miRNA target identification identified

miRNA

families

were

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Fig. 3. Predicted miRNA MFE and MFEI.

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with an average of 0.68 (-kcal/mol) (Fig. 3).

considered

further

for

corresponding

target

identification. Out of 13, 12 miRNA families were found to have 185 target transcripts

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through psRNATargetScan. The targets for miR1846c could not be identified. As it is reported that miRNA have tendency to potentially regulate multiple distinct gene suggesting that these genes are subjected to combitorial control by miRNAs (Srivastava et al., 2016).

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Out of 12 miRNA families, 11 were analyzed to regulate more than one gene, except miR529a (Fig. 4).

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Fig. 4. No. of predicted miRNA target transcripts.

3.6 Interaction study, pathway and annotation for predicting target transcripts We constructed a regulatory network among 12 predicted miRNA families with their

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corresponding target transcripts (Fig. 5a). The pathway analysis of 185 target transcripts was also performed. As a result, 46 target transcripts were found to be involved in different

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pathways (Table 2). Further, we have done annotation of these 46 target transcripts which were found to be involved

in 120 molecular functions, 105 biological functions and 54

cellular components (Table 3, Supplementary 1). A regulatory network of 46 target

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transcripts involved in different function with their corresponding 12 miRNA families was constructed (Fig. 5b).

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Fig. 5. (a) Interaction of potential predicted miRNAs and their target genes, yellow triangle

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show miRNAs family and blue rectangle represents target genes and edges show UPE value. (b) light green triangle represents miRNAs and orange octagon represents corresponding

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target functions and edges represents inhibition type.

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Table 2

Functional annotation and pathway analysis of target transcripts. Targe t Acc.

UPE

Predicted target sequence

KO

pathway

Name

Inhibition type

osa-miR1848

Os07t0471000-01

22.746

GCACGCACGCGUGGGCGCGU

K08852

ERN1

Cleavage

osa-miR1848

Os01t0685850-00

22.23

GGCGCGCG-GCGCGGGCACGG

K02133

ko04141, ko04210, ko04932, ko05010 ko00190, ko05010, ko05012, ko05016

Cleavage

osa-miR1851 osa-miR1851

Os07t0178600-01 Os04t0381000-01

22.574 17.997

UCCAAGAUGCCAUUUCAAAGA UUGAAGAUGCUGUUCCAAAGG

K09419 K17065

osa-miR1857-5p osa-miR1857-5p osa-miR1857-5p osa-miR1857-5p

Os05t0134300-01 Os05t0386000-03 Os12t0123500-01 Os10t0369900-01

18.006 16.993 15.859 17.1

CUCUUGCUCCAAUAAUGUCA UCUUCUGCUCCCAAGGUGCCA CUACCGCUCCAAAGGUGCCC CUCCCGCGCCAACAAUGUCA

K20827 K20782 K14641 K01858

ko03000 ko04139, ko04214, ko04621, ko04668 ko01000 ko00514 ko00230, ko00240 ko00521, ko00562

