Identification of miRNA encoded by Jatropha curcas from EST and GSS

Abstract

miRNAs are endogenous approx 22 nucleotide RNA which mediates transcriptional or Post-transcriptional gene regulation and play a critical role in diverse aspects of plant development. miRNA identification in wet lab have various constraints, it is time consuming and expensive. It also faces the limitation of identifying miRNAs expressed at specific time and/or at special conditions. Due to the nature of strong conservation of miRNA in plant species, the use of comparative genomics approach for expressed sequence tags (ESTs), Genome Survey Sequence (GSS) and structural feature criteria filter has paved the way toward the identification of conserved miRNAs from the plant species whose genomes are not yet available in public domain. To identify the novel miRNA from Jatropha curcas, a total of 46862 EST sequences and 1569 GSS were searched for homology to previously known viridiplantae 2502 mature miRNA. After predicting the RNA secondary structure, 24 new potential miRNA were identified in J. curcas. Using the newly identified miRNA sequences, a total of 78 potential target genes were identified for 3 miRNA families. Most of the miRNA targeted genes were predicted to encode transcription factors that regulate cell growth and development, signaling, and metabolism. These findings considerably broaden the scope of understanding the functions of miRNA in J. curcas.

Introduction

The discovery of miRNA played a very important role in the field of biological research.^1,2 The first Plant miRNAs were identified after a long time in early 2002 from Arabidopsis thaliana.³ Since then thousands of plant miRNA have been submitted in miRBase Sequence database. (http://www.mirbase.org).⁴ miRNAs are endogenous approx 21–24 nucleotide RNA which mediates transcriptional or post-transcriptional gene regulation and play a critical role in diverse aspects of plant development including auxin signaling, meristem boundary formation, organ separation, leaf development and polarity, lateral root formation, transition from juvenile to adult vegetative phase and from vegetative to flowering phase, floral organ identity and reproduction, as well as adaptation to biotic and abiotic stresses, including nutrient deprivation.⁵

Plant miRNAs are primarily found in genomic regions not associated with protein-coding genes and most of the plant miRNAs are produced from their own transcriptional units. miRNA genes are transcribed by RNA polymerase II (pol II). The primary miRNA transcripts (pri-miRNAs) contain cap structures as well as poly(A) tails.⁶ Mature miRNAs are produced from longer RNA hairpin precursors by the endoribonuclease III-like enzyme, dicer like-1 (DCL1). The processed and methylated miRNA/miRNA* duplex is exported to the cytosol via HASTY5, a plant ortholog of exportin.⁷ miRNAs that are incorporated into an argonaute containing RNA-induced silencing complex can affect the target gene expression. miRNA mediated regulations rely on specific miRNA target mRNA interactions that result in degradation of the target transcript and/or attenuation of translation.^8,9

Although hundreds of miRNA have been discovered in recent years in plants many other miRNA gene functions remain to be elucidated. For the discovery of novel microRNA in different species four different methods are used.¹⁰ These are (1) genetic screening; (2) direct cloning through constructing a small RNA library; (3) traditional computational approach based on whole genome sequences; and (4) EST analysis.

Genetics screening technology is the first method for discovery of microRNAs.² This technology requires the isolation of total RNA and sequencing each RNA and therefore it is time-consuming, complicated and expensive, and hence this method is not much practiced.¹⁰

In Direct cloning technology after isolation of small RNAs, the RNAs are separated by gel electrophoresis. Usually, a 5′ and/or 3′ adaptor is added to the small RNA for easily operating the small RNAs. Finally, the small RNAs are sequenced and the miRNAs are identified from the isolated small RNAs. This method has been widely employed to identify miRNAs from different species in plants and animals.^11,12 The major limitation of this method that it is time-consuming and and expensive. This method also fails to identify miRNAs expressed at specific time and/or at special conditions.

In the case of availability of whole genome sequence of organism large number of computational approaches has been designed to search miRNA.^13,14 The unavailability of whole genome sequences played a great limitation in this strategy of the miRNA identification.

