Prediction and characterization of Tomato leaf curl New Delhi virus (ToLCNDV) responsive novel microRNAs in Solanum lycopersicum.

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Contents lists available at ScienceDirect

Virus Research journal homepage: www.elsevier.com/locate/virusres

Prediction and characterization of Tomato leaf curl New Delhi virus (ToLCNDV) responsive novel microRNAs in Solanum lycopersicum

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Bhubaneswar Pradhan a , Afsar Raza Naqvi b,1 , Shradha Saraf b , Sunil Kumar Mukherjee b,c,∗ , Nrisingha Dey a,∗∗ a

Division of Gene Function and Regulation, Institute of Life Sciences, Nalco Square, Chandrasekharpur, Bhubaneswar 751023, Odisha, India Plant Molecular Biology Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India c Department of Genetics, University of Delhi, South Campus, Benito Juarez Marg, New Delhi 110021, India b

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Article history: Received 29 May 2014 Received in revised form 28 August 2014 Accepted 1 September 2014 Available online xxx

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Keywords: Leaf curling Micro RNA Small RNA Next generation sequencing Virus responsive

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1. Introduction

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Tomato leaf curl New Delhi virus (ToLCNDV) infects tomato (Solanum lycopersicum) plants and causes severe crop losses. As the microRNAs (miRNAs) are deregulated during stressful events, such as biotic stress, we wanted to study the effect of ToLCNDV infection on tomato miRNAs. We constructed two libraries, isolating small RNAs (sRNAs) from healthy (HT) and ToLCNDV infected (IT) tomato leaves, and sequenced the library-specific sRNAs using the next generation sequencing (NGS) approach. These data helped predict 112 mature miRNA sequences employing the miRDeep-P program. A substantial number (58) of the sequences were 24-mer in size, which was a bit surprising. Based on the calculation of precision values, 53 novel miRNAs were screened from the predicted sequences. Nineteen of these were chosen for expression analysis; a northern blot analysis showed 15 to be positive. Many of the predicted miRNAs were up-regulated following viral infection. The target genes of the miRNAs were also predicted and the expression analysis of selected transcripts showed a typical inverse relation between the accumulation of target transcripts and the abundance of corresponding miRNAs. Furthermore, the cleavage sites of the target transcripts for three novel miRNAs were mapped, confirming the correct annotation of the miRNAtargets. The sRNA deep sequencing clearly revealed that the virus modulated global miRNA expression in the host. The validated miRNAs (Tom 4; Tom 14; Tom 17; Tom 21; Tom 29; Tom 43) could be valuable tools for understanding the ToLCNDV-tomato interaction, ultimately leading to the development of a virus-resistant tomato plant. © 2014 Elsevier B.V. All rights reserved.

The development and discovery of small non-coding RNAs has improved our understanding of their involvement in plant

Abbreviations: miRNA, microRNA; pre-miRNA, precursor microRNA; sRNA, small RNA; miRNA*, microRNA star; sRNAomes, small RNAomes; NGS, next generation sequencing; HTL, healthy tomato library; ITL, infected tomato library; gDNA, genomic DNA. ∗ Corresponding author at: Plant Molecular Biology Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, Q2 India. ∗∗ Corresponding author. Tel.: +91 0674 2300598; fax: +91 0674 2300728. E-mail addresses: [email protected] (B. Pradhan), [email protected] (A.R. Naqvi), [email protected] (S. Saraf), [email protected] (S.K. Mukherjee), [email protected], [email protected] (N. Dey). 1 Current address: Department of Periodontics, College of Dentistry, University of Illinois at Chicago, Chicago, IL 60612, USA.

development and response to different biotic and abiotic stresses (Khraiwesh et al., 2012). The microRNAs (miRNAs) are a class of small RNAs (sRNAs) of approximately 19–24 nucleotides in length, with a two nucleotide overhang at the 3 end in the duplex conformation (Bartel, 2004; Baulcombe, 2004; He and Hannon, 2004). Mature miRNAs are the key molecule to modulate target transcripts through cleavage, translational repression or mRNA decay. In plants, target transcripts are primarily cleaved by the action of corresponding miRNAs because a high degree of complementarity is present between the sequences of the target transcript and the mature miRNA sequence (Bartel, 2004). The biogenesis and function of these miRNAs have been reviewed elsewhere (Bollman et al., 2003; Ender and Meister, 2010; Fabian et al., 2010; Kurihara and Watanabe, 2004; Lobbes et al., 2006; Liu and Chen, 2010; Yang et al., 2006). The primary function of miRNAs is thought to be a role in plant developmental processes. In addition, the identification of miR398, which targets two Cu/Zn superoxide dismutases (CSD1 and CSD2) in response to oxidative stress (Sunkar, 2006), and

http://dx.doi.org/10.1016/j.virusres.2014.09.001 0168-1702/© 2014 Elsevier B.V. All rights reserved.

Please cite this article in press as: Pradhan, B., et al., Prediction and characterization of Tomato leaf curl New Delhi virus (ToLCNDV) responsive novel microRNAs in Solanum lycopersicum. Virus Res. (2014), http://dx.doi.org/10.1016/j.virusres.2014.09.001

