Research Article | Volume: 6, Issue: 6, Nov-Dec, 2018

Boron toxicity induces altered expression of miRNAs in French bean (Phaseolus vulgaris L.)

M. N. Jyothi S. Usha B. Suchithra T.K. Syeda Ulfath V.R. Devaraj R. Nagesh Babu   

Open Access   

Published:  Oct 20, 2018

DOI: 10.7324/JABB.2018.60601
Abstract

French bean (Phaseolus vulgaris L.), a legume grown all over the world, is an important pulse crop of India whose yield is affected by various biotic and abiotic stresses. Micro RNAs (miRNAs) have been shown to play an important role in the regulation of plant responses to several stresses. Boron toxicity is a significant limitation to cereal crop production. In this study, RT-qPCR confirmed seven miRNAs responsive to high concentrations of boron, exhibited differential expression trends compared with the control. Target prediction and their functional analysis showed that most of the miRNA targets represent transcription factors regulating expression of stress related genes. GO results supported our hypothesis that miRNAs were involved in diverse cellular processes, including plant circadian cycle, vegetative development, transcription, and cross adaptation. Our research characterized a subset of miRNAs that would facilitate understanding the regulatory mechanisms of small noncoding regulatory RNAs involved in stress tolerance.


Keyword:     Expression assay Metal toxicity MRE Transcription factors.


Citation:

Jyothi MN, Usha S, Suchithra B, Ulfath TKS, Devaraj VR, Babu RN. Boron toxicity induces altered expression of miRNAs in French bean (Phaseolus vulgaris L.). J App Biol Biotech. 2018;6(06):001-010. DOI: 10.7324/JABB.2018.60601

Copyright: Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.

HTML Full Text

1. INTRODUCTION

Boron toxicity is an important agronomic problem that limits crop productivity worldwide. Higher concentrations of boron might occur naturally (in the soil or in groundwater), or acquired by the soil due to activities such as mining, use of fertilizers, or through irrigation water. The permissible levels of Boron in irrigation water range from 0.3 mg per litre to 4 mg per litre [1]. The symptoms of boron toxicity are visible chlorotic/necrotic patches on leaves. Most of the studies have mainly concentrated on the toxic effects of heavy metals and macronutrients such as cadmium [2], lead [3,4], mercury [4], copper [5,6], arsenic [7] etc., However, limited research has concerned about the toxic effects of trace elements such as boron. Research on heavy metal toxicity regulations has been done extensively. The regulatory mechanism involves the intracellular metal chelation by a series of Cys-rich peptides [8]. Loss of function within the synergistic network employing metal transporters, chelators and sequesters is the basal cause of plant hypersensitivity. Transcriptional and post-transcriptional gene regulation is an important step in response to metal toxicity or metal deficiency and there exists large lacunae in understanding plant metal homeostasis. With the advent of high throughput sequencing, small RNA mediated gene regulations has gained prominence in unravelling the critics of plant gene regulatory mechanism. MicroRNAs, a class of small non-coding RNAs that regulate mRNA level by either vitiating target mRNA or attenuating its translation. Several investigations escalated the number of miRNAs identified and demonstrated their crucial role in various plant metabolic functions. Many studies reported the involvement of miRNAs in various biological processes such as cell cycle, cell death, foral development and several physiological responses [9]. Increasing evidences also emphasized the influence of various biotic and abiotic stress factors on expression of miRNAs [10]. Through microarray, Ding et al. reported the altered expression of miR168, miR528, and miR162 under cadmium stress and defined their targeted genes were involved in biogenesis of miRNAs [11]. They also determined the miRNA genes possessed MREs (metal responsive cis-regulatory elements) in their upstream sequences which control the expression of these miRNAs. The overexpression of miR192 caused due to increased cadmium concentrations retarded the seed germination and seedling growth in rice [12]. Ozhuner et al. demonstrated the elevated levels of boron induced over expression of miR156d, miR171a, miR397, and miR444a in leaves which were not detected in root while, miR172, miR399, miR2021, miR5053 and miR5066 were expressed in both leaf and root [13]. This exemplifies the tissue-specific expression of miRNAs under stress.

French bean is an important legume crop cultivated for its seeds and pods. The crop yield is found to be afected by drought, high salt, temperature and availability of essential nutrients. Many groups involved in identification and characterization miRNAs under various abiotic stress conditions. Arenas-Huertaro et al. proposed the differential expression of miR2118, miR159a, miR1514a, miR482, miR2119, miR166a, miR319c, and miR399a would render plant tolerant towards drought and salt stress [14]. Valdes-Lopez et al. through next-generation sequencing confirmed the miRNAs respond stress specifically and demonstrated miR399 is crucial in phosphate uptake and acts as a signaling molecule during phosphate starvation [15]. Pelaez et al. exploited high throughput sequencing to characterize the complete set of miRNAs responded to salt stress [16]. Naya et al. through expression analysis functionally characterized the role of miR398b in copper homeostasis. miR398b targets Cu/Zn superoxide dismutase, which was also determined to target stress up-regulated Nodulin 19 in root, nodules, and leaves of French bean [6]. Although extensive research substantiates the key role of miRNAs in various stress responses, resource against the boron stress-responsive miRNAs in legumes specifically French bean is scanty.

Our study aims to characterize putative miRNAs from boron exposed French bean seedlings. To understand the impact of boron toxicity on the expression of conserved stress-responsive miRNAs, RT-qPCR studies were carried out. Further, B responsive miRNAs were cloned and sequenced. Functional analysis and Gene Ontology (GO) study revealed that miRNAs affect plant circadian cycle and vegetative development. We anticipate our effort would provide new insights into the metal stress regulatory pathways acting in plant metal homeostasis.


2. MATERIALS AND METHODS

2.1. Plant Materials and Stress Treatment

Surface sterilized French bean seeds were grown under controlled conditions at 28°C day/25°C night with 12 h photoperiod. 6 days old seedlings were hydroponically treated with 5 ppm of boric acid, in half strength Hoagland medium for 48 h and a group of seedlings was maintained as control [17]. Post-stress treatment, both test, and control tissues were stored at -80°C until further analysis.