EST analysis is a powerful tool to identify miRNAs conserved among various plant species whose whole genome sequences are not available, and to study the conservation and evolution of miRNAs among different species.^15,16 EST analysis strategy have been proven to be successful for the discovery of new miRNAs from various plant species including Cotton,¹⁷ Tea,¹⁸ Oil palm,¹⁹ Radish,²⁰ Potato,²¹ Citrus,²² rapeseed,²³ Lettuce,²⁴ Soybean,²⁵ Wheat,²⁶ Tobacco,²⁷ Groundsel,²⁸ and Cowpea.²⁹ Thus, the computational or bioinformatics based approach is very useful for predicting novel miRNAs, which usually cannot be detected by the direct cloning.

Just like EST analysis, one more nucleotide database, GSS is also used in miRNA identification field¹⁶ and by this strategy, many miRNAs in several plant species are identified.^30,21,31,27

Due to the excessive use of non-renewable hydrocarbons worldwide as energy source resulted it in fast depletion of reserves and threat of global warming, which the researcher made to search for renewable sources.³² Plant based biodiesel derived from the seed oil of J. curcas is now emerging as an available alternative to the conventional fossil fuel. J. curcas is a Latin American origin hardy perennial shrub, which is widespread throughout the tropical regions of the world. Jatropha is a large genus comprising more than 175 species among except for J. curcas and J. glandulifera, which are oil-yielding species, most of remaining is ornamental.³³ Apart from being a potential biofuel crop J. curcas is multipurpose species with many attributes and considerable potential.

Thousand of microRNAs have been discovered in recent years but there have been only 46 novel miRNAs that have been reported for the Jatropha by the cloning approach.³⁴ Since during the cloning approach, there is a chance of degradation of miRNAs and skipping of loosely expressed miRNAs. Therefore, in the present study, we have used all known plant miRNAs (publicly available in miRBase) from viridiplantae to search the conserved J. curcas miRNA homologs in publicly available EST and GSS databases.

Results

A total of 20 four potential miRNAs were detected with predicted stem-loop precursor structure from the publically available EST and GSS database.

Identification of Potential J. curcas miRNAs

The evidence of evolutionary conservancy among plant kingdom provides a powerful tool to predict the new miRNAs in plant species.³⁵ After the removal of repeated miRNA sequences from the viridiplantae group the remaining 2502 miRNAs were locally BLAST against 46862 EST and 1569 GSS sequences of J. curcas respectively by setting the value of 1e-3 and the window size 7. The BLAST result detected the homology of viridiplantae mature miRNA with the 83 and 11 sequences of Jatropha EST and GSS respectively.

Due to the high degree of sequence identity within families of mature miRNAs of plants, this number was reduced to a combined total of 48 candidate Jatropha miRNA sequences, which were further analyzed for their predicted secondary structure properties. A total of 20 from EST and 4 from GSS potential J. curcas miRNA which share a high degree of sequence identity with known mature miRNA and which fulfilled our criteria for miRNA encoding sequences, are listed in Table 1 and their predicted secondary structures are shown in Figure 1A, Fig. 1B and Fig. 1C.

Table 1. Details of the predicted miRNAs from EST.