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miR395 and miR399, which target the sulfate transporter (AST68) and the phosphate transporter (PHO1) respectively, reveal their importance in stress responses (Allen et al., 2005; Jones-Rhoades 47 and Bartel, 2004). 48 Studies in Arabidopsis thaliana and other crop plants such as rice 49 and maize indicate that miRNAs play a critical role in determining 50 plant-architecture. For instance, miR164 and miR165/miR166 take 51 part in shoot development by regulating NAC genes and Class III 52 HD-ZIP genes, respectively (Emery et al., 2003; Mallory et al., 2004; 53 54Q3 Williams et al., 2005; Zhou et al., 2007). These miRNAs, in addition to miR319, are also involved in leaf morphogenesis. For example, 55 miR164 regulates the serration of leaf margins by targeting CUC2 56 (Nikovics et al., 2006), and miR165/miR166 determines the adax57 ial/abaxial pattern of the leaf (Juarez et al., 2004). However, miR319 58 controls leaf morphogenesis by regulating a plant-specific class II 59 TEOSINTE BRANCHED1/CYCLOIDEA/PCF (TCP) transcription factor 60 (Palatnik et al., 2003). Because the viruses often distort the leaf 61 shape and structure of the host, it is important to determine which 62 miRNAs are affected by the virus. 63 The involvement of miRNAs in controlling biotic stresses 64 induced by viruses, bacteria and fungi in plants is well docu65 mented. The miRNAs miR156, miR160, miR164 and miR1885 are 66 involved in viral infections in different plants, including A. thaliana, 67 Nicotiana tabacum, and Brassica napus (Khraiwesh et al., 2012). 68 Studies from our laboratory demonstrated that miR159/miR319 69 and miR172 accumulate with ToLCNDV infection in tomatoes 70 (Naqvi et al., 2010). The Arabidopsis miR393 has been shown to be 71 associated with the resistance against the virulent bacterial strain 72 Pseudomonas syringae by repressing auxin signaling in Arabidopsis 73 (Navarro et al., 2006). 74 Worldwide, the tomato (Solanum lycopersicum L.) is one of 75 the most popular vegetables, ranking second in importance after 76 the potato (http://faostat.fao.org). Geminiviruses have emerged as 77 the most alarming threats to several vegetable crops, including 78 tomatoes, and they cause considerable yield loss (Boulton, 2003; 79 Mansoor et al., 2003; Moffat, 1999). Tomato leaf curl virus (ToLCV), 80 particularly Tomato leaf curl New Delhi virus (ToLCNDV), a gem81 inivirus of the genus Begomovirus, infects tomato plants with a 82 characteristic disease symptom of upward leaf curling and stunting 83 of the affected plants (Moriones and Navas-Castillo, 2000; Polston 84 et al., 1999). This virus is transmitted by an insect vector, Bemisia 85 tabaci (Hunter et al., 1998). These are circular, single-stranded 86 DNA viruses possessing either monopartite or bipartite (DNA-A 87 and DNA-B) genomes. For the bipartite viruses, both DNA-A and 88 DNA-B encoded transcripts are required for infection and symp89 tom development in host plants, but only the DNA-A component 90 is capable of autonomous replication inside the host (Azzam et al., 91 1994; Ingham et al., 1995; Pascal et al., 1993, 1994; Rigden et al., 92 1994). 93 Changes in the profile of tomato miRNAs following viral infec94 tion may provide clues for understanding their involvement in 95 the development of the disease symptoms (Naqvi et al., 2010). 96 Hence, the identification of ToLCNDV infection-responsive miRNAs 97 is necessary to elucidate the mechanism(s) underlying ToLCNDV 98 infections in tomatoes. Recently, the tomato genome sequence has 99 been published (Tomato Genome Consortium, 2012) and the avail100 ability of complete genome information offers an unprecedented 101 opportunity to identify/predict new genes and the sRNAs. Infor102 mation relating to novel miRNAs can finally be used to manipulate 103 the genetic background of the tomato to make it resistant against 104 ToLCNDV. 105 In this study, we constructed two sRNA libraries from healthy 106 (HT) and ToLCNDV infected (IT) leaves of tomato, followed by 107 sequencing these sRNAs using the GAII sequencer (Illumina, USA). 108 These sequences were subsequently analyzed by the miRDeep-P 109 script, which is usually used for plant miRNA prediction (Yang 110 45 46

and Li, 2011). We identified several differentially expressed novel miRNAs during ToLCNDV infection and validated the expression of a selection of those by northern blot analysis. In addition, we identified the target transcripts, the expression of which correlated well with the accumulation pattern of the novel miRNAs. Finally, we mapped the cleavage sites of three transcripts that are targeted by three novel miRNAs employing the standard techniques. The newly identified miRNAs and their target genes could eventually explain the disease symptoms and be valuable tools for developing a ToLCNDV resistant tomato plant.

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2. Materials and method

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2.1.1. Plant materials and virus infection

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Seeds of tomato cv. Pusa Ruby obtained from NSC, Pusa Campus, New Delhi, were soaked in water and kept for germination. At the seedling stage (1 month), they were transferred to vermiculite in a green-house (28 ◦ C, 14 h light and 10 h dark with 70% relative humidity). To obtain IT samples, agro infiltration of tomato plants was carried out at the 4–6 leaf stage using Agrobacterium tumefaciens EHA105 harboring plasmid constructs with dimers of the ToLCNDV-A [HQ264185.1] and ToLCNDV-B [HQ2641856.1] genome components (Pratap et al., 2011), as described below. Briefly, the Agrobacteria cells containing DNA-A and DNA-B dimers were mixed in equal proportion until the OD reached 0.4–0.6. The mixed culture containing 200 ␮m acetosyringone was kept at 28 ◦ C for 30 min and was infiltrated into tomato leaves abaxially using a syringe. Some plants were kept as the HT control without virus infection and a negative control (called mock infiltration) that were treated with Agrobacteria containing empty binary vector (pGreen0029). 2.1.2. Detection of the viral genome in infected tomato leaves Rolling circle amplification (RCA) was used to detect viral genomic DNA (gDNA). The tomato plants showing curled leaves (infected) were used for RCA. CTAB method was used to isolate gDNA from the leaves of IT, HT and mock infiltrated plants (Clarke, 2009). Approximately 100 ng of gDNA from each sample was used for the RCA reaction, following the protocol described in the manufacturer’s instructions (TempliphiTM , GE Healthcare, Cat No. 25-6400-50). The amplified products were digested with the EcoRI restriction enzyme (Fermentas, Cat No. ER0275) to obtain an approximately 2.7 kb band that confirms the presence of the viral genome. 2.1.3. Small RNA library preparation and next generation sequencing Total RNA was isolated from HT and IT tomato leaves using TRIzol reagent (Ambion, Life Technology, Cat No. 15596-018) as per the protocol described in the manufacturer’s instructions. The isolated RNA from both samples was used individually for sRNA library (HTL and ITL) preparation using TruSeqTM (Cat No. RS 2000012, Illumina, USA). Briefly, sRNAs were enriched from total RNA using 50% PEG with 0.1% sodium chloride and were separated by 15% denaturing polyacrylamide gel electrophoresis containing 7 M urea. The RNA bands of corresponding size between 15 and 30 nucleotides were gel purified. The purified RNAs were ligated with 3 and 5 adapters; first strand cDNA synthesis was carried out using the RT primer (Illumina, USA) with a PCR amplification process of 15 cycles, each consisting of the basic steps of denaturation, annealing and extension of 10 s at 98 ◦ C, 30 s at 60 ◦ C and 15 s at 72 ◦ C, respectively. The amplified products were purified by 6%


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polyacrylamide gel electrophoresis and subjected to NGS using the GAII platform (Illumina, USA).