AT PeF1B, AT P5B, AT P2 HSFF DNM1L

Cleavage T ranslation Cleavage Cleavage

osa-miR2102-3p

Os02t0599100-00

24.517

CGCUGCCGUAGCCGGUACCGUG

K11137

ko03460, ko04150

osa-miR2102-3p

Os02t0598800-00

24.517

CGCUGCCGUAGCCGGUACCGUG

K11137

ko03460, ko04150

osa-miR2102-3p osa-miR2102-3p

Os04t0186800-00 Os03t0626700-01

24.861 24.508

CGCCGCCGUCACCGGCAUCGCG CGCCGCCGUCACCGUCGUCGAG

K08176 K03327

ko02000 ko02000

osa-miR2102-3p osa-miR2864.1

Os09t0468800-00 Os08t0447000-01

21.257 19.224

CGCCGCCGUCGCCGCCGCCGAG CCCAGCCAAGGACAGCAAGC

K10664 K00058

osa-miR417 osa-miR417

Os01t0172900-00 Os03t0402000-01

13.781 14.432

UGAAACAAAUUAAAUUCAACG GGAAUUAAUUCACUUCAAUC

K01184 K20302

ko01000 ko00260, ko00680, ko01200, ko01230 ko00040 ko04131

osa-miR417 sbi,osa-miR437 sbi,osa-miR437 sbi,osa-miR437

Os01t0743200-02 Os10t0542900-01 Os01t0382000-01 Os03t0826300-00

12.42 14.879 12.576 14.168

GAAGCAAAGUCACUUUAAUU AAGGCAAAUAUCAAUUGCUUU GGUUAAAGUUAAAUUGCUUU GGUCAAUGUUCAAUUGUUUU

K01051 K20547 K13449 K15356

ko00040 ko00520, ko04016 ko04016, ko04075, ko04626 ko02000

osa-miR444e osa-miR444e osa-miR444e

Os03t0323200-03 Os02t0796500-03 Os02t0796500-02

22.384 21.817 21.817

GAGCUUGCUCUAGCAACAGCA

K03403 K14491 K14491

ko00860 ko04075 ko04075

RPAP2 HPAT APY1_2 INO1, ISYNA1 T ELO2, T EL2 T ELO2, T EL2 PHO84 T C.MAT E, SLC47A, norM, mdtK, dinF AT L6S serA, PHGDH E3.2.1.15 T RAPPC3, BET 3 E3.1.1.11 CHIB PR1 VRG4, GONST 1 chlH, bchH ARR-B ARR-B

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Pre dicte d miRNA family

GAACUUAGCUCUAGCAGCUGCA GAACUUAGCUCUAGCAGCUGCA

Cleavage Cleavage

Cleavage Cleavage Cleavage Cleavage

Cleavage T ranslation T ranslation Cleavage Cleavage Cleavage T ranslation Cleavage Cleavage Cleavage Cleavage

ACCEPTED MANUSCRIPT Os03t0711250-01 Os03t0424800-00

16.832 20.324

GAACAUAUUUCAGCAACUGCA AACAUCCUUCAGCAGCUGCA

K00888 K02966

ko00562, ko04011, ko04070 ko03010

osa-miR444e

Os03t0424500-01

22.44

AACAUCCUUCAGCAGCUGCA

K02966

ko01524, ko04016

osa-miR444e osa-miR444e osa-miR444e

Os04t0556000-02 Os04t0556000-01 Os03t0246800-03

18.3 18.3 20.626

GAUCUUGCACCAGAAACUGCA GAUCUUGCACCAGAAACUGCA AACUU-CUUCAGCAGCUGCA

K17686 K17686 K18442

ko04131 ko00520 ko00001

osa-miR529a osa-miR810b.2 osa-miR810b.2 osa-miR810b.2 sbi, zma-miR156e sbi, zma-miR156e

Os07t0632000-01 Os12t0607100-02 Os07t0658400-01 Os07t0658400-02 Os07t0681600-01 Os01t0769000-01

24.263 20.77 18.498 18.498 19.444 21.573

GACAAGCAGAGCGGGUACAG UAGGUGAAGUUGGAUCACUU GAAGGUGAACUAGAAUCACUU GAAGGUGAACUAGAAUCACUU UUGUCAGCCCUCUUCUGUCA GUACCGGCUCUCUUCUGUUG

K01183 K03242 K00284 K00284 K03654 K12617

ko00630, ko00910 ko03018 ko03018 ko01000 ko03018 ko03018

sbi, zma-miR156e sbi, zma-miR156e sbi, zma-miR156e sbi-miR5564b sbi-miR5564b sbi-miR5564b sbi-miR5564b sbi-miR5564b

Os01t0842500-01 Os06t0367100-02 Os06t0367100-01 Os10t0560900-01 Os10t0147250-00 Os02t0654400-01 Os06t0166400-01 Os04t0469500-01

