miRSNP-Based Approach Identifies a miRNA That Regulates Prostate-Specific Antigen in an Allele-Specific Manner

Recent development of bioinformatics tools for microRNA target prediction

MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene expression. Binding of miRNAs to target mRNAs can repress their translation by inducing the degradation or by inhibiting the translation of the target mRNAs. High-throughput experimental approaches for miRNA target identification are costly and time-consuming, depending on various factors. It is vitally important to develop the bioinformatics methods for accurately predicting miRNA targets. With the increase of RNA sequences in the post-genomic era, bioinformatics methods are being developed for miRNA studies specially for miRNA target prediction. This review summarizes the current development of state-of-the-art bioinformatics tools for miRNA target prediction, points out the progress and limitations of the available miRNA databases, and their working principles. Finally, we discuss the caveat and perspectives of the next-generation algorithms for the prediction of miRNA targets.

The association of stromal antigen 3 (STAG3) sequence variations with spermatogenic impairment in the male Korean population

The stromal antigen 3 (STAG3) gene, encoding a meiosis-specific cohesin component, is a strong candidate for causing male infertility, but little is known about this gene so far. We identified STAG3 in patients with nonobstructive azoospermia (NOA) and normozoospermia in the Korean population. The coding regions and their intron boundaries of STAG3 were identified in 120 Korean men with spermatogenic impairments and 245 normal controls by using direct sequencing and haplotype analysis. A total of 30 sequence variations were identified in this study. Of the total, seven were exonic variants, 18 were intronic variants, one was in the 5’-UTR, and four were in the 3’-UTR. Pathogenic variations that directly caused NOA were not identified. However, two variants, c.3669+35C>G (rs1727130) and +198A>T (rs1052482), showed significant differences in the frequency between the patient and control groups (P = 0.021, odds ratio [OR]: 1.79, 95% confidence interval [CI]: 1.098–2.918) and were tightly linked in the linkage disequilibrium (LD) block. When pmir-rs1052482A was cotransfected with miR-3162-5p, there was a substantial decrease in luciferase activity, compared with pmir-rs1052482T. This result suggests that rs1052482 was located within a binding site of miR-3162-5p in the STAG3 3’-UTR, and the minor allele, the rs1052482T polymorphism, might offset inhibition by miR-3162-5p. We are the first to identify a total of 30 single-nucleotide variations (SNVs) of STAG3 gene in the Korean population. We found that two SNVs (rs1727130 and rs1052482) located in the 3’-UTR region may be associated with the NOA phenotype. Our findings contribute to understanding male infertility with spermatogenic impairment.

Allelic-Specific Regulation of xCT Expression Increases Susceptibility to Tuberculosis by Modulating microRNA-mRNA Interactions

Tuberculosis (TB) is the leading cause of death from a single infectious agent globally, and the development of multidrug resistance represents a serious health concern, particularly in the developing world. Novel effective treatments are urgently required. xCT expression is known to increase susceptibility to TB, and certain polymorphisms in the gene encoding this protein interrupt the binding of microRNA and prevent its suppression. Taking advantage of the FDA approval for the use of sulfasalazine (SASP), which inhibits xCT-mediated cystine transport in humans, we demonstrate how host genotype-specific therapies tailored to the xCT genotype can improve TB outcomes.

xCT forms part of the x_c⁻ cysteine-glutamate antiporter which inhibits antimicrobial inflammatory immune functions and thus increases susceptibility to tuberculosis (TB). However, the associations between xCT gene polymorphisms and susceptibility to TB, as well as whether these modulate xCT expression or affect treatment with the xCT inhibitor sulfasalazine (SASP), are unclear. In the present study, we genotyped xCT polymorphisms in a large Chinese cohort and found that the single-nucleotide polymorphism (SNP) rs13120371 was associated with susceptibility to TB. The rs13120371 AA genotype was strongly associated with an increased risk of TB and increased xCT mRNA expression levels compared to those with the GG or AG genotype. rs13120371 is located on the 3′ untranslated (UTR) region of the xCT gene, in the putative binding site for miR-142-3p, and the results of luciferase reporter assays indicated that the rs13120371 AA genotype inhibited the binding of miR-42-3p to xCT. Bacterial burden was also significantly higher in cells with the AA genotype than in those with the GG genotype. Furthermore, pretreatment with SASP alleviated this burden in cells with the AA genotype but conferred no benefit in cells with the GG phenotype. In summary, we identified a functional SNP (rs13120371) in the xCT 3′ UTR region that increases susceptibility to TB through interacting with miR-142-3p.