2.2. RT-qPCR Validation of miRNA Expression

To evaluate the stress-induced expression of miRNAs and validate the effect of boron toxicity, RT-qPCR was performed using stress responsive miRNA family specific primers designed based on the available literature and SYBR Green PCR Master mix (Takara) on Light cycler 96® (Roche). Total RNA was isolated from the stressed and control samples and cDNA was synthesized using Universal Reverse primer and MMLV Reverse transcriptase (Invitrogen, USA). Each PCR reaction (20 μl) included 2 μl cDNA, 10 μl SYBR Green Master mix, 1 μl sequence- specific forward primer (10 μM), 1 μl Universal reverse primer (10 μM) and 6 μl sterile water (Supplementary file 1). The reactions were performed for 10 min at 95°C followed by 40 cycles of 95°C for 15 s and 60°C for 1 min with a final extension 72°C for 30 s, with two biological replicates; the data were analyzed based on effciencies and fold changes. The reactions were carried as described earlier and signals were normalized against U6 snoRNA and the relative expressions were calculated using the 2-ΔΔCT method and the fold changes were determined by Fold change = log2 ratio of normalized expression of miRNAs under stress to that of control samples. Statistics software GraphPad Prism v.5 was used to obtain standard deviations of the data obtained from the three independent experiments with student’s t-test (p < 0.05).

2.3. Characterization of Boron Responsive miRNAs

To define the miRNA sequence of boron stress-responsive miRNAs, RT-qPCR products were subsequently cloned and sequenced. 50 μl of RT-qPCR product was purified by Nucleospin DNA Cleanup kit (Takara, Japan) according to manufacturer’s instructions. The PCR products were subjected to gel purification and products were ligated to pGEMT Easy vector system I (Promega). Cloning was performed with 5 μl of 2X Rapid Ligation Buffer, 1 μl of pGEM-T Easy Vector (50 ng), and 1 μl of T4 DNA ligase (3 units/μl) was added to the purified and quantified RT-qPCR products in a final volume of 10 μl at 4°C overnight and transformed into E. coli (DH5α). The colony PCR was carried out using gene-specific primers and the PCR positive clones were sequenced and processed for BLAST analysis.

2.4. Data Analysis

Rfam database (http://www.sanger.ac.uk/Software/Rfam) was used to check the sequences for tRNA/rRNA contamination. BLASTN search against French bean genome was performed to identify putative origins. The sequences with perfect (0-3) matches with small RNA sequences were used for fold back secondary structure prediction with MFOLD [18]. A segment was considered a valid miRNA candidate if its secondary structure met the criteria according to Meyers et al. [19].

2.5. Prediction of Potential Target mRNAs

Target prediction for the miRNAs was based on the principle of nearly perfect complementation between the miRNA and target mRNAs. The identified conserved and putative novel miRNAs were all submitted for target gene prediction using psRNATarget (http://plantgrn.noble.org/psRNATarget) [20]. French bean transcript sequences downloaded from Phytozome version 9 (www.phytozome.net) [21] were used to predict the putative targets with default parameters. Sequences with a score of less than 4 were regarded as miRNA target genes. On the basis of their functions putative targets were classified using Gene Ontology (GO) annotations from Blast2Go [22].

2.6. Characterization of miR Genes

The determination of MIR regulatory motifs and TSS (Transcription Start Site) prediction was carried out as described previously [23]. Briefy, 2 kb upstream sequences were retrieved at the beginning of the pre-miRNA for the prediction of TSS for all the types of miRNA (genic and intergenic). The TSS and TATA-box predictions were made using TSSP web tool (http://linux1.softberry.com) Putative promoter sequences from -1,000 to -1 from the TSS were retrieved for all classes of miRNA and used for motif search and identification of strong motifs.

2.7. Scanning for Transcription Factor Binding Motifs (TFBs)

The candidate miRNA genes were scanned for putative TFBs using two modules (1) PLACE (Plant cis-acting regulatory DNA elements) signal scan search, to identify the known cis-regulatory elements (http://www.dna.afrc.go.jp/PLACE/signalscan.html) and (2) MELINA-II (http://melina2.hgc.jp/public/index.html) [24]. We have used four algorithms to predict the motifs (1) Consensus, (2) Gibbs Sampler, (3) MD SCAN (with default parameters) and (4) MEME (with a cut-of E-value of 1 with anr (any number of repetitions) mode). The motifs identified by at least two programs were considered as strong motifs. The motifs which were not detected in PLACE database were considered as unique (novel) motifs. To interpret the genomic locations of miRNA genes, the genomic coordinates of pre-miRNAs were overlapped at the transcript genomic region.

1.8. 5’-RACE Validation of Target Genes

The cDNA was amplified with mirRacer 5’ primer (5’- GGACACTGACATGGACTGAAGGAGTA-3’) and mirRacer 3’ primer (5’-ATTCTAGAGGCCGAGGCGGCCGACATG-3’) to generate a pool of non-gene-specific product. These miRacer primers are complementary to the 5’ and 3’ adaptors, respectively. The conditions used for the amplification were carried out for 25 cycles at a final annealing temperature of 60°C. 5’ miR-RACE reactions were performed with the mirRacer 5’ primer and miRNA-gene-specific forward and reverse primers and a piece of Poly(T) and 5’ adaptor. In each case, a unique gene-specific DNA fragment was amplified. After the amplification, the 5’ RACE PCR products were separated in a 2.5% agarose gel with ethidium bromide staining. The gel slices containing DNA with a size of about 60 bp were excised and the DNA fragments were purified using an agarose gel DNA purification kit (Takara, Japan), according to the manufacturer’s instructions. The DNA fragment was directly sub-cloned with the pGEMT Easy vector (Promega). Colony PCR was performed using the PCR specific primer pairs as above. The 5’ RACE clones with PCR products were sequenced.


3. RESULTS

3.1. Validation of miRNA Expression Using Stem-Loop RT-qPCR

To determine the infuence of boron toxicity, the RT-qPCR was carried out using family-specific primers for sixteen miRNA families reported as responsive to abiotic stress (Supplementary fg 1). We found most of the miRNAs are sensitive to the elevated boron concentration. However, we observed noticeable fold changes among seven miRNAs, miR396, miR398, miR812, miR845, miR2593, miR2608 and miR2905 (Figure 1A) with an average up-regulation by 2 folds. Higher boron availability down-regulated miR398 and miR2608 by 1.75, and 2.7 folds respectively. miR2593 was found to be highly induced by 3 folds and miR845 by 2.6 folds. miR396, miR812 and miR2905 were induced by 1.5, 1.7 fold and 1 fold respectively (Figure 1B).