19.759 20.391 20.391 23.978 24.802 23.944 20.943 17.393

UUGCUAGCUUGCUUCUGUUG CUAUCAGCUCUAUUCUGUUG CUAUCAGCUCUAUUCUGUUG UCAAGCUGUUCGACGGAAUGG GAAGGUGUUCGAGGAAAUGG CAGGCUGUUGGAGGAAGUGA CAAGGUGUUCGGCGAAAUGC CGAGCUGUUCCAGCAAAUGC

K05909 K00700 K00700 K00652 K17964 K03129 K09286 K00850

sbi-miR5564b sbi-miR5564b

Os10t0138100-01 Os01t0851700-01

22.162 17.153

CAACCUGUUCGACGAAAUGU CAAGCGGUUCAAGGAAAUGG

sbi-miR5564b

Os04t0490800-01

22.016

CAAGCUGAUCGACGGAGUGC

ko01000 ko00500 ko00500 ko00780 H00072, H01354, H01368 ko03022, ko05016, ko05168 ko03000 ko00010, ko00030, ko00051, ko00052, ko00680, ko01200, ko01230, ko03018, ko04152, ko05230 ko00906 ko00500, ko04910, ko04922, ko04931 ko00630, ko01200

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osa-miR444e osa-miR444e

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K09842 K00688 K19269

PI4KA RP-S19e, RPS19 RP-S19e, RPS19 copA, AT P7 copA, AT P7 ARFGEF, BIG E3.2.1.14 EIF2S3 E1.4.7.1 E1.4.7.1 recQ PAT L1, PAT 1 E1.10.3.2 GBE1, glgB

Cleavage Cleavage Cleavage Cleavage Cleavage Cleavage T ranslation Cleavage T ranslation T ranslation Cleavage Cleavage

bioF LRPPRC T AF4 EREBP pfkA, PFK

T ranslation T ranslation T ranslation Cleavage Cleavage T ranslation Cleavage T ranslation

AAO3 PYG, glgP

Cleavage T ranslation

PGP, PGLP

Cleavage

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UPE:Unpaired energy (UPE) required to open secondary structure around miRNA’s target site on mRNA, KO: KEGG orthology. Table 3

S. No.

miRNA

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Annotation of predicted target transcripts. No. of

Molecular functions

Biological processes

Cellular components

9

9

4

5

6

2

2

6

9

3

4

4

6

5

5

pathways miR1848

2

miR1851

3

miR1857-5p

4

miR2102-3p

5

miR2864.1

4

5

5

0

6

miR417

2

9

11

3

miR437

5

0

0

3

8

miR444e

11

15

22

14

9

miR529a

2

3

2

1

10

miR810b.2

2

7

6

0

11

miR156e

3

21

21

9

12

miR5564b

5

30

19

9

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7

7

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ACCEPTED MANUSCRIPT 4. Discussion

S. hermonthica as per recent classification has been placed in the family Orobanchaceae (Spallek et al., 2013). Which contains the highest number of parasitic species (Bennett and Mathews, 2006). S. hermonthica is well known as a hemi-parasitic weed which damages major cereal crops. It also plays an important role as a folk medicine for broad spectra of diseases (Holbrook-Smith et al., 2016; Koua et al., 2011; Okpako and Ajaiyeoba, 2004). We of this plant and

predict their role in the

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predicted and analyzed the non-coding RNAs

regulation of mRNA in host plant (Oryza sativa). No information of miRNA of S. hermontiga

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is available in miRBase database. Our study to identify miRNAs and their target sequences

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was based on homology method from EST and GSS data of S. hermonthica. For miRNA identification, we used homology identification with already reported miRNAs in Oryza

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sativa (660), Sorghum bicolor (172) and Zea mays (176). The reason for using miRNAs of crop plants was because S. hermonthica is a parasitise on these plants. In our analysis, 13 miRNA families were identified. Out of which 11 miRNA families (miR1846c-3p, miR1848,