miRNAs	Mature sequence	EST	Loc	NM	LM	LP	G+C	MFE	MFEi
jcu-miR166a	UCGGACCAGGCUUCAUUCCCC	GW880030	3′	1	21	146	39.04	172.4	3.024646
jcu-miR166b	UCGGACCAGGCUUCAUUCCCC	GW880030	3′	2	21	146	39.04	172.4	3.024646
jcu-miR166c	UCGGACCAGGCUUCAUUCCCC	GW880030	3′	0	21	146	39.04	172.4	3.024646
jcu-miR166d	GUCGGACCAGGCUUCAUUCCC	GW880030	3′	2	21	144	38.19	172.4	3.13491
jcu-miR166e	GGAAUGUUGUCUGGCUCGAGGA	GW880030	5′	0	22	142	37.32	172.4	3.253174
jcu-miR166f	CGUCGGACCAGGCUUCAUUCC	GW880030	3′	2	21	142	37.32	172.4	3.253174
jcu-miR166 g	UCGGACCAGGCUUCAUUCCCC	GW880030	3′	1	21	146	39.04	172.4	3.024646
jcu-miR166h	UCGGACCAGGCUUCAUUCCC	GW880030	3′	0	21	144	38.19	172.4	3.13491
jcu-miR167a	UGAAGCUGCCAGCAUGAUCUG	GW879969	5′	1	21	90	44.44	76.30	1.907691
jcu-miR167b	UGAAGCUGCCAGCAUGAUCUU	GW881255	5′	1	21	68	45.59	129.3	4.170806
jcu-miR167c	UGAAGCUGCCAGCAUGAUCUGA	GW879969	5′	1	22	89	43.82	76.30	1.95642
jcu-miR167d	UGAAGCUGCCAGCAUGAUCUUA	GW881255	5′	2	22	68	45.59	129.3	4.170806
jcu-miR167e	AAGCUGCCAGCAUGAUCUUA	GW881255	5′	1	20	64	45.31	129.3	4.458867
jcu-miR167f	UGAAGCUGCCAGCAUGAUCUU	GW881255	5′	0	21	67	46.27	129.3	4.170847
jcu-miR167 g	UGAAGCUGCCAGCAUGAUCUG	GW879969	5′	1	21	90	44.44	76.30	1.907691
jcu-miR167h	AUAUCAUGUGGCAGUUUCACC	GW879969	3′	1	21	93	45.16	76.30	1.816719
jcu-miR167i	UGAAGCUGCCAGCAUGAUCUU	GW881255	5′	1	21	67	46.27	129.3	4.170847
jcu-miR1096	CUGCCUCUUUGCUUCAGGACU	GT980456	3′	2	21	356	42.13	201.94	1.346421
jcu-miR5368a	GGACAGUCUCAGGUAGACA	FM889530	3′	0	19	166	57.23	163.2	1.717862
jcu-miR5368b	GGACAGUCUCAGGUAGACA	GT975674	3′	0	19	166	57.23	83.60	0.879983

Figure 1A. Newly identified miRNAs in J. curcas alongwith mature and precursor sequences and the predicted stem loop structure. The actual size of the precursors may be slightly shorter or longer than the one shown in the figure.

Figure 1B.

Newly identified miRNAs in J. curcas alongwith mature and precursor sequences and the predicted stem loop structure. The actual size of the precursors may be slightly shorter or longer than the one shown in the figure.

Figure 1C.

Newly identified miRNAs in J. curcas alongwith mature and precursor sequences and the predicted stem loop structure. The actual size of the precursors may be slightly shorter or longer than the one shown in the figure.

These miRNA gene candidates are predicted to encode Jatropha members of the jcu-miR166, jcu-miR167, jcu-miR1096, jcu-miR5368, jcu-miR5021 family. The identified potential miRNAs have both higher negative minimal fold energies (MFEs) (71.1–201.94) and MFEIs (0.87–4.458867) (Table 1). We have chosen only those miRNA candidates which shows the value of MFEI greater than 0.85, which is a commonly used value to distinguish miRNAs from other non-coding and coding RNAs.³⁶

Total 24 predicted miRNAs belongs to 5 different miRNA families in Jatropha as shown in Figure 2. miRNA family miR166, miR167, miR1096, miR5368 and miR5021 have 9, 9, 1, 2 and 3 members respectively. The different size of the identified miRNAs within different families suggests that they may offer unique functions for regulation of miRNA biogenesis or gene expression.¹⁶ Predicted miRNAs also depicts the diversity in the location of mature miRNA sequences. The sequences of miR166e, miR167a/b/c/d/e/f/g/h/i and miR5021a/b/c were located at the 5′ end of the miRNA precursors, while the others were found at the 3′ end.

Figure 2. (A) The newly identified miRNAs with the different number and (B) miRNA family with its number of miRNA:

Target Prediction

miRNAs regulates expression of specific gene via hybridization to mRNA transcripts to promote RNA degradation, inhibit translation or both.³⁷ For the better understanding of the biological functions of the newly identified Jatropha miRNAs, we have searched for putative target genes using the psRNATarget program with default parameters (http://plantgrn.noble.org/) against the Arabidopsis thaliana DFCI Gene Index (AGI) Release 15.³⁸ We have adjusted expectation value 2.0 for lower false positive prediction.³⁹ A total of 78 potential targets were identified for the 3 predicted miRNA families which include 21 miRNAs based on their perfect or nearly perfect complementarity with their target sequences in Arabidopsis (Table 2). The two miRNA families miR1096 and miR5368 contains 1 and 3 miRNAs does not shows any complementarity with the model plants.