Figure 1: Expression analysis of boron stress-responsive miRNAs in French bean. A. Relative expression of miRNAs was studied using RT-qPCR. The expressions are normalized against U6. The study was carried in three independent experiments and Standard deviations and t-test were performed with p < 0.05. B. Fold change of respective miRNAs compared to control sample.

[Click here to view]

3.2. Determination of Boron Stress-Responsive miRNAs in French Bean

To determine the sequences of boron stress-responsive miRNAs, the RT-qPCR products were cloned and plasmids were sequenced. The filtered sequences were analysed further for putative miRNA and their precursors. The sequences of length 18-27 nt were mapped to French bean genome to obtain their true precursors forming stable secondary structure with MFEI values >0.85. The length of the miRNA sequences ranged from 19–24 nt and majority of the sequences starts with A, characteristic of plant miRNAs. The length of the precursor sequences varied from 230-330 with high MFE. The MFEI validates the identified pri-miRNA structure. Table 1 describes the genomic location and MFEI values of miRNAs found sensitive to boron toxicity.

Table 1: Characteristics of miRNAs responded to Boron toxicity French bean.

[Click here to view]

3.3. Computational Prediction of Putative Targets and Their Annotations

To obtain a complete view of the mode of action of boron stress-responsive miRNAs, the target prediction was performed using psRNATarget with French bean transcriptome as background database with default parameters. The target hits were annotated to define their functions using Blast2Go (Supplementary file 2). Our results showed most of the targets were involved in biological process maintaining cellular homeostasis. Majority of them were transcription factors which include GRF, MYB, GATA, BTB-TAZ and NEP Interacting Protein 2 and RNA binding RINT1/TIP. Genes involved in biological function include aspartyl amino peptidase, hydrolase, lipoyltransferase, glucouronosyl transferase, etc. (Table 2). Gene ontology studies also reported the miRNA targets were exclusively involved in plant growth and development as most of the targets exhibited enzyme regulation during reproduction and developmental stages, 8 of which exhibited nucleotide binding, 10 were involved in metabolic functions and 13 engaged in cellular processes (Figure 2).

Table 2: Description of genes targeted by boron stress induced conserved miRNAs in French bean.

[Click here to view]

Figure 2: Functional analysis of Boron stress-responsive miRNAs. miRNA function was predicted by defning the biological function of its target gene. The GO analysis of targets was performed using Blast2Go and BGI WEGO. The graph represented is obtained from BGI-WEGO.

[Click here to view]

3.4. Functional Characterization of miRNA Genes

To gain further insights towards the genomic organization of boron stress- responsive miRNAs, computational search for putative TSS, TATABOX, and TFB motifs and polyA hangs were carried out. For the analysis 2 kb, upstream and downstream sequences from the precursor start site was obtained and processed. The identified miRNA genes exhibited the TSS around -600 to -300 from the pri-miRNA start site and their respective TATA box was found -35 to their TSS. However, we could not define the TSS and TATA box for miR396 and can be considered under the category of TATA less genes. Majority of the miRNA genes exhibited polyA strings at +900 from pri-miRNA endpoint. Further, to determine the TFB motifs surrounding the TSS and regulating the miRNA gene expression we employed MELINA II and NSITE algorithms and description of the motifs were defined with PlantCare and PLACE database. The motifs which were found in all the fve algorithms was considered as putative TFB of the given miRNA gene. And we noticed MYC/MYB, WRKY and MRE binding sites were commonly found in all miRNA genes. Other motifs include ABRE, DREB, HD-ZIP, bHLH domains (Table 3). The identified TFB motifs were known to involve in tissue and organ development, stress responses including abiotic cues such as light, metal uptake, nutrient absorption and phytohormone signalling. These results suggest the upstream regions up to 2 kb were crucial in miRNA gene expression.

Table 3: Genomic features of novel miRNAs expressed under boron stress in French bean.

[Click here to view]

3.5. Validation of Target Genes

High complementarity of the miRNA with their target genes results in their degradation. To validate the targets in order to achieve reduce false negatives among the computationally predicted targets, the RLM-RACE was performed. We found most of the perfect complementary pairs produced target cleavage at position 10, with the cleavage site preceded by nucleotide sequence of XXA/CCXX (where X represents any nucleotide) (Figure 3). Perfect binding with ‘seed sequence’ and the complementary target sequence obeys the principle of 5’ seed sequence alignment which further establishes the guanine targets with specific cleavage sites.

Figure 3: Validation of miRNA target genes. The target sequences of the miRNAs were validated for the specific cleavage sites by RLM-RACE and found most of the miRNA mediated target cleavage at position 10.

[Click here to view]


4. DISCUSSION

Recently, microRNAs were established as key gene regulators and many studies evidenced role in various plant physiological processes including plant stress responses. Studies revealed the altered climatic conditions infuence the expression patterns of miRNAs thereby encouraging the modulated expression of genes to acquaint stress tolerance [25]. In this view, we aimed to define the infuence of boron toxicity on French bean in terms of altered expression of conserved stress-responsive miRNAs. We studied the expression of sixteen miRNAs under B toxic conditions out of which seven miRNAs found to be crucial. However, of the seven miRNAs responded only two (miR396 and miR398) were conserved candidates and rest (miR812, miR845, miR2593, miR2608, miR2905) forms a group of non-conserved stress-responsive miRNAs. The qPCR studies clearly demonstrated the average fold change as 70% up-regulation with respect to control.