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miR1851, miR1857-5p, miR2102-3p, miR2864.1, miR417, miR437, miR444e, miR529a, miR810b.2) have shown homology with O. sativa and two miRNA families (miR156e,

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miR5564b) with S. bicolor. The identified mature miRNAs were 20-22 nt which were in accordance with mature miRNAs of potato, soyabean, switch grass, mentha, ginger and chicory (Dehury et al., 2013; Singh and Sharma, 2014; Srivastava et al., 2016). It has been

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reported that, the stability of secondary structure of RNA molecule depends upon the minimal folding free energy (MFE). Lower value of MFE indicate the stability of the RNA molecule. The MFE value of miRNAs are reported to be lower than other RNAs. In our result, the

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average MFE of S. hermonthica miRNAs was -68.32 kcal/mol which is lower as compared to tRNA (- 27 kcal/mol) and sRNA (-33 kcal/mol) (Zhang et al., 2006). The MFE values of S. hermonthica miRNAs were found to be more negative as compared to cotton (-35.19 kcal/mol), helianthus (-35.83 kcal/mol) and ginger (−33.55 kcal/mol) (Barozai et al., 2012; Singh and Sharma, 2014; Zhang et al., 2006). Thus, the identified miRNAs in S hermionthica were significantly stable. The difference between miRNAs and other non-coding and coding RNAs also depend upon MFEI value (Srivastava et al., 2016). The identified MFEI value of pre-miRNAs in S. hermonthica have shown a significantly higher value than other RNAs (Srivastava et al., 2016). Our analysis indicated that S. hemonthica miRNAs were involved in different

biological

process,

molecular

function

and

cellular

component

(Table

3,

Supplementary 1). The identified miR1848 was observed to regulate 8 targets. Out of 8, 2

ACCEPTED MANUSCRIPT putative targets serine/threonine protein kinase domain containing protein and hypothetical protein have shown their involvement in different regulations such as protein processing in endoplasmic reticulum, apoptosis, non-alcoholic fatty liver disease (NAFLD), Alzheimer's disease, oxidative phosphorylation, Parkinson's disease and Huntington's disease. In previous studies, it was reported that miR1848 play a major role in the wax biosynthesis pathway, leaf senescence through phytohormone signaling pathways, APETALA2 (AP2), zinc finger proteins, salicylic acid-induced protein 19 (SIP19), auxin response factors (ARF) and NAC

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transcription factors, in rice (Xia et al., 2015; Xu et al., 2014). miR1851 was also predicted to regulate 8 targets. Out of 8 targets, 3 have shown similarity with heat shock transcription 29

(Fragment),

DRP3A

(DYNAMIN-RELATED

PROTEIN

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factor

3A)

and

GTP

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binding/GTPase/phosphoinositide binding protein showing the involvement in mitophagy yeast, apoptosis - fly, NOD-like receptor signaling pathway,

TNF signaling pathway. In

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earlier studies, it was reported that WRKY transcription factor 54, vacuolar-sorting receptor 6, subtilisin, MYB and scarecrow-like protein were targeted by osa-miR1851 (Xu et al., 2014). miR1857-5p was found to regulate 12 targtes. Out these 4 targets, protein of unknown

3.6.1.5)

(ATP-diphosphatase)

diphosphohydrolase)

and

similar

(adenosine

to

diphosphatase)

myo-inositol-1-phosphate

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(EC

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function DUF408 family protein, similar to predicted protein, similar to apyrase precursor (ADPase)

synthase

showed

(ATPtheir

involvement in different pathways such as O-glycan biosynthesis, purine metabolism, pyrimidine

metabolism,

streptomycin

biosynthesis

and

inositol phosphate

metabolism.

