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验证人类 microRNA 靶途径可用于评估靶预测工具。

Validation of human microRNA target pathways enables evaluation of target prediction tools.

机构信息

Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.

Institute of Human Genetics, Saarland University, 66421 Homburg, Germany.

出版信息

Nucleic Acids Res. 2021 Jan 11;49(1):127-144. doi: 10.1093/nar/gkaa1161.

DOI:10.1093/nar/gkaa1161
PMID:33305319
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7797041/
Abstract

MicroRNAs are regulators of gene expression. A wide-spread, yet not validated, assumption is that the targetome of miRNAs is non-randomly distributed across the transcriptome and that targets share functional pathways. We developed a computational and experimental strategy termed high-throughput miRNA interaction reporter assay (HiTmIR) to facilitate the validation of target pathways. First, targets and target pathways are predicted and prioritized by computational means to increase the specificity and positive predictive value. Second, the novel webtool miRTaH facilitates guided designs of reporter assay constructs at scale. Third, automated and standardized reporter assays are performed. We evaluated HiTmIR using miR-34a-5p, for which TNF- and TGFB-signaling, and Parkinson's Disease (PD)-related categories were identified and repeated the pipeline for miR-7-5p. HiTmIR validated 58.9% of the target genes for miR-34a-5p and 46.7% for miR-7-5p. We confirmed the targeting by measuring the endogenous protein levels of targets in a neuronal cell model. The standardized positive and negative targets are collected in the new miRATBase database, representing a resource for training, or benchmarking new target predictors. Applied to 88 target predictors with different confidence scores, TargetScan 7.2 and miRanda outperformed other tools. Our experiments demonstrate the efficiency of HiTmIR and provide evidence for an orchestrated miRNA-gene targeting.

摘要

微 RNA 是基因表达的调控因子。一个广泛存在但未经证实的假设是,miRNA 的靶标在转录组中是非随机分布的,并且靶标共享功能途径。我们开发了一种称为高通量 miRNA 相互作用报告基因检测(HiTmIR)的计算和实验策略,以促进靶途径的验证。首先,通过计算手段预测和优先考虑靶标和靶标途径,以提高特异性和阳性预测值。其次,新型网络工具 miRTaH 有助于大规模指导报告基因检测构建的设计。第三,进行自动化和标准化的报告基因检测。我们使用 miR-34a-5p 评估了 HiTmIR,鉴定了 TNF 和 TGFB 信号以及帕金森病(PD)相关类别,并对 miR-7-5p 重复了该流程。HiTmIR 验证了 miR-34a-5p 的 58.9%的靶基因和 miR-7-5p 的 46.7%的靶基因。我们通过在神经元细胞模型中测量靶基因的内源性蛋白水平来证实靶向作用。标准化的阳性和阴性靶基因被收集在新的 miRATBase 数据库中,代表了一个用于训练或基准测试新靶基因预测器的资源。应用于 88 个置信度分数不同的靶基因预测器,TargetScan 7.2 和 miRanda 优于其他工具。我们的实验证明了 HiTmIR 的效率,并为协调的 miRNA-基因靶向作用提供了证据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2e/7797041/bfec5ec767b7/gkaa1161fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2e/7797041/7b362b060ec5/gkaa1161fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2e/7797041/7aba3720b4bf/gkaa1161fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2e/7797041/ff31f9d0e642/gkaa1161fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2e/7797041/88602113fd74/gkaa1161fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2e/7797041/9daa22e49563/gkaa1161fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2e/7797041/372e33d10ada/gkaa1161fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2e/7797041/31d6d77a0f96/gkaa1161fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2e/7797041/bfec5ec767b7/gkaa1161fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2e/7797041/7b362b060ec5/gkaa1161fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2e/7797041/7aba3720b4bf/gkaa1161fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2e/7797041/ff31f9d0e642/gkaa1161fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2e/7797041/88602113fd74/gkaa1161fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2e/7797041/9daa22e49563/gkaa1161fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2e/7797041/372e33d10ada/gkaa1161fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2e/7797041/31d6d77a0f96/gkaa1161fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2e/7797041/bfec5ec767b7/gkaa1161fig8.jpg

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