Table 2. Details of the predicted miRNAs from GSS.

miRNAs	Mature sequence	GSS	Loc	NM	LM	LP	G+C	MFE	MFEi
jcu-miR166i	UCGGACCAGGCUUCAUUCCCG	JM428509	5′	0	21	146	39.0	100.	1.75443
jcu-miR5021a	AGAGAAGAAGAAGAAGAAAG	JM428710	5′	2	20	68	45.6	84.4	2.72247
jcu-miR5021b	AGAGAAGAAGAAGAAGAAAC	JM428982	5′	2	20	89	43.8	80.3	2.05898
Jcu-miR5021c	AAAGAAGAAGAAGAAGAAAG	HN339391	5′	3	20	68	45.6	71.1	2.29346

NM: number of mismatch; LM: length of mature miRNAs; LP: length of precursor; MFEs: minimal folding free energies; MFEIs: minimal folding free energy indexes.

These potential miRNA targets were belonged to a number of gene families that involved in different biological functions such as regulation of metabolism, transcription factor, hormone biosynthesis, development and in oil synthesis (Table 3). The miRNA family ‘miR5021’ showed the highest 51 numbers of independent target genes followed by ‘miR166’ family with 21 numbers of target genes and miR167 with 6 targets. The rest miRNA families miR1096 and miR5368 does not report any target in the model organism Arabidopsis thaliana within our filtration strategy.

Table 3. Predicted miRNA targets of Identified miRNAs.