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2.2.1. Pre-processing of NGS raw sequences for mapping and precursor sequence prediction The raw sRNA reads from the NGS data were subjected to pre-processing using the FastX tool kit The adapter (http://hannonlab.cshl.edu/fastx toolkit/). sequences were trimmed and low quality sequences were filtered on the basis of their quality score. The sequences that were 20–25 nucleotides long with an abundance > 4 reads were then mapped to the tomato genome sequence (http://solgenomics.net/organism/Solanum lycopersicum/genome) and the sRNAs were aligned to the tomato genome sequence using the tool Bowtie (http://bowtie.cbcb.umd.edu). Those sRNA reads which mapped to multiple positions in the genome and to other non-coding RNAs (tRNA, rRNA, snRNA and snoRNA) were filtered out and the remaining unique sequences were used for further analysis. The potential precursor sequences (approximately 300 nucleotides flanking the mapped positions) from the genome were excised and secondary structures were predicted using RNAfold (http://www.tbi.univie.ac.at/∼ronny/ RNA/RNAfold.html). The individual structures, described here, were predicted using Mfold software (http://mfold.rna.albany.edu/?q=mfold). The sequences were filtered on the basis of free energy and structures. The reads with secondary structures of calculated free energy > −20 kcal/mole were selected for analysis and subjected to miRDeep-P algorithm to identify novel miRNAs. The sequences were filtered based on structure and the following criteria: (i) four or fewer mismatches between miRNA/miRNA* strand; (ii) the miRNA/miRNA* forms a duplex with two nucleotide overhangs at the 3 end; and (iii) asymmetric bulges are minimal in size (one or two bases) and frequency (typically one or less), especially within the miRNA/miRNA* duplex (Meyers et al., 2008). To determine the precise processing of the pre-miRNA to form mature miRNA and miRNA* duplexes, the raw unique sRNA reads were mapped to the predicted precursor sequences. The processing precision was calculated for the predicted miRNA/miRNA* duplexes as the abundance values of mature miRNA and miRNA* reads divided by the total abundance values of aligned reads (Fahlgren et al., 2010). For identification of conserved miRNAs, the predicted miRNAs were aligned with the miRBase database of the tomato (Release 19, http://www.mirbase.org/index.shtml). The rest of the predicted miRNAs were novel miRNAs. The prediction pipeline is presented in Fig. S1 (Supplementary file). 2.2.2. Normalization of microRNAs The frequencies of the miRNAs of HTL and ITL were normalized to one million by total short reads (excluding reads which did not map to the tomato genome or other non-coding RNAs) in each library. 2.3. Identification of conserved and novel microRNAs and their target genes in tomato The miRNA* sequences in the predicted precursor sequences were confirmed by matching them with miRNA* sequences from the NGS data. To predict the conserved and novel miRNA, the sequences having both miRNA and miRNA* sequences (present in the NGS data) were subjected to BLASTN searches against miRBase mature miRNA sequences from the tomato. The target genes of the corresponding mature

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miRNAs were also predicted using the web-based plant small RNA target analysis server (psRNATarget) (Dai and Zhao, 2011). 2.4. Total RNA isolation and microRNA northern blot analysis Total RNA was individually isolated from HT and IT leaves of three independent tomato plants, respectively and also from the mock infiltrated control plants using TRIzol reagent, following the protocol described in the manufacturer’s instructions (Ambion, Life Technology, Cat No. 15596-018). The total RNA was resolved by 12% denaturing polyacrylamide gel electrophoresis containing 7 M urea (taking 20 ␮g from each sample). The RNA was then electroblotted onto a Hybond N+ membrane (GE Healthcare, Cat No. RPN 303B) and UV cross-linked. The probes were prepared using antisense DNA probes to the mature miRNA sequences (listed in Table S3, Supplementary file) that were end labeled with [␥ 32 P] ATP (6000 Ci/mmol, PerkinElmer Life Sciences, USA) using T4 polynucleotide kinase (Fermentas, Cat No. EK0031). The antisense U6 snRNA sequence was used as the reference control. The probes were purified using MicrospinTM G-25 columns (GE Healthcare, Cat No. 27-5325-01) according to the manufacturer’s instructions. The membranes were incubated with pre-hybridization buffer for 4 h at 37 ◦ C followed by overnight hybridization. The blots were washed three times with 2X SSC + 0.1% SDS at the hybridization temperature, briefly air dried, and autoradiographed using a TYPHOON phosphor imager (GE Healthcare). The intensities of the miRNA bands were quantified using ImageJ software (http://rsbweb.nih.gov/ij/index.html). Each of the northern blot experiments was repeated three times. One representative northern blot is presented with validated relative abundance values (quantified from the intensities of miRNA band) from all the blots. 2.5. Expression analysis of target genes involved in leaf morphogenesis Total RNA was isolated from HT and IT leaves of tomato plant using TRIzol reagent, following the protocol described in the manufacturer’s instructions (Ambion, Life Technology, Cat No. 15596-018). Approximately 3 ␮g of DNaseI treated total RNA (Fermentas, Cat No. EN0523) was used for first strand cDNA synthesis using the RevertAidTM H minus First Strand cDNA Synthesis Kit (Fermentas, Cat No. K1612). Random hexamer primers were used for first strand cDNA synthesis. For real-time PCR, SYBR Select Master Mix (Fermentas, Cat No. K0251) was used in samples containing 20–30 ng cDNAs and the gene specific primers (listed in Table S5, Supplementary file). The tubulin gene was used as the reference control. We performed three independent experiments to confirm the level of expression of each transcript. 2.6. Modified 5 RACE PCR Total RNA was isolated with TRIzol reagent as per the protocol described in the manufacturer’s instructions (Ambion, Life Technology, Cat No. 15596-018) from leaves of IT tomato plants, and a modified 5 RACE was performed on 2 ␮g of total RNA using the RLM RACE kit (Ambion, Life Technology, Cat No. AM1700). A nested PCR was performed using adapter and gene-specific primers (Table S6, Supplementary file), and the PCR products were cloned into the pGEM-T Easy vector (Promega, Cat No. A1360). The clones were sequenced using an ABI 3500 Genetic Analyzer.


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There were 7195133 and 6485965 raw reads in the HTL and ITL, respectively, obtained from the NGS. After adapter trimming and quality filtering of both libraries using the FastX tool, 3831219 and 3752928 unique reads were left in the HTL and ITL, respectively. We chose 102851 unique reads of 20–25 nucleotides in length with abundance value > 4 from ITL. Only

75% (77,138) of the reads mapped exactly to the tomato genome. Finally, we were able to select 310 read-sequences using the miRDeep-P algorithm that were associated with the putative miRNA* sequences. Of these 310 selections, only 112 mature sequences satisfied the criteria for annotation of miRNAs (see Section 2.2.1 and the pipeline of analysis in Fig.S1 (Supplementary file)). The raw data can be obtained from Gene Expression Omnibus (GEO) with the accession no. [GSE53253] or the following link (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token= ihediemixfufpar&acc=GSE53253).

Fig. 1. Predicted hairpin structure of selected precursor microRNA (pre-miRNA). The pre-miRNA sequences were used for probable hairpin structure formation using Mfold

Q5 software and were presented. The miRNA and miRNA* sequences were marked in blue and red bulleted capital letter of miRNA sequences, respectively. (For interpretation of the references to color in this text, the reader is referred to the web version of the article.)


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miRNAs remain almost unaltered in abundance (sly-miR4376) while others are either down- (sly-miR156a, sly-miR166b) or upregulated (sly-miR160a, sly-miR482b) following infection with ToLCNDV. 3.3. Prediction of novel tomato miRNAs and their validation by northern blotting

Fig. 2. Venn diagram showing the known and novel miRNAs from tomato. The total number of tomato miRNA sequences available in mirbase and NGS is presented as Venn diagram, which depicts 13 miRNAs are common to both the domain. The novel miRNAs are again categorized as low processing precision and medium to high processing precision taking the cut off value as 0.10. 53 novel miRNAs are reported as medium to high and 46 are as low processed miRNAs.