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miR1857-5p was earlier, reported to play an important role in expression of drought-tolerant up-ground tissues and down-regulated or repressed with drought treatment (Candar-Cakir et al., 2016). Thirteen targets were observed to be regulated by miR2102-3p. Out of these five

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have shown hypothetical conserved gene, telomere length regulation protein, conserved domain-domain

containing

protein,

similar

to

OSIGBa0113D21.1

protein and

Multi

antimicrobial extrusion protein MatE family protein and Similar to RING-H2 finger protein ATL5F showing their involvement in fanconi anemia pathway and mTOR signaling pathway. miR2102-3p has been reported to participate in the regulation of TCP family transcription factor containing protein (Xue et al., 2009). miR2864.1 was found to regulate 3 target transcripts. Out of these 1 target transcripts was found similar to D-3-phosphoglycerate dehydrogenase involved in glycine, serine and threonine metabolism, methane metabolism, carbon metabolism, biosynthesis of amino acids. It has been reported that FCA gene Os09g03610 controling flowering time is cleaved by osa-miR2864.1 at two different sites (Devi et al., 2016). Predicted miR417 of S. hermonthica was observed to regulate putative 7

ACCEPTED MANUSCRIPT tragets.

Out of these some targets, glycoside hydrolase, family 28 domain containing protein,

TRAPP I complex, Bet3 domain containing protein and similar to pectinesterase have shown their involvement in pentose and glucuronate interconversions. Previous studies have reported that differential expression profile of the miR417 exhibits a negative regulation over seed germination under salt stress condition (Das et al., 2015). We observed miR437 to regulated 4 putative targets. Out of which, 3 were coding for, similar to chitinase, similar to pathogenesis-related protein PRB1-2 precursor and similar to predicted protein involved in

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different pathways such as amino sugar and nucleotide sugar metabolism, MAPK signaling pathway, plant hormone signal transduction and plant-pathogen interaction. An earlier study

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reported that miR437 regulates Glu receptor proteins (Sunkar 2005). Predicted miR444e was

subunit H family protein,

expressed,

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observed to target 26 transcripts. 9 targets were coding for, similar to magnesium-chelatase B-type response regulator, cytokinin signaling,

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hypothetical gene, similar to 40S ribosomal protein S19-3, similar to 40S ribosomal protein S19-3, Similar to heavy metal ATPase, heavy metal P-type ATPase, xylem loading of copper and similar to guanine nucleotide-exchange protein GEP2. These targets have shown

their

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involvement in different pathways such as porphyrin and chlorophyll metabolism, Plant hormone signal transduction, inositol phosphate metabolism, MAPK signaling pathway -

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yeast, phosphatidylinositol signaling system, ribosome, platinum drug resistance, MAPK signaling pathway, brefeldin A-inhibited guanine nucleotide-exchange protein and membrane trafficking. Earlier, miR444e is reported to regulated MADS-box factor 27 and MADS-box Our results suggested that the predicted miR529a regulated

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factor 57 (Sunkar et al., 2008).

only 1 target, similar to xylanase inhibitor protein I precursor showed involvement in amino sugar and nucleotide sugar metabolism. Earlier studies showed the involvement of miR529a

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in the regulation of drought tolerance in rice (Zhou et al., 2010). miR810b.2 was observed to target 12 transcripts. Out of which 3 have shown similar to eukaryotic translation initiation factor 2 gamma subunit, Similar to ferredoxin-dependent glutamate synthase and similar to Fd-GOGAT protein (Fragment). These targets were found to involve in RNA transport, glyoxylate and dicarboxylate metabolism and nitrogen metabolism. In previous studies, it is reported that miR810b.2 was involved in DNA polymerase alpha catalytic subunit (Cheah et al., 2015). miR156e was target shown to 9 transcript sequences coding for DNA helicase, ATP-dependent, RecQ type domain containing protein, topoisomerase II-associated protein PAT1 domain containing protein, similar to laccase (EC 1.10.3.2), similar to starch branching enzyme III

and glycoside hydrolase, subgroup, catalytic core domain containing protein.