miRNA Acc.	Target Acc.	(E)	Target Description	Target Function
Jcu-miR5021c	TC372725	0.0	hydrolase, acting on glycosyl bonds	Metabolism
Jcu-miR5021c	TC360345	1.0	protein kinase	Metabolism
Jcu-miR5021c	TC361668	1.0	F1N19.20	Metabolism
Jcu-miR5021c	TC399898	1.0	transferase, transferring pentosyl groups;	Metabolism
Jcu-miR5021c	TC358199	1.0	E3 ubiquitin-protein ligase	Metabolism
Jcu-miR5021c	TC359005	1.0	E3 ubiquitin-protein ligase UPL3	Metabolism
Jcu-miR5021c	TC369235	1.0	Heparanase-like protein 1 precursor;	Metabolism
Jcu-miR5021c	TC364464	1.0	T8K14.20 protein	Biofuel
Jcu-miR5021c	TC382579	1.0	E3 ubiquitin-protein ligase UPL3	Metabolism
Jcu-miR5021c	TC358567	1.0	phytochrome A supressor spa1	Metabolism
Jcu-miR5021c	NP1660479	1.0	ceramidase family protein	Biofuel
Jcu-miR5021c	TC402600	1.0	ceramidase family protein	Biofuel
Jcu-miR5021c	TC403684	1.0	Receptor-kinase isolog	Metabolism
Jcu-miR5021c	TC361202	1.0	ARP protein	Constitutive and alternative splicing
Jcu-miR5021c	TC369816	1.0	Calmodulin-binding transcription activator	Signal transduction
jcu-miR166a	TC359261	2.0	homeodomain transcription factor	Transcription Factor
jcu-miR166a	TC392453	2.0	Class III HD-Zip protein	Transcription Factor
jcu-miR166a	TC393398	2.0	HD-Zip protein	Transcription Factor
jcu-miR166b	TC359261	2.0	homeodomainn transcription factor	Transcription Factor
jcu-miR166b	TC392453	2.0	Class III HD-Zip protein 3;	Transcription Factor
jcu-miR166b	TC393398	2.0	HD-Zip protein	Transcription Factor
jcu-miR166c	TC359261	2.0	homeodomain transcription factor	Transcription Factor
jcu-miR166c	TC392453	2.0	Class III HD-Zip protein 3	Transcription Factor
jcu-miR166c	TC393398	2.0	HD-Zip protein	Transcription Factor
jcu-miR166d	TC359261	2.0	homeodomain transcription factor	Transcription Factor
jcu-miR166d	TC392453	2.0	Class III HD-Zip protein 3	Transcription Factor
jcu-miR166d	TC393398	2.0	HD-Zip protein	Transcription Factor
jcu-miR166 g	TC359261	2.0	homeodomain transcription factor	Transcription Factor
jcu-miR166 g	TC392453	2.0	Class III HD-Zip protein 3)	Transcription Factor
jcu-miR166 g	TC393398	2.0	HD-Zip protein	Transcription Factor
jcu-miR166h	TC359261	2.0	homeodomain transcription factor	Transcription Factor
jcu-miR166h	TC392453	2.0	Class III HD-Zip protein 3	Transcription Factor
jcu-miR166h	TC393398	2.0	HD-Zip protein	Transcription Factor
jcu-miR166i	TC359261	2.0	homeodomain transcription factor	Transcription Factor
cu-miR166i	TC392453	2.0	Class III HD-Zip protein 3	Transcription Factor
jcu-miR166i	TC393398	2.0	HD-Zip protein	Transcription Factor
jcu-miR167e	TC359604	1.5	ARF2 [Arabidopsis thaliana]	Hormone Biosynthesis
jcu-miR167e	NP230219	2.0	auxin responsive transcription factor	Transcription Factor
jcu-miR167e	TC360610	2.0	Auxin response factor 8	Hormone Biosynthesis
jcu-miR167e	TC385699	2.0	Auxin response factor 8	Hormone Biosynthesis
jcu-miR167h	TC361447	2.0	CDPK-related protein kinase	Metabolism
jcu-miR167h	TC386736	2.0	CDPK-related protein kinase	Metabolism
jcu-miR5021a	NP1652463	0.0	NADK1 (NAD kinase 1)	Metabolism
jcu-miR5021a	BX839123	0.0	NAD(H) kinase 1	Metabolism
jcu-miR5021a	TC363441	0.0	NAD(H) kinase 1	Metabolism
jcu-miR5021a	TC379625	0.0	Cytotoxic protein ccdB	Cytotoxic
jcu-miR5021a	TC362765	0.0	Diacylglycerol O-acyltransferase	Metabolism
jcu-miR5021a	TC370375	0.0	T1F15.13 protein	Metabolism
jcu-miR5021a	TC358471	0.5	Copia-type polypotein	Metabolism
jcu-miR5021a	TC363769	0.5	Copia-type polyprotein	Metabolism
jcu-miR5021a	TC372725	0.5	hydrolase, acting on glycosyl bonds	Metabolism
jcu-miR5021a	TC372762	0.5	At1g49510 [Arabidopsis thaliana]	Development
jcu-miR5021a	TC369912	0.5	MAP3K-like protein kinase	Metabolism
jcu-miR5021a	TC363616	1.0	Fucosyltransferase 3	Metabolism
jcu-miR5021a	TC386702	1.0	pantothenate kinase-related	Metabolism
jcu-miR5021a	NP2693135	1.0	zeaxanthin epoxidase	Hormone Biosynthesis
jcu-miR5021a	TC361228	1.0	AtABA1 protein	Hormone Biosynthesis
jcu-miR5021a	TC361686	1.0	EMBRYO DEFECTIVE 2738; GTP binding	Development
jcu-miR5021a	TC359684	1.0	At2g41900/T6D20.20	Transcription Factor
jcu-miR5021a	TC390065	1.0	(Homeobox-1); transcription factor	Transcription Factor
jcu-miR5021a	TC358199	1.5	E3 ubiquitin-protein ligase UPL3	Metabolism
jcu-miR5021a	TC382579	1.5	E3 ubiquitin-protein ligase UPL3	Metabolism
cu-miR5021b	BX837986	0.0	HB-1 (homeobox-1); transcription factor	Transcription Factor
miR5021b	TC390065	0.0	HB-1 (homeobox-1); transcription factor	Transcription Factor
jcu-miR5021b	NP1652463	1.0	NP_188744.3 NADK1 (NAD kinase 1)	Metabolism
jcu-miR5021b	TC363441	1.0	NAD(H) kinase 1	Metabolism
jcu-miR5021b	TC362765	1.0	Diacylglycerol O-acyltransferase	Metabolism
jcu-miR5021b	TC370375	1.0	T1F15.13 protein	Growth
jcu-miR5021b	TC368480	1.0	Br FatA1	Biofuel
jcu-miR5021b	TC366757	1.0	IAA-amino acid hydrolase ILR1-like 4 precursor	Hormone Biosynthesis
jcu-miR5021b	NP2693135	1.0	ABA DEFICIENT 1); zeaxanthin epoxidase	Hormone Biosynthesis
jcu-miR5021b	TC361228	1.0	AtABA1 protein	Hormone Biosynthesis
jcu-miR5021b	TC361686	1.0	EMBRYO DEFECTIVE 2738); GTP binding	Development
jcu-miR5021b	TC359684	1.0	At2g41900/T6D20.20	Transcription Factor
jcu-miR5021b	TC369912	1.0	MAP3K-like protein kinase	Metabolism
jcu-miR5021b	TC358471	1.5	Copia-type polyprotein	Metabolism
jcu-miR5021b	TC363769	1.5	Copia-type polyprotein	Metabolism
jcu-miR5021b	TC359534	1.5	replication factor C large subunit-like protein	Transcription Factor