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3.2. Identification of conserved tomato miRNAs The precursor-miRNAs (pre-miRNAs) were predicted by examining approximately 300 nucleotide sequences of the tomato genome, corresponding to both the 3 and 5 flanking regions of the mapped sequence and also by examining the secondary structure of this region, using the RNAfold bioinformatics tool. The stem-loop structures with free energy > −20 kcal/mole were used for the identification of pre-miRNAs of conserved and novel miRNAs. The miRNA* sequences corresponding to any particular miRNA were predicted from pre-miRNAs. The co-ordinates of pre-miRNAs, mature miRNAs and miRNA* sequences are presented in Table S1 (Supplementary file). The predicted miRNA* sequences were then searched for within the database of sRNA reads. The miRNAs were annotated as novel only when the miRNA* sequences were identified within the sRNA data. Some selected pre-miRNA structures of novel miRNAs (Tom 2, Tom 5, Tom 66, Tom 28 and Tom 64) are shown in Fig. 1. There are only a few (44) miRNAs for tomato in the miRBase (release 19). We found only 31 unique miRNA sequences in our ITL, which matched exactly with the tomato miRNA sequences present in miRBase (release 19). However, in this report, we have included only those miRNAs/miRNA* whose abundance was > 4 reads. With this stringency, we report here 13 conserved miRNAs (Table 1) belonging to 11 different families in our prediction analysis (Fig. 2) of the NGS data. As evident in Table 1, a few Table 1 Normalized abundance values of known tomato mature microRNAs from NGS data. Sl. no.

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Known microRNAs

Sly-miR156a Sly-miR160a Sly-miR162 Sly-miR166b Sly-miR167 Sly-miR171a Sly-miR172 Sly-miR399 Sly-miR482a Sly-miR482b Sly-miR482c Sly-miR4376 Sly-miR5300

Normalized abundance values (×10−6 ) Healthy (HTL)

Virus infection (ITL)

16,387 135 857 49,999 34,435 228 3865 24 156 278 2 1716 2125

8414 528 1012 29,800 18,096 353 4449 16 217 850 12 1797 1880

The raw unique sRNA reads were mapped to the predicted precursor sequences to determine the precise processing of the pre-miRNAs to form mature miRNA and miRNA* duplexes. Two representatives (Tom 2, Tom 5) of the alignment of all the reads relative to corresponding pre-miRNAs are shown in Fig. 3. The processing precision was calculated for all the predicted miRNA/miRNA* duplexes and the values are shown in Table S1 (Supplementary file). The processing precision value signifies the formation of any mature miRNA/miRNA* duplex. A value close to one indicates 100% or high precision and a value close to zero indicates the production of very few miRNA/miRNA* duplexes (Ma et al., 2010). Initially, we were able to predict 112 miRNAs from the NGS data using the miRDeep-P script and from their predicted hairpin structure. Of these 112, only 99 miRNAs were captured as novel (Fig. 2). We screened the 99 novel miRNAs based on their miRNA/miRNA* duplex processing precision, applying a low stringency cut off value of 0.10 (10% processing precision) (Goettel et al., 2014), and identified 53 novel miRNAs (Table 2a), including three de novo miRNAs (Table 2b), which were induced upon ToLCNDV infection. We present the frequency-distribution pattern of these 112 miRNAs based on their length (Fig. 4). It is noteworthy that a substantial number of the predicted miRNAs were 24-mer long. Although this finding is surprising, at least four of these miRNAs were validated in the northern blot analysis. The majority of the miRNAs were 24-mer, followed by 21-mer sequences. The tomato-specific novel miRNAs and their predicted abundance after normalization from the NGS data are listed in Tables 2a and 2b. Table 2b lists only those miRNAs that are normally not present in healthy tomato plants but are induced de novo following viral infection. Of the total pool of 112 mature miRNAs, we selected only 19 miRNAs (Tom 2, Tom 5, Tom 6, Tom 11, Tom 14, Tom 17, Tom 20, Tom 21, Tom 22, Tom 25, Tom 28, Tom 33, Tom 38, Tom 40, Tom 43, Tom 48, Tom 51, Tom 52 and Tom 89) based on their high abundance in the NGS dataset for the analysis of expression in HT and IT tomato leaves. Subsequently, we performed expression analysis of these selected miRNAs using the northern blots of total RNA from both HT and IT leaves of tomatoes (Fig. 5A and B) and respective miRNAspecific antisense oligomers (Table S2, Supplementary file) as the radio-labeled probes. We observed that 15 out of 19 selected miRNAs were positive in the northern blots (Fig. 5 B). The other four (Tom 20, Tom 25, Tom 51 and Tom 89) were not evident on the northern blot (data not shown). The northern blot data revealed that 10 miRNAs were significantly up-regulated upon ToLCNDV infection in comparison to the HT samples. Interestingly, we observed an absence of predicted miRNA* sequences for four miRNAs (Tom 20, Tom 25, Tom 51 and Tom 89) in HTL but these sequences were present in the ITL of NGS dataset (Table 2a). 3.4. Prediction of target genes and their functional categorization To predict the target genes of newly identified miRNAs from tomato, we used the web-based plant small RNA target analysis server (psRNATarget) (Dai and Zhao, 2011) and two transcript datasets, S. lycopersicum cDNA library version 2.3 and SGN unigenes. The predicted target genes are listed in Table S3


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Normalized abundance values (×10−6 )

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Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom

UCGGACCAGGCUUCAUUCCUC UUGGCUGAGUGAGCAUCACGG UCGCUUGGUGCAGGUCGGGAC AAGCUCAGGAGGGAUAGCGCC AAGCUCAGGAGGGAUAGCACC UGGAGAAGCAGGGCACGUGCA UUGGCUGAGUGAGCAUCACUG AGAAUCUUGAUGAUGCUGCAG UUCCACAGCUUUCUUGAACUG UUUAGCAAGAGUUGUUUUACC ACGGGGACGAGCCAGAGCAUG UGGCAAGCAUCUUUGGCGACU UUGGCAUUCUGUCCACCUCC AAUGCAAUGUCAUAUACCAUC UUCCACAGCUUUCUUGAACUU AGGAAACUGUUUAGUCCAACC GGAAUCUUGAUGAUGCUGCAG AACGAGUGAGACUUGCUCAGUUGG AGGCGCAUGUGUCAUAUCUUUACA UAAAGCUGCCAGCAUGAUCUGG UUAGAUGAACAUCAACAAACA UUUCCUAUUCCACCCAUGCCAA AGGUGUAGGUGUUCAUGCAGA AUCAUGCGAUCUCUUCGGAAU AUAUUGGUGCGGUUCAAUUAG AGAUAUUGGUGCGGUUCAAUG ACGUCCCUUCCCCAUCGUUCAACA UAUGUUCUCAGGUCGCCCCUG AAGUGUGUCUCUGAGAUUUCGGAU GGGCUACUCUCUAUUGGCAUG UGGGGUCCUAGUAGAGUCGGUUC AAGUGUGUCUCUGAGAUUUCGGGC UCUUAUGAAUUCUAGGUCUUCU UAUUGGCCUGGUUCACUCAGA UCAACGCUAAACUCGAUCAUG CAAAAUACCCUUGUCAUCCAA UCGAAAUCUCAGAGACACACUUAU UUGGCAUAAGUUUGUGAAAGCCGG UACCAAUAAUUGAGAUAACAUC AGACAUGUUCUAAUCGUCAGCUUC AUUUACCCCAAGUUCGUUGUC AAGUGUGUCUCUGGAAUUUCGGGC UUGAGCCGCGUCAAUAUCUCU UAGGCGUUGUCUGAGGCUAAC ACCCCUUUUCGGCCUACGUGGCAC ACCCCUUUUUAGCUUACGUGGCAC UCGAAAUCUCAGAGACACACUUAU AGAUUGAUAUACGUUACUCACAGU CUUGGAACCACAGUUACCACC AUAAUAACUAUUAGUUGAAUG UAGAAGAAUCAUAUAUACCCCUA