Further the functional annotation of miRNAs responded to boron toxicity can be performed by defining the role of their target genes. The computational prediction of target genes was carried out using psRNA Target and the functional annotation was performed using Blast2GO. Our results revealed most of the targets are transcription factors as targets of conserved miRNAs and metabolic enzymes form the targets of non-conserved miRNAs. We observed down-regulation of miR398 and miR2608 the miRNAs targeting GATA transcription factors and hydrolases. GATA family transcription factors forms large group of zinc finger proteins which bind to GATA domain and involved in development, nitrogen metabolism [26], plant circadian regulation and light-regulated photomorphogenesis [27]. In plants GATA TFs are demonstrated to regulate light-sensitive genes and involve in plant circadian cycle [28,29]. Overexpression of GmGATA44 in Arabidopsis affected Chlorophyll biosynthesis under low nitrate conditions [30]. Luo et al. demonstrated the GATA TFs forms key signaling molecules in light integrated brassinosteroid pathways [31]. In Arabidopsis, miR398 was found to target two closely related Cu/Zn superoxide dismutase coding genes, cytosolic CSD1 and chloroplastic CSD2, and a reduced level of miR398 led to improved tolerance of transgenic lines compared with the wild-type plants under oxidative stress conditions [32]. It is evidenced as miR398 is signifcantly involved in maintaining plant metal homeostasis. The excess copper resulted in down-regulation of miR398 resulting in accumulation of its targets CSD1 and CSD2 and aid to scavenge the ROS thus generated [33]. Conversely, under copper deficiency, miR398 is induced down-regulating the CSDs, ROS scavenging is taken are by FSD1 which acts in concert with miR156. Suppression of miR156 under copper deficiency results in accumulation of SPL proteins. SPL proteins binds to GTAC motif of FSD1 and CSD2 simultaneously inducing and suppressing the expression of genes respectively [34]. Tissue-specific expression trend was observed with miR398 in excess zinc exposed Arabidopsis. It is noticed that, the transcripts of miR398b/c are reduced in leaves but no response in roots [8]. Similarly, in the French bean, leaves exhibited lowered miR398b transcripts while it is accumulated in roots and nodules during manganese toxicity [14]. This implies down-regulation of miRNAs would render plant to activate ROS scavengers as excess production of ROS is a consequence of boron toxicity and disturbs photosynthetic activity [35].

The up-regulated miRNAs constitute to target TF genes crucial in plant growth and cellular proliferation. Induction of miR396 resulted in degradation of GRFs, the plant-specific TFs essential for cell proliferation and growth [9]. It is shown that miR396 expression is induced in majority of stress conditions such as drought, salinity, nutrient depletion, and metal toxicity. Drought influences up-regulation of miR396 in Arabidopsis, Rice, Medicago, and Cotton [36]. Similarly, salt stress and severe cold conditions also induce miR396 expression [37] while hypoxic conditions were shown to repress miR396 [38]. It is imperative that the miRNA is responsive to various environmental stimuli and modulates plant stress responses and proposed key causes of plant retarded growth and impaired development. The other development specific TF targeted by boron responsive miRNA includes MYB (miR845) and BTB & TAZ domain proteins (miR812). Further, it emphasis the MYB would play major role in stress response.

Some MYBs are involved in the regulation of cell proliferation, differentiation, and apoptosis and determine the fate of plant cells. Studies report MYB as a conserved target of miR159. Differential expression of miR159 is observed in rice exposed to drought where it is repressed by two-fold, evidences the species-specific nature of miRNA expression [39]. The miR159-MYB101 network was established to be essential for modulation of vegetative growth whilst controlling the salt stress-induced premature transition to reproductive phase in potato [40]. miR159 forms the key enzyme involved in hormonal signaling. The expression of miRNA is altered during ABA treatment where it accumulates in germinating seeds leading to desensitization due to reduced MYB33 and MYB101 levels, the TFs essential for hormonal signaling [41].

BTB and TAZ domain proteins are cleaved by miR812 under elevated boron levels. BTB-TAZ forms member of Calmodulin-binding proteins and are engaged in plant signaling pathways [42]. Recent evidence implies their role in gametophyte format ion in Arabidopsis, light signals, nutrient status and hormonal responses [43]. The TFs are induced under no light conditions and involve in sugar-mediated inhibition of germination. Overexpression of BT2, a BTB-TAZ protein, potentiates auxin responses in postgermination and vegetative development stages [44].

The other genes identified as putative targets of boron responsive miRNAs include aminopeptidases, hydrolases, lipoyl transferase, glucosyl transferases etc., and are actively involved in cellular metabolism. Thus, we proclaim, boron toxicity symptoms such as chlorosis, retarded growth, reduced biomass etc., are due to impaired expression of metabolic enzymes that are crucial for maintaining sugar/ lipid homeostasis. This signifies the determination of non-conserved miRNAs in the boron stressed small RNA library as they regulate the metabolic process to achieve cellular homeostasis at a faster rate and forms the front line of defense. Thus we infer, the altered expression of conserved stress-responsive miRNAs may engage in regulating stress responses at basal level and confers long-lasting tolerance mechanisms to evolve; while, the diferential expression of non-conserved miRNAs are crucial in stress recovery pathways by altering the metabolome of the plant thus ofering an immediate response to environmental stimuli.

The dynamics of miRNA expression can be thoroughly understood by gaining insights to genomic organization of miRNA genes. Featuring of miR genes in terms of TSS, TATA position and regulatory motifs would aid in defining regulation of MIR transcription and it is modulated by the function of cis-acting regulatory elements. Differential expression analysis of miRNAs facilitates the elucidation of promoter motifs. The traditional global TSS mapping and their delineation of pri-miRNAs challenges have led to a limited number of miRNAs TSSs identified till date [45].

Conserved motifs -35 positions adjoining to TSS suggest French bean miRNAs were transcribed by RNA pol II and same promoter coding sequence, in addition, similar kind of results were obtained in Arabidopsis and fax. miR396 termed as TATA-less (promoters) is further considered as housekeeping gene. Forty-one distinct transcription factor binding sites were found to be unique among 110 that were identified. MYB and WRKY dwells the majority of the regulatory motifs and may play a vital role in transcribing miRNA involved in stress responses. Of which MYB represents cross-talk between disease and drought response mechanisms and modulated by a negative feedback loop that buffers small changes in the level of their mRNAs [46]. In this study, most of the miRNAs were annotated which use their own transcription initiation regions and a few members appear to be shared with host genes, suggests the alternative post-transcriptional events would determine the fate of each primary transcript. It also reveals the existence and positional preferences of intronic miRNA, TSSs, and evolutionary relationship. Further, functional characterization of transcription factors and their validation will decipher us to explore the detailed miRNA-mediated gene regulatory network during abiotic stress conditions.