Phylogenetic Analysis of Predicted miRNAs

Plant miRNAs shows highly conserved nature between distantly related plant species, both at the level of pri-miRNA and mature miRNAs.16 Multiple sequence alignment of the precursor sequences of the predicted miRNAs with other members of the same family showed the high degree of sequence similarity with others. for example, the precursor sequence similarity between jcu-MIR166 and other MIR167 members was over 60%.(Data not shown) Based on the pre-miRNA sequence comparisons, the evolutionary relationships of J. curcas miRNAs with other members from the same families were analyzed using the ClustalW. It could be seen from the phylogenetic trees that in different families, the evolutionary relationships of J. curcas miRNAs with other species were different; for example, in MIR166 family, jcu-MIR166 and mes-MIR166 were on the same branch (Fig. 3A). Also, it could be seen that different J. curcas miRNA members in the same family were often distantly related (Fig. 3B). These results suggested that different miRNAs might evolve at different rates not only within the same plant species, but also in different ones. Three of five novel miRNAs, namely jcu-miR1096, jcu-miR5368 and jcu-miR5021, for which their homologous miRNAs were not found, showed an unrelated evolutionary relationship with other miRNAs.

Figure 3. Phylogenetic analysis of pre-miRNAs sequences in different families. (A) MIR166 (B) MIR 167

Discussion

Most mature miRNAs are evolutionarily conserved from species to species within the plant kingdom. Therefore, we have used all the previously known plant mature miRNAs from miR registry to search for homologs of miRNAs of J. curcas in the publicly available EST database. By computational predictions, we found 20 four miRNAs belonging to five miRNA families. Formation of stem-loop hairpin secondary structure is a critical step in miRNA maturation and is one of the most important characteristics of pre-miRNAs. However, a stem-loop hairpin structure is not a unique feature of miRNAs, because other RNAs (such as mRNA, rRNA, and tRNA) can also form similar hairpin structures. To avoid designating other RNAs or RNA fragments as new miRNAs, several labs have established uniform systems for annotating new miRNAs. Three criterias, such as negative minimal fold energy (MFE), adjusted minimal fold energy (AMFE) and the minimal fold energy index (MFEI) have been proposed. It is indicated that most miRNA precursors identified have an MFEI greater than 0.85, a commonly used value to distinguish miRNAs from other non-coding and coding RNAs. MFE is an important characteristic that determines the secondary structure of nucleic acids (DNA and RNA). Lower the value of the MFE, the higher the thermodynamically stable secondary structure of the corresponding sequence.⁴⁰ All the mature sequences of Jatropha miRNAs are in the stem portion of the hairpin structures, as shown in Figure 1.

To understand the biological function of miRNAs in plant development, it is necessary to identify their targets. No high-throughput experimental techniques for target site identification have been reported yet. Two strategies have been employed toward this end: (1) genetic approach, which is based on the abnormal expression of target mRNAs in the miRNA loss-of-function mutants, and (2) computational approaches, which have been successful in plants.

