A Practical Guide to miRNA Target Prediction

Akhtar, Most Mauluda; Micolucci, Luigina; Islam, Soriful; Olivieri, Fabiola; Procopio, Antonio Domenico

doi:10.1007/978-1-4939-9207-2_1

Cited by 24 publications

(24 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several target prediction algorithms identify orthologous 3 -UTR sequences and then evaluate the conservation of the potential miRNA target site in other species, in particular in those that are closely related (e.g., miRanda [19]). This analysis across the species allows reducing the number of false positive predictions [15]. However, this approach can present some limitations, since it only searches for targets of evolutionary conserved miRNAs, but conservation does not necessarily imply functionality.…”

Section: Evolutionary Conservationmentioning

confidence: 99%

“…The use of prediction algorithms also considering multi-targeting significantly reduces the number of predictions, increasing the probability of identifying true targets. Thus, it can be very useful for reducing the number of candidates before performing experimental validation studies of miRNA targets [15].…”

Section: Number Of Target Sites In the Same 3 -Utrmentioning

confidence: 99%

“…This is important information, since a stable miRNA-mRNA interaction can be more likely classified as a real interaction. Nevertheless, it is not easy to establish the proper threshold of free energy, since data obtained from known miRNA-mRNA complexes are insufficient [15].…”

mentioning

confidence: 99%

See 2 more Smart Citations

miRNA Targets: From Prediction Tools to Experimental Validation

Riolo

Cantara

Marzocchi

et al. 2020

MPs

135

View full text Add to dashboard Cite

MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on target mRNAs, miRNAs play gene-regulatory roles, producing remarkable changes in several physiological and pathological processes. Thus, the identification of miRNA-mRNA target interactions is fundamental for discovering the regulatory network governed by miRNAs. The best way to achieve this goal is usually by computational prediction followed by experimental validation of these miRNA-mRNA interactions. This review summarizes the key strategies for miRNA target identification. Several tools for computational analysis exist, each with different approaches to predict miRNA targets, and their number is constantly increasing. The major algorithms available for this aim, including Machine Learning methods, are discussed, to provide practical tips for familiarizing with their assumptions and understanding how to interpret the results. Then, all the experimental procedures for verifying the authenticity of the identified miRNA-mRNA target pairs are described, including High-Throughput technologies, in order to find the best approach for miRNA validation. For each strategy, strengths and weaknesses are discussed, to enable users to evaluate and select the right approach for their interests.

show abstract

Section: Evolutionary Conservationmentioning

confidence: 99%

Section: Number Of Target Sites In the Same 3 -Utrmentioning

confidence: 99%

See 1 more Smart Citation

miRNA Targets: From Prediction Tools to Experimental Validation

Riolo

Cantara

Marzocchi

et al. 2020

MPs

135

View full text Add to dashboard Cite

show abstract

“…34 Therefore, we first focused on these conserved miRNAs including miR-212-5p, miR-338-3p, miR-15-5p/16-5p/195-5p/424-5p/497-5p, miR-324-3p and miR-326. Furthermore, according to the rankings of the conserved 8mer, 7mer, and 6mer sites that matched in the miRNA seed region and the context scores, 35 the strong candidate miRNAs in +295 bp -+497 bp were miR-212-5p, miR-324-3p and miR-326 ( Figure 5). Studies have shown that miR-212-5p overexpression inhibits MPP+-induced SK-N-SH cell apoptosis, inflammation and cytotoxicity.…”

Section: Figurementioning

confidence: 99%

<p>Functional Analysis of the 3ʹ Untranslated Region of the Human <em>GRIN1</em> Gene in Regulating Gene Expression in vitro</p>