5. CONCLUSIONS

The study investigates small RNAs in French bean associated with boron toxicity and is the first of its kind. The expression patterns of seven miRNAs were studied and their putative target genes were predicted and annotated by GO, to explore gene functions. The majority of the identified miRNAs were significantly responsive to boron stress. The functional analysis confirmed the putative miRNAs engaged in a network of growth and plant stress signaling. This study will provide useful information to deepen the understanding of the function and regulatory mechanisms of miRNAs in boron toxicity.


6. CONFLICT OF INTEREST

Authors declare no conflict of interest.


7. ACKNOWLEDGMENTS

The research is funded by University Grants Commission, New Delhi, India [40- 192/2011 (SR)].


8. REFERENCES

1. Keren R, Bingham FT. Boron in water, soils, and plants. Adv Soil Sci 1985; 1:230-276.

2. Xu L, Wang Y, Zhai L, et al. Genome-wide identification and characterization of cadmium-responsive microRNAs and their target genes in radish (Raphanus sativus L.) roots. J Exp Bot 2013; 64:4271-4287. Crossref

3. He Q, Zhu S, Zhang B. MicroRNA-target gene responses to lead-induced stress in cotton (Gossypium hirsutum L.) Funct Integr Genomics 2014; 14:507-15. Crossref

4. Wang Y, Zhao Z, Deng M, Liu R, Niu S, Fan G. Identification and Functional Analysis of MicroRNAs and Their Targets in Platanus acerifolia under Lead Stress. Int J Mol Sci 2015; 16:7098-7111. Crossref

5. Zhou ZS, Zeng HQ, Liu ZP, Yang ZM. Genome-wide identification of Medicago truncatula microRNAs and their targets reveals their diferent regulation by heavy metal. Plant Cell Environ 2012; 35:86-99. Crossref

6. Naya L, Paul S, Valdes-Lopez O, Mendoza-Soto AB, Nova-Franco B, et al. Regulation of copper homeostasis and biotic interactions by microRNA 398b in common bean. PLoS ONE 2014; 9:e84416. Crossref

7. Srivastava S, Srivastava AK, Suprasanna P, D’Souza SF. Identification and profiling of arsenic stress-induced microRNAs in Brassica juncea. J Exp Bot 2013; 64:303-315. Crossref

8. Gielen H, Remans T, Vangronsveld J, Cuypers A. MicroRNAs in Metal Stress: Specific Roles or Secondary Responses? Int J Mol Sci 2012; 13:15826-15847. Crossref

9. Jones-Rhoades MW, Bartel DP, Bartel B. MicroRNAs and their regulatory roles in plants. Annual Review of Plant Biology 2006; 57:19-53. Crossref

10. Khraiwesh B, Zhua JK, Zhuc J. Role of miRNAs and siRNAs in biotic and abiotic stress responses of plants. Biochemica et Biophysica Acta 2012; 1819:137-148. Crossref

11. Ding Y, Chen Z, Zhu C. Microarray-based analysis of cadmium-responsive microRNAs in rice (Oryza sativa). J Exp Bot 2011; 62:3563-3573. Crossref

12. Ding Y, Qu A, Gong S, Huang S, Lv B, Zhu C. Molecular identification and analysis of Cd-responsive microRNAs in rice. J Agric Food Chem 2013; 61:11668-11675. Crossref

13. Ozhuner E, Eldem V, Ipek A, Okay S, Sakcali S, Zhang B, et al. Boron Stress Responsive MicroRNAs and Their Targets in Barley. PLoS ONE 2013; 8(3):e59543. Crossref

14. Arenas-Huertero CB, Rabanal F, Blanco-Melo D, De la Rosa C, Estrada-Navarrete G, Sanchez F, Covarrubias AA, Reyes JL. Conserved and novel miRNAs in the legume Phaseolus vulgaris L. in response to stress. Plant Mol Biol 2009; 70:385-401. Crossref

15. Valdes-Lopez O, Yang SS, Aparicio-Fabre R, Graham PH, Reyes JL, Vance CP, Hernandez G. MicroRNA expression profile in common bean (Phaseolus vulgaris L.) under nutrient deficiency stresses and manganese toxicity. New Phytol 2010; 187:805-818. Crossref

16. Pelaez P, Trejo MS, Iniguez LP, Estrada-Navarrete G, Covarrubias AA, Reyes JL, Sanchez F. Identification and characterization of microRNAs in Phaseolus vulgaris L. by high-throughput sequencing. BMC Genomics 2012; 13:83. Crossref

17. Hoagland and Arnon. The water-culture method for growing plants without soil. University of California, College of Agriculture, Agricultural Experiment Station 1950.

18. Zuker M. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 2003; 31:3406. Crossref

19. Meyers BC, Axtell MJ, Bartel B, Bartel DP, Baulcombe D, Bowman JL, et al. Criteria for annotation of plant micro RNAs. Plant Cell 2008; 20:3186-3190. Crossref

20. Xinbin Dai, Zhao PX. psRNATarget: A Plant Small RNA Target Analysis Server. Nucleic Acids Research 2011; 39:W155-W159. Crossref

21. Goodstein DM, Shu S, Howson R, et al. Phytozome: a comparative platform for green plant genomics. Nucleic Acids Research 2012; 40:D1178-D1186. Crossref

22. Conesa A, Stefan Gotz. 2008. Blast2GO: A Comprehensive Suite for Functional Analysis in Plant Genomics International Journal of Plant Genomics 2008; Article ID 619832, 1-12.