Osa-miR166g-3p NOVEL Nta-miR168a Osa-miR390a Osa-miR164 NOVEL NOVEL Ath-miR172c NOVEL NOVEL NOVEL NOVEL Osa-miR394a NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL Stu-miR482c NOVEL NOVEL NOVEL Aly-miR171c-5p NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL NOVEL

131,085 1645 13,049 1169 3513 1437 2242 737 386 40 52 124 256 81 182 3 30 22 109 116 36 53 36 235 49 13 5 1 30 50 41 305 49 7 6 20 170 1 2 2 9 19 7 4 2 3 170 71 2 1 5

Healthy (HTL) 2 5 6 8 9 10 11 14 15 17 18 20 21 22 23 25 27 28 30 31 32 33 35 36 37 38 39 40 43 45 47 48 51 53 55 56 62 63 66 67 68 72 73 89 91 93 99 102 104 107 111

Predicted star sequence

Virus infection (ITL) 51,957 9527 9074 4139 1623 3137 2712 1440 1407 786 348 292 375 268 251 210 155 154 106 88 87 79 62 54 62 42 42 38 21 30 28 416 24 23 22 18 56 14 12 11 22 30 12 37 6 16 56 76 7 5 8

GGGAUGUUGUCUGGCUCGACA CAGGUGCUCACUCAGCUAAUA CCUGCCUUGCAUCAACUGAAU CGCUAUCCAUCCUGAGUUUCA CGCUAUCCAUCCUGAGUUUUA CAUGUGCCUGUUUUCCCCAUC GAGGUGCUCACUCAGCUAAUA GCAGCAUCUUCAAGAUUCACA GUUCAAUAAAGCUGUGGGAAG AGAAACAACACUUGCUAAAGG UGCACUGCCUCUUCCCUGGCU UAGCCAAGGAUGACUUGCCUUU AGGUGGGCAUACUGUCAACA UGGUAUAUGACCUUGCAAUUCA GUCCAAGAAAGCUGUGGGAAA UUGGACUGAAGGGUUUCCUUC GGAGCAUCAUCAAGAUUCACA AGCUUAGCAAGCCUCAUCCGUUUU UAAGGAUAUGACACAUGAGCCCAA AGGUCAUCUAGCAGCUUCAAU UUUGUUGAUGGUCAUCUAUUC GGAGUGGGUGGGAUGGAAAAA UGCAUUUGCACCUGCACCUUA UCCAAAGGGAUCGCAUUGAUCC AAUUGAGCCGUGCCAAUAUCA UUGAGCCGCGCCAAUAUCACG UUGAAUGUUGGGGAACGAACGUCU GGGUUGAUUUGAGAACAUAUG UCGAAAUUUCAGAGACACACUUAU CGCCAAAGGAGAGCUGCCCUG ACUGACUCUACUCGGAUUCCAAA UUGAAAUCUCAGAGACACACUUAU ACAACUUAGAGUUCAUAAGAUC UGAUUGAGCCGUGUCAAUAUC UGAUUGAGUGCAGCGUUGAUG GGAUGACAAGGGUAUUUUGGA AACUGUGUCUCUGAGAUUUCGGAC AGCUUUAACAAAUUUGUGCCAACC UGUUAUCUCAGUGUUGGCAUG AGCUGACGAUUAGAACAUGUCAAA CAACGUACGUAGGGUAAGUGG UCAAAAUCUCAGAGACACACUUAU AGAUAUUGAUGCGGUUCAAUC UAGCCAAGGAUGACUUGCCUAAA GUCACGUAGACCGAAAAGGGGUAG GCCACGUAGACUAAAAAGGGGUAG AAAUGUGUCUCUAAGAUUUCGGAC UUACAGUGACGUAUAUCAACCUAA UGGUGAUUGUGGUUCGAAAAU UUCGACUAAUAGUUAUUAUUU AGGGTATATGTGAGCAATTTTGTA


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Predicted mature sequence

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Mir ID


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Table 2a Normalized abundance values of predicted novel and conserved tomato mature microRNAs from NGS data (precision values ≥0.10).



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Fig. 3. Mapping of all the raw sRNA reads to the pre-miRNA structure. All the raw unique sRNA reads were mapped to the predicted precursor sequences of Tom 2 and Tom 5. The upper sequence is the predicted pre-miRNA sequence and below this, miRNA and miRNA* sequences are shown and the processing precision value are presented. All the aligned sRNA reads were also shown with their corresponding abundance values as time of representation in the library as ( x).

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(Supplementary file). The functional categorizations of the targets were carried out using the agriGO analysis toolkit (Du et al., 2010) and were found to be involved in different biological and molecular processes, such as transcriptional regulation, defense responses, apoptosis, protein folding and chaperone activities (Table S4, Supplementary file).

3.5. Expression analysis of target genes involved in leaf morphogenesis To confirm the expression analysis of the target genes, we selected four newly identified miRNAs (Tom 14, Tom 17, Tom 21 and Tom 43) and two known miRNAs (Tom 4 (Sly-miR156a)


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Table 2b Normalized abundance values of de novo tomato mature microRNAs upon virus infection from NGS data. Sl. no.

1 2 3

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Mir ID

Tom 88 Tom 109 Tom 110

Predicted mature sequence

Novel/conserved

UUGGCAUGAGUUUGUGAAAGCCGG AGUCACUUUGAUGAUUGUCAAACA UUGGAAAGAAGGGAGCUCUAC

NOVEL NOVEL NOVEL


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and Tom 29 (Sly-miR160a)) that play a role in leaf morphogenesis/disease symptoms (Table 3). We analyzed the transcript level using real-time PCR with the corresponding gene specific primers (Table S5, Supplementary file). These selected targets have very good matches with their corresponding miRNAs (Table 3). The real-time expression data suggested that the abundance of the transcripts are in agreement with the prediction because the predicted targets were over-expressed with the downregulation of the corresponding miRNAs (Fig. 6). In case of the Squamosa promoter binding protein (SBP) transcription factor