These targets were found to be involved in pathways such as RNA degradation and starch

ACCEPTED MANUSCRIPT and sucrose metabolism. In previous studies miR156e was reported to be involved in overexpression of production of a bushy mutant and regulation of strigolactones (SLs) pathway (Chen et al., 2015). miR5564b regulated 82 different targets. Out of these 8 targets coded for pyridoxal phosphate-dependent transferase, major region, subdomain 1 domain containing

protein,

hypothetical conserved

gene,

transcription initiation factor TFIID

component TAF4 domain containing protein, similar to TINY-like protein (AP2 domain containing protein RAP2.10) (Fragment), similar to OSIGBa0124N08.2 protein, similar to

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aldehyde oxidase-2, similar to cytosolic starch phosphorylase (Fragment) and haloacid dehalogenase-like hydrolase domain containing protein. miR5564b was reported to be

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involved in biotin metabolism, pyruvate dehydrogenase complex deficiency, leigh syndrome,

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cytochrome c oxidase (COX) deficiency, basal transcription factors, huntington's disease, herpes simplex infection, glycolysis/gluconeogenesis, pentose phosphate pathway, fructose

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and mannose metabolism, galactose metabolism, methane metabolism, carbon metabolism, biosynthesis of amino acids, RNA degradation, AMPK signaling pathway, central carbon metabolism in cancer, carotenoid biosynthesis, starch and sucrose metabolism, insulin pathway,

glucagon

signaling

pathway,

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signaling

insulin

resistance,

glyoxylate

and

dicarboxylate metabolism and carbon metabolism. Previous studies have been reported that

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miR5564b was involved in stress tolerance in Sorghum bicolor (Zhang et al., 2011).

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5. Conclusion

Computational approach for small RNA identification plays an important role to study mechanisms of miRNAs and their targets playing important regulatory functions. The

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identification of S. hermonthica miRNAs and their predicted target genes adds to our understanding

the molecular mechanisms of RNA communication and exchange between

both parasitic and host plants. The identified miRNAs and their putative targets may lead to an improved

understanding of the molecular mechanism allowing S. hermonthica to

parasitize host plants We reported 13 conserved miRNA families and 12 miRNA families were found to have function in different pathways. It was overall observed that predicted miRNA was mainly involved in serine family amino acid biosynthetic process, signal transduction,

cytokinin-activated

signaling pathway, metal ion transport, abscisic acid

biosynthetic process, xanthine catabolic process, auxin biosynthetic processresponse to water deprivation etc. These 12 miRNAs of

S. hermonthica may structurally mimic host plants

miRNAs and hamper the regulation of host plant mRNA involved in growth, development

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defense

system.

Computational methods of miRNA prediction combined

with

transcriptomics detail of organism on non model organism, could be of help to assess whether parasitic plants have evolved mechanisms to exchange

molecules to their advantage with

their host plants (Ichihashi et al., 2015; Weiberg et al., 2015, Knip et al., 2014).

Author contribution statement AS and SS designed and conceptualized the research, SS preformed the

identification,

PT

analysis, assembly, annotation and wrote the manuscript. AS critically revised the article. All

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authors read and approved the manuscript.

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Acknowledgements We would like to thank EST database of NCBI for making the data freely available to the scientific community. Financial assistance under BTISnet programme

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of DBT, New Delhi is gratefully acknowledged.

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ACCEPTED MANUSCRIPT Abbrevations: miRNA: MicroRNA, MFE: Minimal Folding Energy, MFEI: Minimal Folding Index, GO: Gene Ontology,

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EST: Expressed Sequence Tag,

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GSS: Genome Survey Sequence

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Graphical abstract

ACCEPTED MANUSCRIPT Highlights

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

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

Mobile small RNAs play an important role in regulation of trans- kingdom genes. Thirteen conserved miRNAs identified in Striga hermonthica. These thirteen miRNAs showed homology with their host plants Oryza sativa and Sorghum bicolor. Out of thirteen miRNAs, 12 miRNAs predicted to regulate 185 target mRNA of Oryza sativa. Predicted miRNA of S. hermonthica might be used as a tool for protection of cereal crops damage.

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