Dataset Information

Evaluating the Effect of 3'-UTR Variants in DICER1 and DROSHA on Their Tissue-Specific Expression by miRNA Target Prediction.

ABSTRACT: Untranslated gene regions (UTRs) play an important role in controlling gene expression. 3'-UTRs are primarily targeted by microRNA (miRNA) molecules that form complex gene regulatory networks. Cancer genomes are replete with non-coding mutations, many of which are connected to changes in tumor gene expression that accompany the development of cancer and are associated with resistance to therapy. Therefore, variants that occurred in 3'-UTR under cancer progression should be analysed to predict their phenotypic effect on gene expression, e.g., by evaluating their impact on miRNA target sites. Here, we analyze 3'-UTR variants in DICER1 and DROSHA genes in the context of myelodysplastic syndrome (MDS) development. The key features of this analysis include an assessment of both "canonical" and "non-canonical" types of mRNA-miRNA binding and tissue-specific profiling of miRNA interactions with wild-type and mutated genes. As a result, we obtained a list of DICER1 and DROSHA variants likely altering the miRNA sites and, therefore, potentially leading to the observed tissue-specific gene downregulation. All identified variants have low population frequency consistent with their potential association with pathology progression.

SUBMITTER: Bug DS

PROVIDER: S-EPMC8929110 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Evaluating the Effect of 3'-UTR Variants in <i>DICER1</i> and <i>DROSHA</i> on Their Tissue-Specific Expression by miRNA Target Prediction.

Bug Dmitrii S DS Tishkov Artem V AV Moiseev Ivan S IS Petukhova Natalia V NV

Current issues in molecular biology 20210706 2

Untranslated gene regions (UTRs) play an important role in controlling gene expression. 3'-UTRs are primarily targeted by microRNA (miRNA) molecules that form complex gene regulatory networks. Cancer genomes are replete with non-coding mutations, many of which are connected to changes in tumor gene expression that accompany the development of cancer and are associated with resistance to therapy. Therefore, variants that occurred in 3'-UTR under cancer progression should be analysed to predict th ...[more]

PMID: 34287278

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Project description:miRNAs are small non-coding RNAs that regulate gene expression by binding to the 3'-UTR of genes. Many recent studies have reported that miRNAs play important biological roles by regulating specific mRNAs or genes. Many sequence-based target prediction algorithms have been developed to predict miRNA targets. However, these methods are not designed for condition-specific target predictions and produce many false positives; thus, expression-based target prediction algorithms have been developed for condition-specific target predictions. A typical strategy to utilize expression data is to leverage the negative control roles of miRNAs on genes. To control false positives, a stringent cutoff value is typically set, but in this case, these methods tend to reject many true target relationships, i.e., false negatives. To overcome these limitations, additional information should be utilized. The literature is probably the best resource that we can utilize. Recent literature mining systems compile millions of articles with experiments designed for specific biological questions, and the systems provide a function to search for specific information. To utilize the literature information, we used a literature mining system, BEST, that automatically extracts information from the literature in PubMed and that allows the user to perform searches of the literature with any English words. By integrating omics data analysis methods and BEST, we developed Context-MMIA, a miRNA-mRNA target prediction method that combines expression data analysis results and the literature information extracted based on the user-specified context. In the pathway enrichment analysis using genes included in the top 200 miRNA-targets, Context-MMIA outperformed the four existing target prediction methods that we tested. In another test on whether prediction methods can re-produce experimentally validated target relationships, Context-MMIA outperformed the four existing target prediction methods. In summary, Context-MMIA allows the user to specify a context of the experimental data to predict miRNA targets, and we believe that Context-MMIA is very useful for predicting condition-specific miRNA targets.

Project description:BackgroundMicroRNA (miRNA) processing machinery gene variant was associated with several diseases. We aimed to explore for the first time the association of machinery gene (DROSHA rs10719A/G; DICER1 rs3742330A/G; RAN rs14035C/T; and XPO5 rs11077T/G) variants with the susceptibility and phenotype of end-stage renal disease (ESRD).MethodA total of 281 participants (98 ESRD patients and 183 healthy volunteers) were enrolled. Real-Time TaqMan allelic discrimination assay was applied for the genotyping of the specified variants. Multiple logistic regression models, univariate, multivariate, and principal component analyses were carried out.ResultsCarrying one DICER1 rs3742330*G allele conferred protection against developing ESRD [heterozygote comparison: OR = 0.30, 95% CI = 0.15-0.62, dominant model: OR = 0.35, 95% CI = 0.17-0.70]. Similarly, for XPO5 rs11077T/G, homozygote and heterozygote carriers of G variant were less likely to develop ESRD [homozygote comparison: adjusted OR = 0.23, 95% CI = 0.11-0.50, and heterozygote comparison: OR = 0.50, 95% CI = 0.22-0.92, and allelic model: OR = 0.46, 95% CI = 0.34-0.70]. RAN rs14035*TT subjects were 5 times more likely to develop ESRD while being heterozygote (CT) have twice the risk [homozygote comparison: 5.18, 95% CI = 2.28-11.8, heterozygote comparison: OR = 2.12, 95% CI = 1.10-409]. Subgroup analysis also detected DICER1 rs3742330*A, XPO5 rs11077*T, and RAN rs14035*T as genetic risk determinants for ESRD development in both sex and age categories. Two genotype combinations of DROSHA/DICER1/XPO5/RAN were associated with increased susceptibility to ESRD [A-A-T-T: OR = 9.49, 95%CI = 2.48-36.31 (P = .001) and G-A-T-T: OR = 5.92, 95%CI = 1.77-19.83 (P = .004), respectively].ConclusionVariants and combined genotypes of DICER1 rs3742330, XPO5 rs11077, and RAN rs14035 might be associated with ESRD development in the study population. Integrating molecular analysis in ESRD risk stratification is warranted.

Dataset Information

Evaluating the Effect of 3'-UTR Variants in DICER1 and DROSHA on Their Tissue-Specific Expression by miRNA Target Prediction.

Publications

Evaluating the Effect of 3'-UTR Variants in <i>DICER1</i> and <i>DROSHA</i> on Their Tissue-Specific Expression by miRNA Target Prediction.

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