It is noted that most of the predicted targets were the genes coding transcription factors, which are mainly involved in plant growth, developmental patterning or cell differentiation. Probably, this is a general characteristic of plant miRNAs that tend to be complementary to their regulatory targets. In some cases, a miRNA can be complementary to more than one regulatory target. In this case, the targets can be grouped into several gene families. The different mRNA targets can be separated into several groups. The first group contains targets that are predicted to encode transcription factors and second group contains targets encoding a range of different enzymes and proteins, which plays role in plant metabolism. Another group of miRNA targets encode proteins whose functions are largely unclear. Different enzymes coding genes are also target of miRNAs in plant like, lipid syntheisis, protein kinase-like protein involve in protein serine/threonine kinase activity. Although the present research has shown a very good results and which may be useful for understanding of better role of miRNAs in plant gene regulation. But due to the unavailability of the full length of genome of the studied plant the target of two miRNA families namely miR1096 and miR5368 couldn’t predicted.

In this paper, with a comparative genomics approach, 24 miRNAs were identified from the EST and GSS databases of J. curcas. The findings from this study will contribute to further researches of miRNAs function and regulatory mechanisms.

Materials and Methods

miRNA reference data set

To search potential miRNAs, the initial miRNA data set has been retrieved from the previously deposited 4677 miRNA of the group viridiplantae from the publically available miRNA database miRBase, version 18.0.⁴¹ To avoid the overlapping of miRNAs, the repeated sequences of miRNAs were removed with the Jalview program with threshold value to 100.⁴² The size of the data set has been subsequently reduced to 2502 non-redundant sequences. These miRNAs were defined as a reference set of miRNA sequences.

Bioinformatics tools

Local similarity searches were performed by Blast-2.2.26+ program downloaded from the NCBI ftp site (ftp://ftp.ncbi.nih.gov/). miRNA precursor folding was performed by mFOLD Web server (version 2.3).⁴³

Jatropha EST and GSS

Total 46862 EST and 1569 GSS(Genomic Survey Sequences) of J. curcas were retrieved from the EST and GSS division of GenBank nucleotide database respectively.⁴⁴ and all of these sequences were screened against the known plant miRNAs.

Prediction of potential miRNAs

There are two important parameters to identify miRNA sequence from the EST and GSS analysis; first is conservation of sequences, and the second is the hairpin stem-loop structure of the potential pre-miRNAs (Fig. 4).

Figure 4. Depiction of the steps followed to search for potential miRNAs in J. curcas.

The known plant miRNA sequences were subjected to the BLAST search for Jatropha homologs of miRNAs against EST and GSS databases. The initial BLAST search was performed with the program of BLAST-2.2.26+, by adjusting the BLASTN parameter settings as: expect values at 1e-3; low complexity was chosen as the sequence filter; the number of descriptions and alignments was raised to 1,000. The default word-match size between the query and database sequences was 7. To be the potent miRNA candidate, the RNA sequences should follow the given criteria:

(1) The candidate miRNA should contain at least 18 nt length and does not include any gap.

(2) Up to 0–2 nt mismatches in sequence with all previously known plant mature miRNAs were allowed.

The ESTs that closely matched the previously known plant mature miRNAs were included in the set of miRNA candidates and used for additional characterization based on the following criteria:

(1) The entire EST sequence was selected to predict the secondary structures and to screen for miRNA precursor sequences (2) The selected ESTs were further compared with each other to eliminate redundancies (3) These precursor sequences were used for BLASTX analysis for removing the protein-coding sequences and retained only the non-protein sequences.

Prediction of secondary structure

Precursor sequences of these potential miRNA homolog’s were used for secondary structure predictions using the Zuker folding algorithm with MFOLD 3.1,⁴³ which is publicly available at www.bioinfo.rpi.edu/applications/mfold/old/rna/. The following parameters were used in predicting the secondary structures:

(1) Linear RNA sequence; (2) Folding temperatures fixed at 37°C; ionic conditions of 1M NaCl and with no divalent ions; (3) Percent suboptimility number of 5; (4) Maximum interior/bulge loop size of 30; (5) The grid lines in energy dot plot turned on.