23. Jyothi MN, Rai DV, Nagesh Babu R. Identification and characterization of high temperature stress responsive novel miRNAs in French bean (Phaseolus vulgaris L.). Appl Biochem Biotechnol 2015. doi. 10.1007/ S12010-015-1614-2. Crossref

24. Okumura T, Makiguchi H, Makita Y, Yamashita R. Nakai K. Melina II: a web tool or comparisons among several predictive algorithms to find potential motifs from promoter regions. Nucleic Acids Res 2007; 35:227-231. Crossref

25. Contreras-Cubas C, Palomar M, Arteaga-Vázquez M, Reyes JL, Covarrubias AA. Non-coding RNAs in the plant response to abiotic stress. Planta 2012; 236:943-58. Crossref

26. Bi YM, Zhang Y, Signorelli T, Zhao R, Zhu T, Rothstein S. Genetic analysis of Arabidopsis GATA transcription factor gene family reveals a nitrate-inducible member important for chlorophyll synthesis and glucose sensitivity. The Plant Journal 2005; 44:680-692. Crossref

27. Arguello-Astorga G, Herrera-Estrella L. Evolution of light-regulated plant promoters. Annu Rev Plant Physiol Plant Mol Biol 1998; 49:525-555. Crossref

28. Manfield IW, Devlin PF, Jen CH, Westhead DR, Gilmartin PM. Conservation, convergence, and divergence of light-responsive, circadian-regulated, and tissue-specific expression patterns during evolution of the Arabidopsis GATA gene family. Plant Physiol 2007; 143:941-958. Crossref

29. Jeong MJ, Shih MC. Interaction of a GATA factor with cis-acting elements involved in light regulation of nuclear genes encoding chloroplast glyceraldehyde-3-phosphate dehydrogenase in Arabidopsis. Biochem Biophys Res Commun 2003; 300:555-562. Crossref

30. Zhang C, Hou Y, Hao Q, Chen H, Chen L, Yuan S, et al. Genome-wide survey of the soybean gata transcription factor gene family and expression analysis under low nitrogen stress. PLoS ONE 2015; 10:e0125174. Crossref

31. Luo XM, Lin WH, Zhu S, Zhu JY, Sun Y, Fan XY, et al. Integration of light and brassinosteroid-signaling pathways by a GATA Transcription Factor in Arabidopsis. Dev Cell 2010; 19:872-883. Crossref

32. Jagadeeswaran G, Saini A, Sunkar R. Biotic and abiotic stress down-regulate miR398 expression in Arabidopsis. Planta 2009; 229:1009-1014. Crossref

33. Yamasaki H, Abdel-Ghany SE, Cohu CM, Kobayashi Y, Shikanai T, Pilon M. Regulation of copper homeostasis by micro-RNA in Arabidopsis. J Biol Chem 2007; 282:16369-16378. Crossref

34. Yamasaki H, Hayashi M, Fukazawa M, Kobayashi Y, Shikanai T. SQUAMOSA promoter binding protein-like7 is a central regulator for copper homeostasis in Arabidopsis. Plant Cell 2009; 21:347-361. Crossref

35. Nagesh babu R, Jyothi MN, Sharadamma N, Devaraj VR. Changes in antioxidative and photosynthetic properties system of French bean (Phaseolus vulgaris L.) to boron toxicity. Journal of Agricultural and Biological Science 2012; 7(11):892-898.

36. Jones-Rhoades MW, Bartel DP. Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol Cell 2004; 14(6):787-99. Crossref

37. Zhou X, Wang G, Sutoh K, Zhu JK, Zhang W. Identification of cold-inducible microRNAs in plants by transcriptome analysis. Biochimica et Biophysica Acta 2008; 1779:780-788. Crossref

38. Moldovan D, Spriggs A, Yang J, Pogson BJ, Dennis ES, Wilson IW. Hypoxia- responsive microRNAs and trans-acting small interfering RNAs in Arabidopsis. J Exp Bot 2009; 61:165-177. Crossref

39. Zhou L, Liu Y, Liu Z, Kong D, Duan M, Luo L. Genome-wide identification and analysis of drought-responsive microRNAs in Oryza sativa. J Exp Bot 2010; 61:4157-4168. Crossref

40. Ai Kitazumi, Yoshihiro Kawahara, Ty S. Onda, David De Koeyer, Benildo G. de los Reyes. Implications of miR166 and miR159 induction to the basal response mechanisms of an andigena potato (Solanum tuberosum subsp. andigena) to salinity stress, predicted from network models in Arabidopsis. Genome 2015; 58:13-24. Crossref

41. Tuteja N. Abscisic Acid and Abiotic Stress Signaling. Plant Signaling & Behavior 2007; 2:135-138. Crossref

42. Du L, Poovaiah BW. A novel family of Ca2+/calmodulin-binding proteins involved in transcriptional regulation: interaction with fish/ Ring3 class transcription activators. Plant Mol Biol 2004; 54:549-569. Crossref

43. Robert HS, Quint A, Brand D, Vivian-Smith A, Offringa R. BTB and TAZ DOMAIN scaffold proteins perform a crucial function in Arabidopsis development. Plant J 2009. 58:109-121. Crossref

44. Mandadi KK, Misra A, Ren S, McKnight TD. BT2, a BTB Protein, Mediates Multiple Responses to Nutrients, Stresses, and Hormones in Arabidopsis. Plant Physiology 2009; 150(4):1930-1939. Crossref

45. Cui X, Xu SM, Mu DS, Yang ZM. Genomic analysis of rice microRNA promoters and clusters. Gene 2009; 431:61-66. Crossref

46. Guleria P, Mahajan M, Bhardwaj J, Yadav SK. Plant Small RNAs: Biogenesis, Mode of Action and Their Roles in Abiotic Stresses. Genomics, Proteomics and Bioinformatics 2011; 9:183-199. Crossref

Supplementary File 1: List of Primers.

[Click here to view]

Supplementary File 2: Description of targets predicted by psRNATarget and their gene ontology descriptions.

[Click here to view]

Supplementary Fig. 1: Expression analysis of conserved stress responsive miRNAs under Boron toxicity in French bean. To analyse the impact of boron toxicity on expression patterns of miRNAs, 16 stress responsive miRNAs was selected and their expression trends were studied by RT-qPCR. The data was normalized against U6 gene.