IMPORTANCE Tuberculosis (TB) is the leading cause of death from a single infectious agent globally, and the development of multidrug resistance represents a serious health concern, particularly in the developing world. Novel effective treatments are urgently required. xCT expression is known to increase susceptibility to TB, and certain polymorphisms in the gene encoding this protein interrupt the binding of microRNA and prevent its suppression. Taking advantage of the FDA approval for the use of sulfasalazine (SASP), which inhibits xCT-mediated cystine transport in humans, we demonstrate how host genotype-specific therapies tailored to the xCT genotype can improve TB outcomes.

Meta-Analysis of Gene Expression and Identification of Biological Regulatory Mechanisms in Alzheimer's Disease

Alzheimer's disease (AD), also known as senile dementia, is a progressive neurodegenerative disease. The etiology and pathogenesis of AD have not yet been elucidated. We examined common differentially expressed genes (DEGs) from different AD tissue microarray datasets by meta-analysis and screened the AD-associated genes from the common DEGs using GCBI. Then we studied the gene expression network using the STRING database and identified the hub genes using Cytoscape. Furthermore, we analyzed the microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and single nucleotide polymorphisms (SNPs) associated with the AD-associated genes, and then identified feed-forward loops. Finally, we performed SNP analysis of the AD-associated genes. Our results identified 207 common DEGs, of which 57 have previously been reported to be associated with AD. The common DEG expression network identified eight hub genes, all of which were previously known to be associated with AD. Further study of the regulatory miRNAs associated with the AD-associated genes and other genes specific to neurodegenerative diseases revealed 65 AD-associated miRNAs. Analysis of the miRNA associated transcription factor-miRNA-gene-gene associated TF (mTF-miRNA-gene-gTF) network around the AD-associated genes revealed 131 feed-forward loops (FFLs). Among them, one important FFL was found between the gene SERPINA3, hsa-miR-27a, and the transcription factor MYC. Furthermore, SNP analysis of the AD-associated genes identified 173 SNPs, and also found a role in AD for miRNAs specific to other neurodegenerative diseases, including hsa-miR-34c, hsa-miR-212, hsa-miR-34a, and hsa-miR-7. The regulatory network constructed in this study describes the mechanism of cell regulation in AD, in which miRNAs and lncRNAs can be considered AD regulatory factors.

Computational Resources for Prediction and Analysis of Functional miRNA and Their Targetome

microRNAs are evolutionarily conserved, endogenously produced, noncoding RNAs (ncRNAs) of approximately 19-24 nucleotides (nts) in length known to exhibit gene silencing of complementary target sequence. Their deregulated expression is reported in various disease conditions and thus has therapeutic implications. In the last decade, various computational resources are published in this field. In this chapter, we have reviewed bioinformatics resources, i.e., miRNA-centered databases, algorithms, and tools to predict miRNA targets. First section has enlisted more than 75 databases, which mainly covers information regarding miRNA registries, targets, disease associations, differential expression, interactions with other noncoding RNAs, and all-in-one resources. In the algorithms section, we have compiled about 140 algorithms from eight subcategories, viz. for the prediction of precursor (pre-) and mature miRNAs. These algorithms are developed on various sequence, structure, and thermodynamic based features incorporated into different machine learning techniques (MLTs). In addition, computational identification of miRNAs from high-throughput next generation sequencing (NGS) data and their variants, viz. isomiRs, differential expression, miR-SNPs, and functional annotation, are discussed. Prediction and analysis of miRNAs and their associated targets are also evaluated under miR-targets section providing knowledge regarding novel miRNA targets and complex host-pathogen interactions. In conclusion, we have provided comprehensive review of in silico resources published in miRNA research to help scientific community be updated and choose the appropriate tool according to their needs.

