Small RNAs in Plant Responses to Abiotic Stresses: Regulatory Roles and Study Methods
Abstract
:1. Introduction: The Importance of Small RNAs
2. Mechanisms of sRNA-Mediated Genetic Regulation
2.1. Transcriptional Gene Silencing
2.2. Post-Transcriptional Gene Silencing
Mechanism of Regulation | sRNA Types Participated | Origin of sRNAs | Targets of sRNAs | Modes of Action |
---|---|---|---|---|
Transcriptional gene silencing | hc-siRNAs | Transcripts of heterochromatic regions | Heterochromatic regions (act in cis) | RNA-directed DNA methylation |
Post-transcriptional gene silencing | miRNAs | Short stem-loop-forming transcripts | Other transcripts (act in both cis and trans) | Transcript cleavage; translational inhibition |
TAS-transcripts | Triggering double strand synthesis of TAS-transcripts | |||
NAT-siRNAs | Antisense transcripts | Other transcripts in both cis and trans | Transcript cleavage; translational inhibition | |
ta-siRNAs | TAS-loci derived transcripts | Other transcripts in both cis and trans | Transcript cleavage; translational inhibition |
2.2.1. miRNA
2.2.2. siRNA
3. Computational Methods to Identify sRNAs
3.1. Computational Prediction of miRNA Gene Loci
Tool | Application | Property | Reference |
---|---|---|---|
MIRFINDER | Detection of potential conserved miRNAs in Arabidopsis thaliana and Oryza satica | The use of NCBI BLAST to search for conserved short hits (~21–22 nt). The hits with flanking sequences were identified as putative hairpin precursors. | [58] |
miRSeeker | Identification of novel miRNA candidates that are conserved in insect, nematode, or vertebrate | The use of AVID to align Drosophila melanogaster and Drosophila pseudoobscura euchromatic sequences to search for conserved sequences meeting these two criteria: 1. Having extended stem-loop structure; 2. Having nucleotide divergence from known miRNAs. | [59] |
mirCoS | Prediction of mammalian miRNAs | Detection of known miRNAs and prediction of new miRNAs based on sequence, secondary structure and conservation by comparing human and mouse genomes. | [60] |
miRRim | Identification of novel miRNAs in human | Detection of miRNAs with the use of a hidden Markov model. | [61] |
miRAlign | Detection of miRNA homologs or orthologs in animals. | Detection of miRNAs based on sequence and structure alignment. The sensitivity is better than BLAST search and ERPIN search with comparable specificity. | [62] |
microHARVESTER | Identification of plant miRNA homologs | Identification of plant miRNA homologs based on query miRNA. | [63] |
MiRscan | Identification of vertebrate miRNA genes | Evaluation of conserved stem-loops. | [64] |
miRDeep | Identification of miRNAs with deep sequencing data | The use of known miRNA training set obtained from Caenorhabditis elegans to deduce parameters of most probable miRNA precursors. These parameters were used to score precursor candidates using a probabilistic approach. | [65] |
MiRCheck | Identification of miRNAs in Arabidopsis thaliana and Oryza sativa | The use of EINVERTED from EMBOSS [66] to predict genome-wide inverted repeats in both Arabidopsis thaliana and Oryza sativa to define possible hairpin regions, and the check for segments with high homology between Arabidopsis thaliana hairpins and Oryza sativa hairpins using Patscan. | [67] |
3.1.1. Choosing the Right Tools for Plant miRNA Discovery
Tool | Property | Reference |
---|---|---|
miRDeep-P | Adopting miRDeep core algorithm with modified step of setting a maximal value for the MFE log-odds score to account for longer plant miRNA precursors | [70] |
miRPlant | Implementing miRDeep* [74] with 100 and 200 nt extended genomic regions from mapped read peaks to include more bona fide miRNA precursor candidates | [71] |