Liu

Jinghua

et al. 2020

NDT

View full text Add to dashboard Cite

Purpose: Abnormal expression of the NR1 subunit of the N-methyl-d-aspartate (NMDA) receptor may potentially increase the susceptibility to neuropsychiatric diseases. The purpose of this study was to investigate the functional sequence of the 3ʹUTR of the human GRIN1 gene, which encodes the GluN1 receptor to determine the effect on the expression of GluN1 receptor. Methods: We transferred seven recombinant pmirGLO recombinant vectors containing the 3ʹUTR truncated fragment of the GRIN1 gene into HEK-293, SK-N-SH, and U87 cell lines and compared the relative fluorescence intensity of adjacent length fragments. The TargetScan database was used to predict miRNAs. Then, miRNA mimics/inhibitors were co-transfected into the three cell lines with the 3ʹUTR of GRIN1 (pmirGLO-GRIN1), to investigate their influence on GRIN1 gene expression. Results: Compared with the pmirGLo-Basic vector, the relative fluorescence intensity of the complete GRIN1 gene 3ʹUTR recombinant sequence −27 bp-+1284 bp (the next base of the stop codon is +1) was significantly decreased in all three cell lines. The relative fluorescence intensities were significantly different between −27 bp-+294 bp and −27 bp-+497 bp regions, and between −27 bp-+708 bp and −27 bp-+907 bp regions. According to the prediction of the TargetScan database and analysis, miR-212-5p, miR-324-3p and miR-326 may bind to +295 bp-+497 bp, while miR-491-5p may bind to +798 bp-+907 bp. After co-transfection of miRNA mimic/inhibitor or mimic/inhibitor NC with a recombinant vector in the 3ʹUTR region of GRIN1 gene, we found that has-miR-491-5p inhibited GRIN1 expression significantly in all three cell lines, while has-miR-326 inhibitor upregulated GRIN1 expression in HEK-293 and U87 cells. Conclusion: miR-491-5p may bind to the 3ʹUTR of the GRIN1 gene (+799 bp-+805 bp, the next base of the stop codon is +1) and down-regulate gene expression in HEK-293, SK-N-SH, and U87 cell lines, which implicates a potential role of miR-491-5p in central nervous system diseases.

show abstract

“…MicroRNAs (miRNAs) are a class of endogenous, noncoding single-stranded microRNAs that are widely found in eukaryotic cells [30,31]. ey can bind to the 3′untranslated region (UTR) of target genes causing degradation of mRNA and inhibition of protein translation, as well as affecting the expression of target genes [32,33]. Other studies have reported that some miRNAs are expressed in the brain and play special roles in the proliferation and apoptosis of neurons, which are associated with the development of psychiatric diseases [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Effect of Electroacupuncture on Reuptake of Serotonin via miRNA-16 Expression in a Rat Model of Depression

Zhao

Tian

Song

et al. 2019

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

The current study aimed to investigate the effects and mechanisms of electroacupuncture (EA) treatment applied to Bai hui (GV20) and Yin tang (GV29) acupoints (1 mA, 2 Hz, continuous wave, 20 minutes) for 28 days in a rat model of chronic unpredictable mild stress (CUMS) on reuptake of serotonin (5-hydroxytryptamine (5-HT)) and miRNA-16 levels in the hippocampus and serum. Rats were housed in individual cages, and CUMS was used to establish a rat model of depression. After EA treatment for 4 weeks, behavioral changes and indices including 5-HT transporter (SERT), 5-HT, and miRNA-16 levels in the hippocampus and serum were examined. The EA treatment significantly improved base levels of sucrose preference and exploratory behavior and significantly decreased SERT protein and mRNA expression in the hippocampus of depressed rats. Significantly increased 5-HT levels were observed, and miRNA-16 levels were significantly decreased in the hippocampus and serum of depressed rats. In conclusion, the antidepressant effects of EA treatment may be affected via inhibition of 5-HT reuptake, upregulation of 5-HT levels, and inhibition of miRNA-16 expression in the hippocampus and serum.

show abstract

A Practical Guide to miRNA Target Prediction

Cited by 24 publications

References 66 publications

miRNA Targets: From Prediction Tools to Experimental Validation

miRNA Targets: From Prediction Tools to Experimental Validation

<p>Functional Analysis of the 3ʹ Untranslated Region of the Human <em>GRIN1</em> Gene in Regulating Gene Expression in vitro</p>

Effect of Electroacupuncture on Reuptake of Serotonin via miRNA-16 Expression in a Rat Model of Depression

Contact Info

Product

Resources

About