[Click here to view]
Reference

1. Keren R and Bingham FT. Boron in water, soils, and plants. Adv Soil Sci. 1985; 1: 230-276.

2. Xu L, Wang Y, Zhai L, et al. Genome-wide identification and characterization of cadmium-responsive microRNAs and their target genes in radish (Raphanus sativus L.) roots. J Exp Bot. 2013; 64:4271-4287. https://doi.org/10.1093/jxb/ert240

3. He Q, Zhu S, Zhang B. MicroRNA-target gene responses to lead-induced stress in cotton (Gossypium hirsutum L.) Funct Integr Genomics. 2014; 14:507-15. https://doi.org/10.1007/s10142-014-0378-z

4. Wang Y, Zhao Z, Deng M, Liu R, Niu S, Fan G. Identification and Functional Analysis of MicroRNAs and Their Targets in Platanus acerifolia under Lead Stress. Int J.Mol Sci. 2015; 16:7098-7111. https://doi.org/10.3390/ijms16047098

5. Zhou ZS, Zeng HQ, Liu ZP, Yang ZM. Genome-wide identification of Medicago truncatula microRNAs and their targets reveals their different regulation by heavy metal. Plant Cell Environ. 2012; 35: 86-99. https://doi.org/10.1111/j.1365-3040.2011.02418.x

6. Naya L, Paul S, Valdes-Lopez O, Mendoza-Soto AB, Nova-Franco B, et al. Regulation of copper homeostasis and biotic interactions by microRNA 398b in common bean. PLoS ONE. 2014; 9:e84416. https://doi.org/10.1371/journal.pone.0084416

7. Srivastava S, Srivastava AK, Suprasanna P, D'Souza SF. Identification and profiling of arsenic stress-induced microRNAs in Brassica juncea. J Exp Bot. 2013; 64:303-315. https://doi.org/10.1093/jxb/ers333

8. Heidi Gielen, Tony Remans, Jaco Vangronsveld, Ann Cuypers. MicroRNAs in Metal Stress: Specific Roles or Secondary Responses? Int. J. Mol. Sci. 2012; 13: 15826-15847. https://doi.org/10.3390/ijms131215826

9. Jones-Rhoades MW, Bartel DP, Bartel B. MicroRNAs and their regulatory roles in plants. Annual Review of Plant Biology. 2006; 57:19-53. https://doi.org/10.1146/annurev.arplant.57.032905.105218

10. Khraiwesh B, Zhua JK, Zhuc J. Role of miRNAs and siRNAs in biotic and abiotic stress responses of plants. Biochemica et Biophysica Acta. 2012; 1819:137-148. https://doi.org/10.1016/j.bbagrm.2011.05.001

11. Ding Y, Chen Z, Zhu C. Microarray-based analysis of cadmium-responsive microRNAs in rice (Oryza sativa). J Exp Bot. 2011; 62: 3563-3573. https://doi.org/10.1093/jxb/err046

12. Ding Y, Qu A, Gong S, Huang S, Lv B, Zhu C. Molecular identification and analysis of Cd-responsive microRNAs in rice. J Agric Food Chem. 2013; 61:11668-11675. https://doi.org/10.1021/jf401359q

13. Ozhuner E, Eldem V, Ipek A, Okay S, Sakcali S, Zhang B et al. Boron Stress Responsive MicroRNAs and Their Targets in Barley. PLoS ONE. 2013; 8(3): e59543. https://doi.org/10.1371/journal.pone.0059543

14. Arenas-Huertero CB, Rabanal F, Blanco-Melo D, De la Rosa C, Estrada-Navarrete G, Sanchez F, Covarrubias AA, Reyes JL. Conserved and novel miRNAs in the legume Phaseolus vulgaris L. in response to stress. Plant Mol Biol. 2009; 70:385-401. https://doi.org/10.1007/s11103-009-9480-3

15. Valdes-Lopez O, Yang SS, Aparicio-Fabre R, Graham PH, Reyes JL, Vance CP, Hernandez G. MicroRNA expression profile in common bean (Phaseolus vulgaris L.) under nutrient deficiency stresses and manganese toxicity. New Phytol. 2010; 187:805-818. https://doi.org/10.1111/j.1469-8137.2010.03320.x

16. Pelaez P, Trejo MS, Iniguez LP, Estrada-Navarrete G, Covarrubias AA, Reyes JL, Sanchez F. Identification and characterization of microRNAs in Phaseolus vulgaris L. by high-throughput sequencing. BMC Genomics. 2012; 13:83. https://doi.org/10.1186/1471-2164-13-83

17. Hoagland and Arnon. The water-culture method for growing plants without soil. University of California, College of Agriculture, Agricultural Experiment Station. 1950.

18. Zuker M. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 2003; 31:3406 https://doi.org/10.1093/nar/gkg595

19. Meyers BC, Axtell MJ, Bartel B, Bartel DP, Baulcombe D, Bowman JL et al. Criteria for annotation of plant micro RNAs. Plant Cell. 2008; 20:3186-3190. https://doi.org/10.1105/tpc.108.064311

20. Xinbin Dai and Patrick X. Zhao. psRNATarget: A Plant Small RNA Target Analysis Server. Nucleic Acids Research. 2011; 39:W155-W159. https://doi.org/10.1093/nar/gkr319

21. Goodstein DM, Shu S, Howson R, et al. Phytozome: a comparative platform for green plant genomics. Nucleic Acids Research. 2012; 40: D1178-D1186. https://doi.org/10.1093/nar/gkr944

22. Conesa A, and Gotz S. Blast2GO: A Comprehensive Suite for Functional Analysis in Plant Genomics. Int J Pla Gen, 2008: 619832.