Predicted star sequence

AGCUUUAACAAAUUUGUGCCAACC UUUGACAAUUAUCAGAGUGACUUG AGAGCUCUUCUGAAUUCCAAAG


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[Solyc02g077920.2.1 and SGN-U317177] and the F-box type of protein families [Solyc05g015520.2.1] targeted by the miRNAs Tom 4 and Tom 21, respectively, we observed an approximately 2-fold-enhanced accumulation of SBP and F-box transcripts following viral infection. However, the inverse relationship between the abundance of miRNA and the accumulation of target transcript did not hold for miRNA Tom 21. Apart from this event, the inverse relationship held well with other test miRNAs. We also observed an approximately 4-fold upregulation of the CCNBS-LRR [Solyc05g008070.2.1] type transcript directed by miRNA

Table 3 The microRNA and their target genes involved in leaf development. microRNA

TARGET

miRNA ID

Accession no. and description

Function

Expectation value

Tom 4 Sly-miR156a

Solyc02g077920.2.1 (Squamosa Promoter binding like protein)

Plant architecture, leaf development and vegetative phase change

0.0

SGN-U317177 (Squamosa Promoter binding like protein, SBP-3)

Plant architecture, leaf development and vegetative phase change

0.0

Tom 29 Sly-miR160a

SGN-U324618 (ARF-17)

Hormone signaling and plant development

0.5

Tom 11

Solyc08g082850.2.1 (ABC Transporter)

Translocation of various substrates across membranes

3.0

Tom 14

Solyc04g049800.2.1 (AP2/ERF Like)

Determination of floral organ identity and leaf epidermal cell identity

0.5

Tom 17

Solyc05g008070.2.1 (CC NBS LRR)

Disease Resistance

1.0

Tom 21

Solyc05g015520.2.1 (F Box Family protein)

Ubiquitination of proteins targeted for proteasomal degradation

1.0

Tom 43

GQ496337 (TCP19)

Leaf architecture and plant development

1.0

miRNA: target alignment


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Fig. 4. Distribution pattern of the predicted miRNAs from tomato. The frequency distributions of all miRNAs were presented based on number of nucleotide present in the miRNA.

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Tom 17 in ToLCNDV infected plants. On the contrary, we observed downregulation of targets such as Auxin response factor (ARF) 17 [SGN-U324618], AP2/ERF [Solyc04g049800.2.1] and TCP19 [GQ496337] upon ToLCNDV infection, which justifies the functional activation of miRNAs (Tom 29, Tom 14 and Tom 43) during ToLCNDV infection. 3.6. Cleavage assay of target transcript

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Having established that novel miRNAs and their targets exhibit inverse expression profiles, we attempted to identify the cleavage site in the target transcript of three newly identified miRNAs (Tom 11, Tom 17 and Tom 21) because identification of the cleavage sites would establish the veracity of the target annotation. The targets are ABC transporters [Solyc08g082850.2.1], CC-NBSLRR [Solyc05g008070.2.1] and F-box types of protein families [Solyc05g015520.2.1] for Tom 11, Tom 17 and Tom 21, respectively. Of these three, we particularly wanted to map the cleavage sites of Tom 21 because its mature miRNA size is 20-mer, which is shorter than the usual miRNA size. Moreover, following viral infection, the Tom 21 abundance increased but the target abundance was discrepant; i.e., it increased approximately two-fold instead of dropping (Fig. 6). The modified 5 RACE PCR was performed with the RNA derived from HT tomato leaves as described in Section 2.6, followed by sequencing of the clones. The sequences obtained from five, eight and five independent recombinant clones of target transcripts of Tom 11, Tom 17 and Tom 21, respectively, were aligned with the adapter sequence and the cleavage site was identified (marked by an arrow as shown in Fig. 7). The cleavage data confirmed that the predicted Tom 21 miRNA target site was genuine.

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4. Discussion

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The white fly-transmitted ToLCNDV may be monopartite, containing DNA-A, or bipartite, containing both DNA-A and DNA-B genomic components. Monopartite viruses can induce disease symptoms in host plants but bipartite viruses enhance symptom severity. Recent advances in miRNA research have established the roles of miRNAs in gene regulation controlling plant development, metabolism and stress responses (abiotic and biotic) (Khraiwesh et al., 2012). The host miRNAs contributing resistance against bacterial disease are also emerging (Navarro et al., 2006). Previously, our group reported the deregulation of miRNA159/319 and miRNA172 during profiling of the ToLCNDV infection-responsive miRNA from tomato with an array-based approach (Naqvi et al., 2010). As an extension of the previous study, we adopted high throughput sequencing approaches to the pool of sRNAomes present in ToLCNDV infected plants, with the aim of identifying novel miRNAs for elucidating the mechanism of plant–virus interactions.

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We have reported 53 out of 99 identified, novel miRNAs from tomato using NGS data. The majority of the annotated mature miRNAs were 24-mer (approximately 52%), followed by 21- (approximately37%) and 22-mer (approximately8%) (Fig. 4). Repeat associated siRNAs (rasiRNAs) are a class of sRNAs of 24–29 nucleotide involved in the maintenance of heterochromatin structure, regulating the transcripts arising from repeat sequences, and silencing transposons and retro transposons (Farazi et al., 2008). To rule out the possibility that 24-mer sRNAs may be rasiRNAs, the sequences mapping to highly repeated sequences annotated in genomes were discarded during the prediction pipeline using miRDeep-P (see Section 2.2.1 and Fig. S1 (Supplementary file)). In addition, the stem loop structures of the pre-miRNA sequences were generated and are shown in Fig. 2. Some miRNAs are wellconserved across species, and tend to accumulate at a higher level in contrast to young miRNAs, which might cleave the target with less precision efficiency. The young miRNAs also deviate in size from the canonical miRNAs, being typically 24-mer in length instead of 21- to 22-mer (Ma et al., 2010; Goettel et al., 2014). It has also been hypothesized that the evolution of MIR genes could be observed in the inverted duplicates of genomic regions (Dorner et al., 2007; Fahlgren et al., 2007, 2010; Farazi et al., 2008; Nozawa et al., 2012; Rajagopalan et al., 2006). Selected miRNAs, including 24-mers, were characterized using total RNA extracted from three different biological replicates of HT and IT tomato plants. We selected 19 out of 99 predicted novel miRNAs and found that 15 (Fig. 5B) were positive on northern blot while the other four were negative. The absence of bands in northern blots may be due to the low expression of the corresponding miRNAs inside host tissues. Three of the sRNAs, which tested positive in northern blots, were probed with oligomers for both strands. However, intense auto-radiographic signals were observed only for the antisense probes and very weak signals were generated with the sense probes, suggesting a single-stranded character for the sRNAs. These data strongly suggested that these are true miRNAs and not the siRNAs. Interestingly, some miRNA sequences were found exclusively in the IT samples but not in the HT samples. It has been reported earlier that the viral RNAi suppressors stabilize the miRNA: miRNA* duplexes (Chapman et al., 2004; Lakatos et al., 2006). As ToLCV encodes at least three RNAi suppressors (AC2, AC4 and AV2), it is possible that these molecules play their roles in the increased detection of the star sequences (Chellappan et al., 2005; Vanitharani et al., 2004). We also predicted the target genes of all the miRNAs, and, using the agriGo analysis toolkit (Du et al., 2010), functionally categorized the identified targets of the miRNAs. The functional annotation of the 112 miRNAs suggests their involvement in metabolic, cellular and biological processes activated during ToLCNDV infection. These are listed in Table S4 (Supplementary file). Precise information and the functional role of each miRNA could be of immense value in developing ToLCNDV resistant plants. Furthermore, we were interested in studying miRNAs that are related to leaf curling/disease symptom development. The major disease symptom of ToLCNDV is the curling of leaves, which may be associated with the alteration of respective sets of genes. To examine the global role of the novel miRNAs identified in this study, we sought to capture their targets employing in silico analysis. Target prediction analysis revealed various genes that defend plants against pathogen infection. For instance, Tom 14, Tom 17 and Tom 43 have the gene targets of AP2/ERF, CC-NBSLRR and TCP19 proteins, respectively, that might contribute to leaf-curling. The AP2/ERF transcription factor is the key regulator for determination of leaf epidermal cell and floral organ identity and also the mechanism adopted by plants to respond to various types of biotic and environmental stress (Riechmann and Meyerowitz, 1998). The TCP19 transcription factor regulates