The rs17084733 variant in the KIT 3' UTR disrupts a miR-221/222 binding site in gastrointestinal stromal tumour: a sponge-like mechanism conferring disease susceptibility

Several miRNAs are dysregulated in gastrointestinal stromal tumours (GIST), and miR-221/222 appear to have a prominent role in GIST biology. Therefore, we investigated the role of DNA variants located in miR-221/222 precursor sequences and their target KIT 3'UTR. Ninety-five polymorphisms were analysed in 115 GIST cases and 88 healthy controls. KIT 3'UTR rs17084733 and pri-miR-222 rs75246947 were found significantly associated with GIST susceptibility. Specifically, KIT rs17084733 A allele was more common in GIST, particularly in KIT wild-type (WT) patients (Padj = 0.017). rs17084733 variant is located within one of the three miR-221/222 binding sites in the KIT 3'UTR, resulting in a mismatch in this seed region. Conversely, KIT mRNA levels were lower in patients carrying the variant allele, except for KIT mutant GIST. Luciferase assay data in GIST cells, generated using a construct containing all the three miR-221/222 binding sites, are consistent with KIT mRNA levels in GIST patients. Reporter assay data, generated using a construct containing only the site encompassing rs17084733, confirmed that this is a functional variant disrupting the miR-221/222 binding site. In conclusion, this is the first study investigating the role of SNPs on miR-221/222 precursor sequences and their binding region on KIT 3'UTR in GIST. We identified the KIT variant rs17084733 as a possible novel genetic biomarker for risk of developing KIT-WT GIST. Moreover, our findings suggest the role of one of the three miR-221/222 binding sites on KIT 3'UTR as endogenous sponge, soaking up and subtracting miR-221/222 to the other two sites characterized by a higher affinity.

A meta-analysis of public microarray data identifies biological regulatory networks in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is a long-term degenerative disease that is caused by environmental and genetic factors. The networks of genes and their regulators that control the progression and development of PD require further elucidation.

METHODS

We examine common differentially expressed genes (DEGs) from several PD blood and substantia nigra (SN) microarray datasets by meta-analysis. Further we screen the PD-specific genes from common DEGs using GCBI. Next, we used a series of bioinformatics software to analyze the miRNAs, lncRNAs and SNPs associated with the common PD-specific genes, and then identify the mTF-miRNA-gene-gTF network.

RESULT

Our results identified 36 common DEGs in PD blood studies and 17 common DEGs in PD SN studies, and five of the genes were previously known to be associated with PD. Further study of the regulatory miRNAs associated with the common PD-specific genes revealed 14 PD-specific miRNAs in our study. Analysis of the mTF-miRNA-gene-gTF network about PD-specific genes revealed two feed-forward loops: one involving the SPRK2 gene, hsa-miR-19a-3p and SPI1, and the second involving the SPRK2 gene, hsa-miR-17-3p and SPI. The long non-coding RNA (lncRNA)-mediated regulatory network identified lncRNAs associated with PD-specific genes and PD-specific miRNAs. Moreover, single nucleotide polymorphism (SNP) analysis of the PD-specific genes identified two significant SNPs, and SNP analysis of the neurodegenerative disease-specific genes identified seven significant SNPs. Most of these SNPs are present in the 3'-untranslated region of genes and are controlled by several miRNAs.

CONCLUSION

Our study identified a total of 53 common DEGs in PD patients compared with healthy controls in blood and brain datasets and five of these genes were previously linked with PD. Regulatory network analysis identified PD-specific miRNAs, associated long non-coding RNA and feed-forward loops, which contribute to our understanding of the mechanisms underlying PD. The SNPs identified in our study can determine whether a genetic variant is associated with PD. Overall, these findings will help guide our study of the complex molecular mechanism of PD.