23. Jyothi MN, Rai DV, Nagesh Babu R. Identification and characterization of high temperature stress responsive novel miRNAs in French bean (Phaseolus vulgaris L.). Appl Biochem Biotechnol. 2015; doi. 10.1007/S12010-015-1614-2 https://doi.org/10.1007/s12010-015-1614-2

24. Okumura T, Makiguchi H, Makita Y, Yamashita R. Nakai K. Melina II: a web tool or comparisons among several predictive algorithms to find potential motifs from promoter regions. Nucleic Acids Res. 2007; 35:227-231. https://doi.org/10.1093/nar/gkm362

25. Contreras-Cubas C, Palomar M, Arteaga-Vázquez M, Reyes JL, Covarrubias AA. Non-coding RNAs in the plant response to abiotic stress. Planta. 2012; 236: 943-58. https://doi.org/10.1007/s00425-012-1693-z

26. Bi YM, Zhang Y, Signorelli T, Zhao R, Zhu T, Rothstein S. Genetic analysis of Arabidopsis GATA transcription factor gene family reveals a nitrate-inducible member important for chlorophyll synthesis and glucose sensitivity. The Plant Journal. 2005; 44:680-692. https://doi.org/10.1111/j.1365-313X.2005.02568.x

27. Arguello-Astorga G, Herrera-Estrella L. Evolution of light-regulated plant promoters. Annu Rev Plant Physiol Plant Mol Biol. 1998; 49: 525-555. https://doi.org/10.1146/annurev.arplant.49.1.525

28. Manfield IW, Devlin PF, Jen CH, Westhead DR, Gilmartin PM. Conservation, convergence, and divergence of light-responsive, circadian-regulated, and tissue-specific expression patterns during evolution of the Arabidopsis GATA gene family. Plant Physiol. 2007; 143:941-958. https://doi.org/10.1104/pp.106.090761

29. Jeong MJ and Shih MC. Interaction of a GATA factor with cis-acting elements involved in light regulation of nuclear genes encoding chloroplast glyceraldehyde-3-phosphate dehydrogenase in Arabidopsis. Biochem Biophys Res Commun. 2003; 300:555-562. https://doi.org/10.1016/S0006-291X(02)02892-9

30. Zhang C, Hou Y, Hao Q, Chen H, Chen L, Yuan S, et al. Genome-wide survey of the soybean gata transcription factor gene family and expression analysis under low nitrogen stress. PLoS ONE. 2015; 10:e0125174. https://doi.org/10.1371/journal.pone.0125174

31. Luo XM, Lin WH, Zhu S, Zhu JY, Sun Y, Fan XY, et al. Integration of light and brassinosteroid-signaling pathways by a GATA Transcription Factor in Arabidopsis. Dev Cell. 2010;19:872-883. https://doi.org/10.1016/j.devcel.2010.10.023

32. Jagadeeswaran G, Saini A, Sunkar R. Biotic and abiotic stress down-regulate miR398 expression in Arabidopsis. Planta. 2009; 229:1009-1014. https://doi.org/10.1007/s00425-009-0889-3

33. Yamasaki H, Abdel-Ghany SE, Cohu CM, Kobayashi Y, Shikanai T, Pilon M. Regulation of copper homeostasis by micro-RNA in Arabidopsis. J Biol Chem. 2007; 282:16369-16378. https://doi.org/10.1074/jbc.M700138200

34. Yamasaki H, Hayashi M, Fukazawa M, Kobayashi Y, Shikanai T. SQUAMOSA promoter binding protein-like7 is a central regulator for copper homeostasis in Arabidopsis. Plant Cell. 2009; 21:347-361. https://doi.org/10.1105/tpc.108.060137

35. Nagesh babu R, Jyothi MN, Sharadamma N, Devaraj VR. Changes in antioxidative and photosynthetic properties system of French bean (Phaseolus vulgaris L.) to boron toxicity. Journal of Agricultural and Biological Science. 2012; 7(11):892-898.

36. Jones-Rhoades MW, Bartel DP. Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol Cell. 2004; 14(6): 787-99. https://doi.org/10.1016/j.molcel.2004.05.027

37. Zhou X, Wang G, Sutoh K, Zhu JK, Zhang W. Identification of cold-inducible microRNAs in plants by transcriptome analysis. Biochimica et Biophysica Acta. 2008; 1779:780-788. https://doi.org/10.1016/j.bbagrm.2008.04.005

38. Moldovan D, Spriggs A, Yang J, Pogson BJ, Dennis ES, Wilson IW. Hypoxia-responsive microRNAs and trans-acting small interfering RNAs in Arabidopsis. J Exp Bot. 2009; 61:165–177. https://doi.org/10.1093/jxb/erp296

39. Zhou L, Liu Y, Liu Z, Kong D, Duan M, Luo L. Genome-wide identification and analysis of drought-responsive microRNAs in Oryza sativa. J Exp Bot. 2010; 61:4157-4168. https://doi.org/10.1093/jxb/erq237

40. Ai Kitazumi, Yoshihiro Kawahara, Ty S. Onda, David De Koeyer, Benildo G. de los Reyes. Implications of miR166 and miR159 induction to the basal response mechanisms of an andigena potato (Solanum tuberosum subsp. andigena) to salinity stress, predicted from network models in Arabidopsis. Genome. 2015; 58: 13-24. https://doi.org/10.1139/gen-2015-0011

41. Tuteja N. Abscisic Acid and Abiotic Stress Signaling. Plant Signaling & Behavior. 2007; 2:135-138. https://doi.org/10.4161/psb.2.3.4156

42. Du L and Poovaiah BW. A novel family of Ca2+/calmodulin-binding proteins involved in transcriptional regulation: interaction with fsh/Ring3 class transcription activators. Plant Mol Biol. 2004; 54:549-569. https://doi.org/10.1023/B:PLAN.0000038269.98972.bb

43. Robert HS, Quint A, Brand D, Vivian-Smith A, Offringa R. BTB and TAZ DOMAIN scaffold proteins perform a crucial function in Arabidopsis development. Plant J. 2009. 58:109–121. https://doi.org/10.1111/j.1365-313X.2008.03764.x

44. Mandadi KK, Misra A, Ren S, McKnight TD. BT2, a BTB Protein, Mediates Multiple Responses to Nutrients, Stresses, and Hormones in Arabidopsis. Plant Physiology. 2009; 150(4):1930–1939. https://doi.org/10.1104/pp.109.139220

45. Cui X, Xu SM, Mu DS, Yang ZM. Genomic analysis of rice microRNA promoters and clusters. Gene. 2009; 431:61-66. https://doi.org/10.1016/j.gene.2008.11.016

46. Guleria P, Mahajan M, Bhardwaj J, Yadav SK. Plant Small RNAs: Biogenesis, Mode of Action and Their Roles in Abiotic Stresses. Genomics, Proteomics and Bioinformatics. 2011; 9:183-199. https://doi.org/10.1016/S1672-0229(11)60022-3

Article Metrics
362 Views 263 Downloads 625 Total

Year

Month

Related Search

By author names

Similar Articles