All other parameters were set with default values.

Following criteria were used to choose the candidate miRNA as described by Zhang et al.:¹⁵

(1) pre-miRNA sequence can fold into an appropriate stem-loop hairpin secondary structure

(2) it contains ~22 nt mature miRNA sequence within one arm of the hairpin

(3) an MFEI of greater than 0.85³⁶

(4) 30–70% A+U content(5) Predicted mature miRNAs had no more than six mismatches with the opposite miRNA* sequence in the other arm (6) Maximum size of 3 nt for a bulge in the miRNA sequence; and (7) No loop or break in miRNA sequences was allowed. These criteria significantly reduced false positives and required that the predicted miRNAs fit the criteria.⁴⁵

ΔG values (kcal/mol) of stem-loop structures generated by MFOLD program were applied to calculate their negative minimal free energies (MFEs), which is directly correlated with the sequence length, to normalize the potential effect of sequence length on MFE and to differentiate miRNAs from other RNAs, we used two energy measurements namely adjusted minimal folding energy (AMFE) and minimal folding free energy index (MFEI). AMFE is defined as the MFE of a 100 nucleotide length.

$$AMFE=\frac{MFE}{\text{Length of precursor sequence (LP)}}\times 100$$

The minimal folding free energy index (MFEI) for each sequence was calculated as described by Zhang et al.³⁶

$$MFEI=\frac{AMFE}{\text{(G}+\text{C)\%}}$$

Prediction of potential targets of miRNAs

The perfect or near-perfect complementary of miRNAs and their target mRNAs in plants has greatly simplified the identification of miRNA targets. In this study, we applied this strategy to search for the targets of identified miRNAs by homology algorithm. As for J. curcas, since only few gene sequences are available, we have used Arabidopsis as a reference system for finding the targets of the candidate miRNAs. The predicted J. curcas miRNAs were used as query against the Arabidopsis thaliana by using psRNA Target: A Plant small RNA Target Analysis Server to predict the targets of miRNA. http://plantgrn.noble.org/psRNATarget/

It should follow given criteria:-

1) Range of central mismatch for translational inhibition 9–11 nucleotide;

2) Maximum expectation value 3

3) Maximum mismatches at the complementary site ≤ 4 without any gaps.

4) Multiplicity of target sites 2

By the parameter set as with default parameters; maximum expectation:2.0, length for complementarity scoring (hspsize): 20, target accessibility-allowed maximum energy to unpair the target site (UPE): 25.0, Flanking length around target site for target accessibility analysis: 17 bp in upstream and 13 bp in downstream, Range of central mismatch leading to translation inhibition: 9–11nt. The methodology of potential target prediction of predicted miRNA is shown as in Figure 5.

Figure 5. Procedure of potential target search by psRNATarget Server. (http://plantgrn.noble.org/psRNATarget/)

Phylogenetic analysis

A homology search of predicted pre-miRNA was done against all plant miRNAs using NCBI stand-alone BLAST allowing maximum of 3 mismatches and e-value < 0.001. The corresponding precursor sequences of homolog pre-miRNA’s were identified and collected. The collected sequences of diverse plant species were aligned with homolog jatropha miRNA using Clustal W. A query of jatropha pre-miRNA against known miRNA families allowed us to identify 2 previously reported large families. The precursor sequences of two known family members were selected along with respective precursor sequences of J. curcas. Then, the Neighbor-joining trees were constructed for each family based on Kimura 2-parameter model with using MEGA 5.1 to illustrate the evolutionary relationships among the members of the family.

Nomenclature of miRNAs

The nomenclature of predicted miRNAs were adopted in accordance with miRBase.⁴ The mature sequences are designated ‘MIR’, and the precursor hairpins are labeled as ‘mir’ with the prefix ‘jcu’ for J. curcas.

Glossary

Abbreviations:

ncRNA

Non coding RNA

miRNA

microRNA

Pre-miRNA

Precursor microRNA

miRNA*

passenger strand on precursor miRNA

MFE

minimal free energy

AMFE

adjusted MFE

MFEi

minimal free energy index

EST

expressed sequence tag

GSS

genome survey sequence