miR-PREFeR | Filtering miRNA precursor candidates with criteria suggested in [75] for annotating plant miRNAs | [76] |
MIReNA | Filtering putative precursors with length-normalized and GC-normalized MFE to accommodate the prediction of plant miRNAs | [72] |
ShortStack | Defining structural miRNA parameters based on selected annotated miRNA in miRBase depending on the “miRType” specified by user, either “plant” or “animal”, subsequently filter candidates with criteria suggested in [75] | [77] |
3.1.2. Computational Prediction of TAS-Like Loci
3.1.3. Common Features of Target Prediction Tools
3.1.4. Functions of Prediction Tools
3.2. Computational Prediction of sRNA Targets
3.3. High-Throughput sRNA Target Identification—Degradome
4. Experimental Validations of Predicted sRNAs
Method | Stress | sRNA | Reference |
---|---|---|---|
Validation of the existence of sRNA | |||
qRT-PCR | Salinity, copper deficiency | miR397, miR857 | [103] |
Northern blot | Salinity, sulphur deprivation, oxidative stress, nitrogen deficiency, inorganic phosphtase deprivation, drought, irradiation, copper deficiency | miR399, miR395, miR398, miR408 | [34,90,104,105,106,107,108,109,110] |
Validation of the target gene | |||
5′ RACE | Copper deficiency | miR397, miR408 | [103] |
Transgenic plant for functional test | |||
Arabidopsis | Inorganic phosphate deprivation | miR399 | [104] |
Arabidopsis | Drought | miR196 | [111] |
Cree** bentgrass | Drought, salinity | miR319 | [112] |
4.1. Validation of sRNAs Expression
4.1.1. Quantitative Detection of sRNAs by Northern Blot
4.1.2. Quantitative Detection of sRNAs by qPCR
4.1.3. In Situ Hybridization for Spatiotemporal Detection of sRNAs
4.2. Validation of sRNA Targets
4.2.1. Labeled miRNA Pull-down (LAMP) Assay System
4.2.2. RNA Ligase-Mediated Amplification of cDNA End (RLM-RACE)
4.3. Functional Validation of sRNAs
4.3.1. Reporter Assays
4.3.2. Validation of the Effect of the sRNA of Interest on the Target Gene Expression
Purpose | Method | Advantage(s) | Disadvantage(s) |
---|---|---|---|
Validation of the existence of predicted sRNA | Northern blot | Quantitative, simultaneous detection of sRNA and its precursor | Optimization steps are needed to improve sensitivity and specificity. |
qPCR | Small amount of RNA is required | Normalization by spike-in control or housekee** genes can be unreliable. | |
Validation of the existence of predicted sRNA | In situ hybridization | Allows tissue-specific and spatiotemporal detection | Optimization steps are needed to improve sensitivity and specificity. |
Functional analysis of sRNA | LAMP assay | Straightforward | An in vitro approach, the pre-miRNA processing and specificity have been questioned; not popular for plants. |
RLM-RACE | Previous knowledge of the cleaved mRNA is not required | Cannot distinguish by which type of sRNA the mRNA cleavage is mediated. | |
Reporter assays | An in vivo approach | Transformation of the species under study is needed. |
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Ku, Y.-S.; Wong, J.W.-H.; Mui, Z.; Liu, X.; Hui, J.H.-L.; Chan, T.-F.; Lam, H.-M. Small RNAs in Plant Responses to Abiotic Stresses: Regulatory Roles and Study Methods. Int. J. Mol. Sci. 2015, 16, 24532-24554. https://doi.org/10.3390/ijms161024532
Ku Y-S, Wong JW-H, Mui Z, Liu X, Hui JH-L, Chan T-F, Lam H-M. Small RNAs in Plant Responses to Abiotic Stresses: Regulatory Roles and Study Methods. International Journal of Molecular Sciences. 2015; 16(10):24532-24554. https://doi.org/10.3390/ijms161024532
Chicago/Turabian StyleKu, Yee-Shan, Johanna Wing-Hang Wong, Zeta Mui, Xuan Liu, Jerome Ho-Lam Hui, Ting-Fung Chan, and Hon-Ming Lam. 2015. "Small RNAs in Plant Responses to Abiotic Stresses: Regulatory Roles and Study Methods" International Journal of Molecular Sciences 16, no. 10: 24532-24554. https://doi.org/10.3390/ijms161024532