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G Model VIRUS 96396 1–13 10


Fig. 5. (A) Northern blots of selected predicted (novel) miRNAs. The miRNA levels were up regulated upon virus (ToLCNDV) infection. The experiments were performed thrice. The normalization was done taking U6 snRNA as probe and further quantified using ImageJ software. The corresponding NGS and validated values (band intensities) were represented in top. (B) Relative fold changes of the intensities of all the novel miRNAs. The relative intensities of northern bands obtained from 15 novel miRNAs depicting the relative abundances of corresponding miRNA in healthy and infected tomato plants. The bands were quantified from the northern blot experiment. The intensities of healthy were treated as 1 in all the miRNAs. The standard deviation was calculated and represented in the bars. The experiments were repeated thrice and the mean data were presented.




Fig. 6. Real time expression profiles of some candidate genes involved in leaf development. The histogram showing the relative fold changes (log2 fold) of all the genes taken for the study with respect to healthy and ToLCNDV infected leaf samples. The experiments were repeated thrice and the mean data were presented.

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leaf architecture and plant development (Palatnik et al., 2003), while CC-NBS-LRR type proteins are involved in pathogen recognition and disease resistance against diverse pathogens, including bacteria, viruses, fungi, nematodes and insects (Jones and Jones, 1997). We also studied the expression of the SBP-box and ARF-17 transcription factors targeted by the corresponding miRNAs, Tom 4 and Tom 29, respectively, as experimental controls. The real-time PCR and northern blot analysis confirmed the down-regulation of ARF-17, AP2/ERF and TCP19 upon ToLCNDV infection, while the Tom 4 (sly-miR156a) targeted SBP transcription factors were upregulated by approximately 2-fold. Interestingly, we also observed the up-regulation (approximately 4-fold) of the transcript CCNBS-LRR type, targeted by miRNA Tom 17. The up-regulation of this CC-NBS-LRR type of proteins is obvious because they play a crucial role in pathogen recognition and adaptation. These data suggest that the interaction of miRNA with corresponding targets plays a major role in leaf curling. In a previous study, we showed that ToLCNDV infection in tomatoes induces altered expression of several genes. The majority of those genes participate in plant defense responses and signaling. For instance, Avr9/Cf-9 is involved in PAMP recognition and early signaling, PR-1 regulates innate immune responses and rpL40, 40S ribosomal protein S-15-like is induced in response to pathogen entry. Genes that regulate transcription were also up-regulated in ToLCNDV infected plants, including transcription factors such as ERF4 and WRKY30 and histone H2A (Naqvi et al., 2011). Taken together, these observations suggest that the expression of various protein coding and miRNAs genes is impacted by viral infection. The balance between these pro-host or pro-pathogen genes will eventually decide the outcome of this interaction. Transgenic plants over-expressing the genes ARF-17, AP2/ERF and TCP19 may have potential in developing ToLCNDV resistant tomato plants. However, the up-regulation of the CC-NBS-LRR-type protein or the down-regulation/silencing of the miRNA Tom 17 could help us to understand the mechanism of ToLCNDV infection. The miRNAmediated gene regulation relies mainly on translational repression or cleavage of the targets. In this context, we demonstrated the cleavage site of Tom 17 miRNA based upon our prediction, which cleaves the target Solyc05g008070.2.1 (a CC-NBS-LRR-type

11

Fig. 7. Modified 5 RACE PCR Analysis of a Target Transcript. Analysis of microRNA cleavage sites using modified 5 RACE. The nested PCR performed using adapterspecific and gene specific primers, and subsequently the PCR products obtained was cloned in to pGEMT easy vector. The sequencing of the recombinant clones were aligned with adapter sequences and the cleavage site was identified (marked by an arrow).

protein). The modified 5 RACE PCR analysis confirmed the cleavage of the target [Solyc05g008070.2.1] transcript between U and C residues, corresponding to the complementary A and G residues at the 10th and 11th position of Tom 17 miRNA. In addition, we mapped the cleavage sites of the target [Solyc05g015520.2.1] transcript between the A and C residues that correspond to the complementary bases U and G of miRNA Tom 21. The predicted cleavage sites matched very well with those found by the mapping techniques. This analysis revealed that although the target prediction with miRNA Tom 21 was accurate, the target abundance did not show the expected decrease as Tom 21 accumulated following viral infection (Fig. 6). It thus appears that the biosynthesis of the Tom 21 target occurs at a rate higher than the degradation rate of its mRNA. The ABC transporter ATP binding proteins are involved in the translocation of various substrates across membranes, including phytohormones (Geisler and Murphy, 2006; Santelia et al., 2005; Yang and Murphy, 2009) and secondary metabolites (Yazaki, 2006). Secondary metabolites protect plants against herbivores and pathogens, while phytohormone signaling, specifically auxin and gibberellin, has a significant impact on leaf patterning and plant development. It is tempting to engineer the target ABC transporter, employing the Tom 11 miRNA, and thus new opportunities might arise for developing a plant resistant to viral attack. This report enumerates the tomato miRNAs that respond to ToLCNDV infection. The functional studies of each of these miRNAs or a subset of these will be required to understand the details of ToLCNDV-tomato interaction, and almost certainly a few of these miRNAs could be manipulated or engineered to confer viral resistance in a transgenic tomato. 5. Conclusions We have identified several novel miRNA sequences from tomatoes before and after ToLCNDV infection. The miRNAs from the NGS data and the miRNA northern blot of RNAs derived from HT and IT tomato leaves exhibited distinctive expression patterns. The target transcripts were regulated by the corresponding miRNAs. The salient findings of this report have been schematically displayed in


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supplementary Fig. S2. Our analysis reveals that the validated and novel ToLCNDV induced miRNAs Tom 4, Tom 14, Tom 17, Tom 21, Tom 29, and Tom 43 could be valuable tools for understanding ToLCNDV-tomato interaction and also the possibility of developing a virus-resistant tomato plant. Competing interests The authors declare that they have no competing interests. Authors contributions Conceived and designed the experiments: N.D. and S.K.M. Performed the experiments: B.P., A.R.N., S.S. Analyzed the data: B.P., N.D. and S.K.M. Wrote the manuscript: B.P. and S.S. Helped in drafting the manuscript: N.D. and S.K.M. All authors read and approved the final manuscript. Acknowledgements We are thankful to DST, Govt. of India for providing DST INSPIRE fellowship to BP. We also thankful to our Director, Institute of Life Sciences, Govt. of India, for providing laboratory facilities. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.virusres.2014.09.001. References Allen, E., Xie, Z., Gustafson, A.M., Carrington, J.C., 2005. microRNA-directed phasing during trans-acting siRNA biogenesis in plants. Cell 121, 207–221, http://dx.doi.org/10.1016/j.cell.2005.04.004. Azzam, O., Frazer, J., de la Rosa, D., Beaver, J.S., Ahlquist, P., Maxwell, D.P., 1994. Whitefly transmission and efficient ssDNA accumulation of bean golden mosaic geminivirus require functional coat protein. Virology 204, 289–296, http://dx.doi.org/10.1006/viro.1994.1533. Bartel, D.P., 2004. microRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281–297. Baulcombe, D., 2004. RNA silencing in plants. Nature 431, 356–363, http://dx.doi.org/10.1038/nature02874. Bollman, K.M., Aukerman, M.J., Park, M.-Y., Hunter, C., Berardini, T.Z., Poethig, R.S., 2003. HASTY, the Arabidopsis ortholog of exportin 5/MSN5, regulates phase change and morphogenesis. Development 130, 1493–1504. Boulton, M.I., 2003. Geminiviruses: major threats to world agriculture. Ann. Appl. Biol. 142, 143, http://dx.doi.org/10.1111/j.1744-7348.2003.tb00239.x. Chapman, E.J., Prokhnevsky, A.I., Gopinath, K., Dolja, V.V., Carrington, J.C., 2004. Viral RNA silencing suppressors inhibit the microRNA pathway at an intermediate step. Genes Dev. 18, 1179–1186, http://dx.doi.org/10.1101/gad.1201204. Chellappan, P., Vanitharani, R., Fauquet, C.M., 2005. microRNA-binding viral protein interferes with Arabidopsis development. Proc. Natl. Acad. Sci. U. S. A. 102, 10381–10386, http://dx.doi.org/10.1073/pnas.0504439102. Clarke, J.D., 2009. Cetyltrimethyl Ammonium Bromide (CTAB) DNA Miniprep for Plant DNA Isolation. Cold Spring Harb Protoc, http://dx.doi.org/ 10.1101/pdb.prot5177. Dai, X., Zhao, P.X., 2011. psRNATarget: a plant small RNA target analysis server. Nucleic Acids Res. 39, W155–W159, http://dx.doi.org/10.1093/nar/gkr319. Dorner, S., Eulalio, A., Huntzinger, E., Izaurralde, E., 2007. Delving into the diversity of silencing pathways. Symposium on microRNAs and siRNAs: biological functions and mechanisms. EMBO Rep. 8, 723–729, http://dx.doi.org/10.1038/sj.embor.7401015. Du, Z., Zhou, X., Ling, Y., Zhang, Z., Su, Z., 2010. agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res. 38, W64–W70, http://dx.doi.org/10.1093/nar/gkq310. Emery, J.F., Floyd, S.K., Alvarez, J., Eshed, Y., Hawker, N.P., Izhaki, A., Baum, S.F., Bowman, J.L., 2003. Radial patterning of Arabidopsis shoots by class III HD-ZIP and KANADI genes. Curr. Biol. 13, 1768–1774. Ender, C., Meister, G., 2010. Argonaute proteins at a glance. J. Cell. Sci. 123, 1819–1823, http://dx.doi.org/10.1242/jcs.055210. Fabian, M.R., Sonenberg, N., Filipowicz, W., 2010. Regulation of mRNA translation and stability by microRNAs. Annu. Rev. Biochem. 79, 351–379, http://dx.doi.org/10.1146/annurev-biochem-060308-103103. Fahlgren, N., Howell, M.D., Kasschau, K.D., Chapman, E.J., Sullivan, C.M., Cumbie, J.S., Givan, S.A., Law, T.F., Grant, S.R., Dangl, J.L., Carrington,

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Frequent Occurrence of Tomato Leaf Curl New Delhi Virus in Cotton Leaf Curl Disease Affected Cotton in Pakistan.

Natural Occurrence of Tomato leaf curl New Delhi virus in Iranian Cucurbit Crops.

Genomic profiling of exogenous abscisic acid-responsive microRNAs in tomato (Solanum lycopersicum).

Identification and characterization of histone deacetylases in tomato (Solanum lycopersicum).

Comparative transcriptome profiling of a resistant vs. susceptible tomato (Solanum lycopersicum) cultivar in response to infection by tomato yellow leaf curl virus.

Genome-wide analysis of bHLH transcription factor and involvement in the infection by yellow leaf curl virus in tomato (Solanum lycopersicum).

Association of tomato leaf curl Sudan virus with leaf curl disease of tomato in Jeddah, Saudi Arabia.

Discovery of Novel Leaf Rust Responsive microRNAs in Wheat and Prediction of Their Target Genes.

Identification and Functional Characterization of a Tonoplast Dicarboxylate Transporter in Tomato (Solanum lycopersicum).

Molecular characterization and expression analysis of chloroplast protein import components in tomato (Solanum lycopersicum).

Molecular Evidence for Occurrence of Tomato leaf curl New Delhi virus in Ash Gourd (Benincasa hispida) Germplasm Showing a Severe Yellow Stunt Disease in India.

Molecular characterization of Chilli leaf curl virus and satellite molecules associated with leaf curl disease of Amaranthus spp.

Molecular characterization of fruit-specific class III peroxidase genes in tomato (Solanum lycopersicum).

Lysobacter lycopersici sp. nov., isolated from tomato plant Solanum lycopersicum.

Female-biased symbionts and tomato yellow leaf curl virus infections in Bemisia tabaci.

The Involvement of Heat Shock Proteins in the Establishment of Tomato Yellow Leaf Curl Virus Infection.

Replication of Tomato Yellow Leaf Curl Virus in Its Whitefly Vector, Bemisia tabaci.

Seed Transmission of Tomato yellow leaf curl virus in White Soybean (Glycine max).

No evidence of seed transmissibility of Tomato yellow leaf curl virus in Nicotiana benthamiana.

Synthesis and biological activity evaluation of novel amino acid derivatives as potential elicitors against Tomato yellow leaf curl virus.

Comparative Analyses of Tomato yellow leaf curl virus C4 Protein-Interacting Host Proteins in Healthy and Infected Tomato Tissues.

Mapping and introgression of a tomato yellow leaf curl virus tolerance gene, TY-1.

The role of corchorus in spreading of tomato yellow leaf curl virus on tomato in Jeddah, Saudi Arabia.

Tomato (Solanum lycopersicum L.) SlIPT3 and SlIPT4 isopentenyltransferases mediate salt stress response in tomato.